Literature Review Outline: To ensure that students are on track, submit a 2‐3 page double‐spaced APA formatted justification for your topic of interest. This justification should be written in formal

Literature Review Outline: To ensure that students are on track, submit a 2‐3 page double‐spaced APA formatted justification for your topic of interest. This justification should be written in formal
European Archives of Psychiatry and Clinical Neuroscience https://doi.org/10.1007/s00406-022-01485-7 ORIGINAL PAPER Distinct functional brain abnormalities in insomnia disorder and obstructive sleep apnea Weiwei Duan 1 · Xia Liu 2 · Liangliang Ping 3 · Shushu Jin 4 · Hao Yu 1 · Man Dong 1 · Fangfang Xu 1 · Na Li 1 · Ying Li 1 · Yinghong Xu 1 · Zhe Ji 1 · Yuqi Cheng 5 · Xiufeng Xu 5 · Cong Zhou 1,4 Received: 24 April 2022 / Accepted: 29 August 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany 2022 Abstract Insomnia disorder (ID) and obstructive sleep apnea (OSA) are the two most prevalent sleep disorders worldwide, but the pathological mechanism has not been fully understood. Functional neuroimaging ndings indicated regional abnormal neural activities existed in both diseases, but the results were inconsistent. This meta-analysis aimed to explore concordant regional functional brain changes in ID and OSA, respectively. We conducted a coordinate-based meta-analysis (CBMA) of resting-state functional magnetic resonance imaging (rs-fMRI) studies using the anisotropic eect-size seed‐ based d mapping (AES-SDM) approach. Studies that applied regional homogeneity (ReHo), amplitude of low-frequency uctuations (ALFF) or fractional ALFF (fALFF) to analyze regional spontaneous brain activities in ID or OSA were included. Meta-regressions were then applied to investigate potential associations between demographic variables and regional neural activity alterations. Signicantly increased brain activities in the left superior temporal gyrus (STG.L) and right superior longitudinal fasciculus (SLF.R), as well as decreased brain activities in several right cerebral hemisphere areas were identied in ID patients. As for OSA patients, more distinct and complicated functional activation alterations were identied. Several neuroimaging alterations were functionally correlated with mean age, duration or illness severity in two patients groups revealed by meta- regressions. These functionally altered areas could be served as potential targets for non-invasive brain stimulation methods. This present meta-analysis distinguished distinct brain function changes in ID and OSA, improving our knowledge of the neuropathological mechanism of these two most common sleep disturbances, and also provided potential orientations for future clinical applications. Registration number: CRD42022301938. Keywords Insomnia disorder · Obstructive sleep apnea · Resting-state fMRI · Neuroimaging · Meta-analysis Introduction Insomnia disorder (ID) and obstructive sleep apnea (OSA) are the two most prevalent sleep disorders worldwide [1 ]. The former implicates a perceived diculty in falling or staying asleep and obtaining refreshing sleep, as well as early morning awakening [2 , 3], while the latter is a com- mon chronic sleep-related breathing disorder, characterized by repeated complete or partial collapse and obstructions of the upper airway, leading to recurrent intermittent hypoxia, hypercapnia, and sleep frequent awakening [4 , 5 ]. The preva- lence of ID in the worldwide population ranges from 4 to 22% [6 , 7]. The disease strongly aects patients’ regular statues, which may reduce the eciency of daily work and increase the risk of road and motor vehicle accidents [8 ]. On the other hand, the prevalence of OSA is noticeable in * Cong Zhou [email protected] 1 School of Mental Health, Jining Medical University, Jining, China 2 Department of Sleep Medicine, Shandong Daizhuang Hospital, Jining, China 3 Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China 4 Department of Psychology, Aliated Hospital of Jining Medical University, Jining, China 5 Department of Psychiatry, The First Aliated Hospital of Kunming Medical University, Kunming, China Vol.:(0123456789) 1 3 European Archives of Psychiatry and Clinical Neuroscience general population and around 50% in patients with cardio- vascular or metabolic disorders [4 , 9]. Though the clinical manifestations of these two diseases are distinct, they both interfere with the quality of life of the patients. With condi- tions continuing, patients with ID and OSA present high comorbidity with aective disorders and emotional dysregu- lation [10– 13]. Specially, dierent types of sleep disorders and non-sleep circadian disorders were proven to be risk fac- tors of subsequent depression [14]. In addition, sleep disor – ders are closely related to airway diseases. Airway diseases such as obstructive sleep apnea syndrome (OSA) can disturb sleep structure, reduce sleep quality, and induce refractory insomnia. OSA also contributes to cognitive decline, and there is increasingly evidence showing OSA to be one of the rare modiable risk factors for neurodegenerative dementia [ 4 ]. Even brief disturbances in sleep can have a lasting eect on the internal activity and reactivity during waking [1 ]. Long-time sleep disturbances will further aect brain func- tions of patients with either ID or OSA. Advances in neuro- imaging techniques allow researchers to visualize and inves- tigate brain activities with non-invasive means, among them is the resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI approach measures blood oxygen- level-dependent (BOLD) signals to reect the spontane- ous uctuations during neural activity in resting state [15], and has been widely applied in neuropsychiatric disorders to enhance a better understanding of the pathophysiology and potential mechanisms of the diseases [16]. Although the design of rs-fMRI research is similar in essence, the analysis methods for processing rs-fMRI data are diverse, mainly consist of seed-based functional connectivity (FC), independent component analysis (ICA), graph theory and regional spontaneous brain activity analysis [17]. In terms of the last one, regional homogeneity (ReHo), amplitude of low-frequency uctuations (ALFF), fractional ALFF (fALFF) are three widely used methods for characterizing local spontaneous activity of rs-fMRI data. ReHo measures the local synchronization of the time series of neighboring voxels, whereas ALFF/fALFF measures the amplitude of time series uctuations at each voxel [18]. Commonalities and dierences exist in these metrics, which provide sup – plementary information to improve  the understanding of regional spontaneous brain activities [19]. Previous electro- encephalogram (EEG) studies have revealed functional brain dynamics vary in ID and OSA [20– 22], fMRI could provide more insights into the neurological function characteristic in these two sleep disturbances. For now, a number of rs-fMRI studies explored brain function characteristics in both ID and OSA, but the results are complex and inconsistent. These two sleep disorders pos- sess their own clinical characteristics, and also have distinct neurophysiological and social bases. However, nowadays, research has found similar or disparate neuroimaging changes involved with sleep and arousal in ID and OSA. Most of the reported brain areas involved in sleep-wakeful- ness or even cognitive processing [23– 25]. The variability of the ndings might attribute to relatively small sample sizes, heterogeneous patient groups that diered in demo- graphic characteristics, and use of diverse methodologi- cal techniques across studies. Meta-analysis is a powerful method to synthesize neuroimaging ndings from dierent studies in a comprehensive way, which helps to overcome the discrepancies of regional alterations among various neu- roimaging studies [26]. This method can also distinguish false results from replicable ndings, and summarize and integrate a large amount of data across studies [27]. Besides, progresses in neuroimaging meta-analytic methodology have made it possible to correlate imaging results with clinical characteristics [28]. The anisotropic eect-size seed‐ based d mapping (AES-SDM) is an advanced statistical technique for coordinate‐based meta‐analysis (CBMA) to attain a syn- optic view of distributed neuroimaging ndings and dierent neuroimaging methods (e.g., structural and functional) in an objective and quantitative fashion [29]. The strengths of AES‐ SDM has been summarize elsewhere [29– 33]. To date, a few research performed meta-analysis on fMRI studies of ID and OSA. One activation likelihood estimation (ALE) meta-analysis [34] found no signicant convergent evidence for functional disturbances in ID across previ- ous studies. This study took rs-fMRI, task-fMRI, as well as positron emission tomography (PET) studies together in the meta-analysis. The methodological heterogeneity might lead to the lack of consistent brain alterations in ID. By comparison, another AES-SDM meta-analysis [35] con- centrated on rs-fMRI (including FC, ALFF, ReHo and ICA) and contained articles written in English and other language (Chinese). This study found that patients with persistent ID exhibited over activations in right parahippocampal gyrus (PHG.R) and left median cingulate/paracingulate gyri, together with weakened activities in right cerebellum and left superior frontal gyrus/medial orbital. The lately ALE meta-analysis [8 ] explored both structural and functional brain changes in ID, but distinguished ALFF and ReHo stud- ies, and analyzed these two measures separately without any pooled meta-analysis. One ALE meta-analysis on OSA [4 ] investigated structural and functional neural adaptations. Convergent evidence for structural atrophy and functional disturbances in the right basolateral amygdala/hippocampus and the right central insula were identied in this study. This meta-analysis was a relatively comprehensive research, but was conducted in about 6 years ago, which is surely in need of updating. In the field of exploring the consistent alteration of regional spontaneous brain activities caused by diseases, CBMA containing ALFF, fALFF and ReHo has been 1 3 European Archives of Psychiatry and Clinical Neuroscience applied in major depression disorder (MDD) [36], Parkin- son’s disease [37], type 2 diabetes mellitus (T2DM) [38] and anxiety disorders [17]. In our present study, we aimed to perform a CBMA of rs-fMRI studies which utilized ALFF, fALFF or ReHo in ID and OSA, so as to detect the common and distinct neurophysiological mechanisms of these two diseases for a comparative view. Moreover, we intended to explore the potential eects of demographics and clinical characteristics including mean age, duration of disease, and severity of illness on brain functions using meta-regression approach, which we hope would bring some inspirations for future clinical diagnoses and treatments of ID and OSA. Methods Literature search strategy We performed this meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [39– 41]. The protocol of this CBMA was registered at PROSPERO (http:// www. crd. york. ac. uk/ PROSP ERO) (registration number: CRD42022301938). Relevant literatures were acquired using systematic and comprehensive searches from the PubMed, ScienceDirect and Web of Science databases published (or “in press”) up to December 31, 2021. The search keywords were (“insomnia” OR “insomnia disorder”) and ((“functional magnetic reso- nance imaging” or “fMRI”) or (“amplitude of low frequency uctuation” or “fractional amplitude of low frequency uc- tuation” or “ALFF” or “fALFF”) or (“regional homogeneity” or “ReHo” or “local connectivity” or “coherence”)) for ID, and (“obstructive sleep apnea” or “OSA” or “obstructive sleep apnea syndrome” or “OSAS” or “obstructive sleep apnea–hypopnea syndrome” or “OSAHS”) and ((“functional magnetic resonance imaging” or “fMRI”) or (“amplitude of low frequency uctuation” or “fractional amplitude of low frequency uctuation” or “ALFF” or “fALFF”) or (“regional homogeneity” or “ReHo” or “local connectivity” or “coher – ence”)) for OSA, respectively. Additionally, the reference lists of identied studies and relevant reviews were manually checked to avoid omitting. Study selection The common inclusion criteria were: (1) studies compared ReHo, ALFF or fALFF value dierences between patients and HC in whole-brain analyses; (2) reported results in Talairach or Montreal Neurological Institute (MNI) coor – dinates; (3) used a threshold for signicance; (4) articles written in the English language and published in peer- reviewed journals. Exclusion criteria were: (1) meta-analy – sis, reviews, case reports or tractography-based only study; (2) studies with no direct between-group comparison; (3) studies from which peak coordinates or parametric maps were unavailable. Quality assessment and data extraction Two authors (D.W. and L.X.) independently searched the literatures, assessed the quality of the retrieved articles, extracted, and cross-checked the data from eligible arti- cles. The quality of the nal studies was also independently checked by both authors following guidelines for neuroimag- ing meta-analyses promoted by Müller and colleagues [27]. For both diseases, the following data were recorded: rst author, cohort size, demographics (age and gender), illness duration, imaging parameters, data processing method, as well as statistical threshold in each study. For ID studies, Pittsburgh Sleep Quality Index (PSQI) and Insomnia Sever – ity Index (ISI) scores were extracted, and for OSA studies, we specially recorded Apnea–Hypopnea Index (AHI) scores, Epworth Sleepiness Scale (ESS) scores and BMI. Meta‑analyses Regional brain activity dierences between patients and HC were performed using the SDM software v5.15 (http:// www. sdmpr oject. com) [30, 42] in a voxel-based meta-anal- ysis approach. We conducted the analysis according to the SDM tutorial and previous meta-analytic studies. The AES- SDM technique is a powerful statistical technique that uses peak coordinates for meta-analysis to assess dierences in brain activity [29]. The AES-SDM procedures have been described in detail elsewhere [17, 26, 35, 43– 45], and were briey summarized as below: (1) the software recreated the eect-size maps of dierences in regional activities between patients and HC for each study based on the peak coordi- nates of the eects and statistics the level of t -statistics (Z- or P -values for signicant clusters which were then converted to t-statistics using the SDM online converter); (2) the peak coordinates for each study were recreated using a standard MNI map of the eect size of the group dierences in neu- roimaging by means of an anisotropic Gaussian kernel [31]. Both positive and negative coordinates were reconstructed in the same map [30]; (3) the standard meta-analysis was conducted to create a mean map via voxel-wise calculation of the random-eects mean of the study maps. According to the inventors of the AES-SDM algorithm, the default AES-SDM threshold uncorrected P = 0.005 is approximately equivalent to a corrected P = 0.025 [29]. Here, a more stringent thresholds were applied for both ID and OSA analyses with an uncorrected P value = 0.0025, representing the multiple comparison correction for two dis- eases (P = 0.005/2 = 0.0025), which is consistent with pre- vious studies [43, 45]. Other parameters included the peak 1 3 European Archives of Psychiatry and Clinical Neuroscience height threshold Z = 1.00 and cluster size threshold = 100 voxels. Sensitivity analyses To assess the replicability of the results, we performed a sys- tematic whole-brain voxel-based jackknife sensitivity analy – sis. This procedure involved repeating the main statistical analysis for each result N times (N represents the number of datasets in each meta-analysis), discarding a dierent study each time. If a brain region remains signicant after run – ning jackknife sensitivity in all or most of the combinations of studies, the nding is considered highly replicable [30]. Subgroup meta‐analyses In the present investigation, we initially conducted pooled meta-analysis of all the included studies for each disease. Subsequently, we performed subgroup meta‐ analyses, which only included methodologically homogenous studies so as to minimize the inuence of any potential methodological dif- ferences among individual studies. Specically, we planned to conduct subgroup meta‐ analyses of ReHo studies as well as ALFF/fALFF studies in each disease separately. Meta‑regression analyses Considering the potential associations between demo – graphic variables and neuroimaging changes, meta-regres – sion analyses were performed in each patient group. A more conservative threshold (P < 0.0005) was adopted in consistent with previous meta-analyses and the recom- mendations of the AES-SDM authors [30], and only brain regions identied in the main eect were considered. Results Included studies and sample characteristics Figure  1 presents the ow diagram of the identication and the attributes of the studies in ID and OSA. The demo – graphics and neuroimaging approaches of the samples for each disease are summarized in Table 1 and Table 2 , respectively. For the meta-analysis of ID, the search strategy identied 155 studies, 9 of which met the inclu- sion criteria [23, 46–53]. The study by Wang et al. [51] contained two randomly selected datasets of insomnia patients. The nal sample of ID comprised 316 insomnia patients and 291 HC, along with 49 coordinates extracted from 10 datasets. For the meta-analysis of OSA, a total of 241 studies were identied according to the search strat- egy, and 10 of them met our inclusion criteria [24, 25, 54– 61]. Among them, three studies [24, 57, 58] contained both ALFF and ReHo analyses, and one study [60] con- tained ALFF, fAFLL and ReHo analyses. We treated these analyses as independent datasets. The nal sample of OSA comprised 376 OSA patients and 397 HC, along with 81 coordinates extracted from 15 datasets. Fig. 1 Flow diagram for the identication and exclusion of studies 1 3 European Archives of Psychiatry and Clinical Neuroscience Results of the pooled meta‑analyses fMRI in ID The pooled meta-analysis revealed that compared with HC, ID patients exhibited signicant increased brain activities in two clusters including the left superior temporal gyrus (STG.L, BA 48), right superior longitudinal fasciculus (SLF.R) II, as well as three clusters with decreased brain activities including the right hemispheric lobule IX, right median cingulate/paracingulate gyri and right inferior fron- tal gyrus (IFG.R, opercular part, BA 48). The results are illustrated in Fig.  2A and Table  3. fMRI in OSA Increased functional activations were found in four clusters in OSA patients relative to controls, locating in the right median cingulate/paracingulate gyri, right lenticular nucleus (putamen, BA 48), left parahippocampal gyrus (PHG.L, BA 36) and corpus callosum (CC), together with decreased acti- vations in three clusters including the left calcarine ssure/ surrounding cortex (BA 17), right superior frontal gyrus (SFG.R, dorsolateral, BA 10) and left middle frontal gyrus (MFG.L, BA 46). See Fig.  2B and Table  3. Sensitivity analysis The whole-brain jackknife sensitivity analyses revealed that the results were highly replicable, as decreased brain activi- ties in right cerebellum in ID and increased brain activities in the right median cingulate/paracingulate gyri in OSA remained signicant throughout all but 1 combination of the datasets. The remaining resultant clusters remained sig – nicant in all but 2 or 3 combinations of datasets, except the result of decreased MFG.L functions in OSA remaining signicant in all but 4 combinations of datasets. The details are shown in Table  3. Subgroup analysis Detailed results of heterogeneous methodologies (ReHo or ALFF/fALFF studies) on each disease are presented in Table S1 in the Appendix A. Supplementary data. The results of dierent subgroups were highly consistent with the pooled meta-analysis ndings, but the signicant cluster numbers were a little bit less, which might be related with the statistical eects. Besides, distinct results were found between ReHo and ALFF/fALFF studies, which might be due to the methodological heterogeneity. Table 1 Demographic and clinical characteristics and the neuroimaging approaches of the participants in the 9 studies (10 datasets) included in the meta-analysis of ID ALFF low-frequency uctuation, BA Brodmann area, fALFF fractional amplitude of low-frequency uctuations, FDR false discovery rate, FWE family-wise error, HC healthy controls, ID insomnia disorder, ISI Insomnia Severity Index, N/A not available, PCC posterior cingulate cortex, PSQI Pittsburgh Sleep Quality Index, ReHo regional homogeneity Study Subjects, n (female, n) Age, years Duration, years PSQI scoreISI scoreScannerType of analysis Statistical threshold Number of coordinates ID HCIDHC (Dai et al., 2014) 24 (17)24 (12) 54.852.56.0 15.619.33.0 TReHo P < 0.01, AlphaSim corrected 3 (Wang et al., 2016) 59 (38)47 (33) 39.340.0N/A 12.4N/A1.5 TReHo P < 0.05, AlphaSim corrected 7 (Dai et al., 2016) 42 (27)42 (24) 49.249.15.44 15.218.43.0 TALFF P < 0.01, AlphaSim corrected 4 (Li et al., 2016) 55 (31)44 (33) 39.239.93.8 12.519.71.5 TALFF P < 0.01, AlphaSim corrected 6 (Ran et al., 2017) 21 (16)20 (14) 40.638.7N/A 13.3N/AN/AALFF N/A 5 (Wang et al., 2020)a 15 (10)15 (8) 48.445.5N/A N/AN/A3.0 TfALFF P < 0.001, FWE corrected 7 (Wang et al., 2020)b 15 (9)15 (8) 49.745.5N/A N/AN/A3.0 TfALFF P < 0.001, FWE corrected 9 (Zhao et al., 2020) 22(13)20(12) 42.636.2N/A 12.4N/A3.0 TALFF P < 0.01, AlphaSim corrected 1 (Zhang et al., 2021) 32(20)34(21) 37.535.8N/A 12.0N/A3.0 TReHo P < 0.05, AlphaSim corrected 4 (Feng et al., 2021) 31 (8)30 (10) 44.842.3N/A 13.919.43.0 TReHo P < 0.05, FDR corrected 3 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 2 Demographic and clinical characteristics and the neuroimaging approaches of the participants in the 10 studies (15 datasets) included in the meta-analysis of OSA ALFF low-frequency uctuation, AHI apnea–hypopnea index, BMI body mass index, ESS Epworth Sleepiness Scale, fALFF fractional amplitude of low-frequency uctuations, FDR false dis- covery rate, GRF Gaussian random eld, HC healthy controls, N/A not available, OSA obstructive sleep apnea, ReHo regional homogeneity Study Subjects, n (female, n) Age, years Duration of disease, years AHI, per hour ESSBMI kg/m 2 Scanner Type of analysis Statistical threshold Number of coordinates OSA HCOSAHC (Santarnecchi et al., 2013) 19 (3)19 (5) 43.2416.5 36.3 14.430.3 1.5 T ReHo P < 0.05, FDR corrected 16 (Peng et al., 2014) 25 (0)25(0) 39.439.5 – 60 15.227.8 3 T ReHo P < 0.001, FDR orrected 8 (Li et al., 2015) 25 (0)25 (0) 39.439.5 – 60 15.227.8 3 T ALFF P < 0.05, FDR orrected 2 (Kang et al., 2020) 14 (0)16 (0) 48.744.8 – 28.9 –27.4 3 T ALFF P < 0.05, AlphaSim corrected 5 (Kang et al., 2020) 14 (0)16 (0) 48.744.8 – 28.9 –27.4 3 T ReHo P < 0.05, AlphaSim corrected 7 (Qin et al., 2020) 36 (0)38 (0) 48.546.1 – 58.8 –29.0 3 T ALFF P < 0.001, AlphaSim corrected 10 (Qin et al., 2020) 36 (0)38 (0) 48.546.1 – 58.8 –29.0 3 T ReHo P < 0.001, AlphaSim corrected 8 (Zhou et al., 2020) 33 (3)22 (4) 43.639.7 – 57.9 14.429.1 3 T ReHo P < 0.05, GRF corrected 4 (Ji et al., 2021) 20 (8)29 (17) 7.27.7 – 16.5 –19.2 3 T ALFF P < 0.05, AlphaSim corrected 2 (Ji et al., 2021) 20 (8)29 (17) 7.27.7 – 16.5 –19.2 3 T ReHo P < 0.05, AlphaSim corrected 3 (Bai et al., 2021) 31 (12)33 (16) 5.76.0 1.8 12.9 –18.4 3 T ALFF P < 0.001, GRF corrected 1 (Bai et al., 2021) 31 (12)33 (16) 5.76.0 1.8 12.9 –18.4 3 T fALFF P < 0.001, GRF corrected 2 (Bai et al., 2021) 31 (12)33 (16) 5.76.0 1.8 12.9 –18.4 3 T ReHo, P < 0.001, GRF corrected 2 (Santarnecchi et al., 2021) 20(3)20(4) 42.9416.9 38.3 13.829.5 N/A fALFF P < 0.05, Monte Carlo corrected 7 (Li et al., 2021) 21(1)21(1) 40.140.1 – 48.4 10.827.3 3.T ReHo P < 0.01, GRF corrected 4 1 3 European Archives of Psychiatry and Clinical Neuroscience Meta‑regression analysis In ID group, the meta-regression analysis found a positive correlation between brain function alterations in SLF.R II and the mean age as well as the PSQI of the patients, along with a negative correlation between brain function altera- tions in the right cerebellum (hemispheric lobule IX) and the illness duration. In OSA patients, the mean age of the patients was sig- nificantly and positively correlated with brain function alterations in the right median cingulate/paracingulate gyri, PHG.L, and CC. The AHI was positively correlated with brain function alterations in PHG.L and CC, and negatively correlated with brain function alterations in the SFG.R. The BMI impacted brain activities the most, with a positive correlation with brain function alterations in right median cingulate/paracingulate gyri, PHG.L and CC, as well as a negative correlation with SFG.R. The details are shown in Table  4. Discussion To our knowledge, this study is the rst CBMA of rs-fMRI studies investigating regional spontaneous neural activity abnormalities in ID and OSA simultaneously. Unlike some previous meta-analyses, this whole-brain meta-analysis excluded the inuence of treatment and external tasks to purely reect intrinsic brain activity, and might provide more reliable information on the neural patterns and their potential roles in the pathophysiology of ID and OSA. Our pooled meta-analysis results showed increased brain activi- ties in the STG.L, SLF.R, and decreased brain activities in the right cerebellum, right median cingulate/paracingulate gyri and IFG.R when comparing ID patients with HC. When conducting comparisons between OSA patients and HC, increased functional activations in the right median cingu- late/paracingulate gyri, right lenticular nucleus, PHG.L and CC, and decreased activations in the left calcarine ssure/ surrounding cortex, SFG.R and MFG.L were identied. Our current ndings indicated complexed resting-state dysfunc- tions in these two sleep disorders, and were mostly consist- ent with previous meta-analyses [ 4, 8 , 35 ], but distinct neural activity alterations existed between ID and OSA. ID patients demonstrated increased brain activities in the STG.L and SLF.R. Hyperactive fMRI signals might be coin- cided with the hyperarousal model of insomnia [62], reect- ing a sleep–wake dysregulation. The STG is a vital compo- nent of the default mode network (DMN), which is believed to be related with interplaying between attention orientation and default mode processing, and are associated with dis- rupted switching between resting and task-context process- ing [63]. Evidence has shown that sleep deprivation, which might occur in insomnia, leads to aberrant stability and func- tion of the DMN [1 ]. The ndings of another study sug- gested that sleep disturbances were associated with greater Fig. 2 Meta-analysis of regional abnormal resting-state brain activi- ties in (A) ID and (B) OSA. Signicant clusters are overlaid on MRI- cron template for Windows for display purposes only. CC corpus callosum, ID insomnia disorder, IFG.R right inferior frontal gyrus, MFG.L left middle frontal gyrus, OSA obstructive sleep apnea, PHG.L left parahippocampal gyrus, SFG.R right superior frontal gyrus, SLF.R II right superior longitudinal fasciculus II, STG.L left superior temporal gyrus 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 Regional functional brain abnormalities in ID patients and OSA patients compared to HC in the pooled meta-analysis Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ ID vs HC ID > HC Left superior temporal gyrus, BA 48 − 38 − 6− 12 1.621 0.000159979 211Left insula, BA 48 (98) Left superior temporal gyrus, BA 48 (80) Left lenticular nucleus, putamen, BA 48 (13) Left inferior network, inferior fronto-occipital fasciculus (10) Left inferior network, uncinate fasciculus (5) BA 20 (4) Left striatum (1) 8/10 Right superior longitudinal fasciculus II 32 − 1654 1.628 0.000144482 172Right precentral gyrus, BA 6 (50) Right frontal superior longitudi- nal (41) Right superior longitudinal fas- ciculus II (30) Right superior frontal gyrus, dorsolateral, BA 6 (25) Right precentral gyrus, BA 4 (16) Corpus callosum (10) 8/10 ID < HC Right cerebellum, hemispheric lobule IX 10 − 58− 42 − 2.036 0.000206411 672Right cerebellum, hemispheric lobule VIII (335) hemispheric lobule IX (145) Right cerebellum, undened (142) Cerebellum, vermic lobule VIII (26) Right cerebellum, hemispheric lobule VIIB (13) Cerebellum, vermic lobule IX (11) 9/10 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ Right median cingulate/paracin- gulate gyri 4 − 3644 − 1.756 0.001326323 191Right median cingulate/paracin- gulate gyri, BA 23 (94) Left median cingulate/paracingu- late gyri, BA 23 (36) Right median cingulate/paracin- gulate gyri (34) Left median cingulate/paracingu- late gyri (16) Right median network, cingulum (11) 7/10 Right inferior frontal gyrus, opercular part, BA 48 54 108 − 1.774 0.001171529 163Right inferior frontal gyrus, oper – cular part, BA 48 (49) Right rolandic operculum, BA 48 (48) Right inferior frontal gyrus, oper – cular part, BA 44 (28) Right insula, BA 48 (15) Right inferior frontal gyrus, opercular part, BA 6 (8) Right frontal aslant tract (7) Right rolandic operculum, BA 6 (3) Right inferior frontal gyrus, opercular part (3) Right insula (1) Right fronto-insular tract 4 (1) 7/10 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ OSA vs HC OSA > HC Right median cingulate/paracin- gulate gyri 4 − 232 2.545 ~ 0 1534Left median cingulate/paracingu- late gyri, BA 24 (245) Right median cingulate/paracin- gulate gyri, BA 24 (227) Left median cingulate/paracingu- late gyri (210) Right median cingulate/paracin- gulate gyri, BA 23 (140) Left median cingulate/paracingu- late gyri, BA 23 (133) Left median network, cingulum (103) Right median cingulate/paracin- gulate gyri (90) Right median network, cingulum (83) Corpus callosum (77) Right median cingulate/paracin- gulate gyri, BA 32 (63) Right anterior cingulate/paracin- gulate gyri, BA 24 (59) Right anterior cingulate/paracin- gulate gyri (24) Left supplementary motor area (21) Left median cingulate/paracingu- late gyri, BA 32 (14) Right supplementary motor area (9) Left superior frontal gyrus, medial, BA 32 (9) Right supplementary motor area, BA 24 (4) Left superior frontal gyrus, medial (5) Left supplementary motor area, BA 23 (3) (undened) (15) 14/15 Right lenticular nucleus, puta- men, BA 48 32 14− 2 1.643 0.000340641 434Right lenticular nucleus, puta- men, BA 48 (222) Right insula, BA 47 (91) Right insula, BA 48 (75) Right striatum (26) Right lenticular nucleus, putamen (14) Right insula (3) Right lenticular nucleus, puta- men, BA 47 (2) Right inferior network, inferior fronto-occipital fasciculus (1) 13/15 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ Left parahippocampal gyrus, BA 36 − 18 − 14− 30 1.965 0.000020623 379Left parahippocampal gyrus, BA 35 (96) Left median network, cingulum (71) Left parahippocampal gyrus, BA 36 (57) Left parahippocampal gyrus, BA 30 (51) Left pons (30) Left hippocampus, BA 35 (9) Left hippocampus (7) Left fusiform gyrus, BA 36 (6) Left fusiform gyrus, BA 30 (5) Left cerebellum, hemispheric lobule III (2) Left cerebellum, hemispheric lobule IV/V (1) Left hippocampus, BA 30 (1) Left fusiform gyrus (1) (undened) (42) 13/15 Corpus callosum − 102248 2.003 0.000020623 239Corpus callosum (102) Left superior frontal gyrus, dor – solateral, BA 8 (58) Left superior frontal gyrus, medial, BA 8 (40) Left superior frontal gyrus, dor – solateral, BA 9 (16) Left supplementary motor area, BA 8 (13) Left superior frontal gyrus, medial, BA 9 (3) Left superior frontal gyrus, medial, BA 32 (2) Left frontal aslant tract (2) Left supplementary motor area, BA 32 (2) Left supplementary motor area, BA 6 (1) 12/15 1 3 European Archives of Psychiatry and Clinical Neuroscience waking resting-state connectivity between the retrosplenial cortex/hippocampus and various nodes of the DMN [64 ]. The SLF is a large bundle of association tracts in the white matter of each cerebral hemisphere connecting the parietal, occipital and temporal lobes with ipsilateral frontal cortices [ 65], and SLF.R has been proved to play roles in the forma- tion of distress both within and between components of the DMN, salience network, and executive-control network [66]. Disruptions of SLF has also been reported in diusion tensor imaging (DTI) studies of primary insomnia [65, 67]. Taken Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ OSA < HC Left calcarine ssure/surrounding cortex, BA 17 0 − 86− 10 − 1.933 0.000082552 290Left calcarine ssure/surrounding cortex, BA 17 (77) Right lingual gyrus, BA 17 (54) Left cerebellum, hemispheric lobule VI, BA 17 (38) Right lingual gyrus, BA 18 (38) Left lingual gyrus, BA 17 (28) Left calcarine ssure/surrounding cortex, BA 18 (7) Left calcarine ssure/surrounding cortex (5) Right inferior network, inferior longitudinal fasciculus (4) Cerebellum, vermic lobule VI, BA 17 (4) Right cerebellum, hemispheric lobule VI, BA 18 (4) Left lingual gyrus, BA 18 (1) Left cerebellum, hemispheric lobule VI (1) (undened) 29 12/15 Right superior frontal gyrus, dorsolateral, BA 10 16 6410 − 1.524 0.000722528 134Right superior frontal gyrus, dorsolateral, BA 10 (82) Right superior frontal gyrus, dorsolateral (20) Right superior frontal gyrus, medial, BA 10 (19) Corpus callosum (7) Right superior frontal gyrus, dorsolateral, BA 11 (6) 13/15 Left middle frontal gyrus, BA 46 − 44460 − 1.406 0.001594663 100Left middle frontal gyrus, BA 46 (42) Left inferior frontal gyrus, trian- gular part, BA 45 (30) Left middle frontal gyrus, orbital part, BA 47 (18) Left inferior frontal gyrus, trian- gular part, BA 46 (4) Left inferior frontal gyrus, orbital part, BA 46 (3) Left middle frontal gyrus, BA 45 (3) 11/15 * All voxels with P < 0.0025 uncorrected BA Brodmann area, HC healthy controls, ID insomnia disorder, MNI Montreal Neurological Institute, OSA obstructive sleep apnea, SDM seed‐ based d mapping 1 3 European Archives of Psychiatry and Clinical Neuroscience together, overactivated functions in the STG.L and SLF.R might lead to sleep-wakefulness disorders in ID patients. Besides, disrupted brain functions overlapping with above ndings have been constantly reported in MDD patients [28, 36]. Macroscopically speaking, ID is clinically described as a heterogeneous disorder, which includes dierent subtypes of pathophysiology in terms of cognitions, mood, traits, his- tory of life events and family history and not necessarily due to sleep complaints only [14, 34]. From a microscopic point of view, in consideration of the neuroimaging link between sleep disturbances and mental diseases, our fMRI results provided more objective insights that insomnia and circadian rhythm might participate in the pathophysiology of depres- sion and other neuropsychiatric disorders. Increasing evidence has demonstrated that in addition to well-known role in motor control, the cerebellum also plays roles in cognitive and emotional regulatory processes [ 68, 69], and also associates with sleep regulation [35]. The cerebellum is structurally and functionally connected to the limbic-cortical network [68, 70], which forms a feedback information ow that allows the cerebellum to involve in advanced neural activities. The IFG.R is thought to play roles in attentional control [71] and working memory [72]. Weakened regional brain functions in above regions might be related with cognitive decline and low spirit symptoms in ID patients. It is particularly noteworthy that the function of the right median cingulate/paracingulate gyri was altered in both ID and OSA, but presented converse patterns in these two dis- eases. The median cingulate/paracingulate gyri belong to the limbic system, which is responsible for regulating emotional disorders [35], and also involve in the subjective percep- tion of pain and one’s cognition and memory [73]. Altered activities of above brain areas reect complex changes of brain function in these two sleep disorders. Moreover, OSA patients showed distinct neural activity abnormalities in other brain regions compared with ID patients, including hyperactivities in right lenticular nucleus, PHG.L, CC and hypoactivities in left calcarine fissure/surrounding cor – tex, SFG.R and MFG.L. Therefore, though both served as most commonly seen sleep disturbances in clinic, ID and OSA possessed dierent neural mechanisms, or exhibited as various functional brain abnormalities. And most of the involved altered brain areas lied in the DMN, the central executive network (CEN) and the salience network (SN), all of which are essential in performing neural functions dur – ing rest, cognition, autonomic and emotional processes [1 ]. This provides important inspirations for our clinical work in the future, that is, these functionally altered areas could be served as potential targets for non-invasive brain closed loop stimulation, such as repetitive transcranial magnetic stimu- lation (rTMS), to rebalance the sleep homeostasis [35]. For example, high-frequency rTMS may increase reduced activ – ity in the right median cingulate/paracingulate gyri in ID patients, while low-frequency rTMS may be used to decrease increased activity in this area in OSA patients, which helps Table 4 Associations between demographic variables and brain function alterations in ID and OSA patients revealed by meta‐regression analy – ses AHI apnea–hypopnea index, BA Brodmann area, BMI body mass index, ID insomnia disorder, MNI Montreal Neurological Institute, OSA obstructive sleep apnea, SDM seed‐based d mapping, PSQI Pittsburgh Sleep Quality Index MNI coordinates Factor Anatomic label XYZSDM value P Number of voxels ID patients Age Right superior longitudinal fasciculus II 30− 1452 2.280 0.000139356 106 Duration Right cerebellum, hemispheric lobule IX 14− 58− 44 − 3.844 ~ 0 1446 PSQI Right superior longitudinal fasciculus II 28− 1460 2.144 0.000010312 68 OSA patients Age Right median cingulate/paracingulate gyri 2630 3.801 ~ 0 1038 Left parahippocampal gyrus, BA 36 − 20− 16− 30 2.861 0.000020623 273 Corpus callosum − 102448 3.039 0.000015497 199 AHI Left parahippocampal gyrus, BA 36 − 22− 16− 28 3.079 ~ 0 321 Corpus callosum 10820 2.848 0.000025809 18 Right superior frontal gyrus, dorsolateral, BA 10 16668 − 2.300 0.000206411 70 BMI Right median cingulate/paracingulate gyri 2430 3.600 ~ 0 1004 Left parahippocampal gyrus, BA 36 − 22− 16− 30 2.634 0.000020623 193 Corpus callosum − 102648 2.543 0.000020623 72 Right superior frontal gyrus, dorsolateral, BA 10 166410 − 1.941 0.000196099 20 1 3 European Archives of Psychiatry and Clinical Neuroscience to reverse abnormal brain function [74]. With timely inter- vention, the degrees of cognitive decits such as diculties with attention, memory, executive-functioning, and quality of life might be reversed. The sensitivity analysis and subgroup analysis revealed high reproducible, which conrmed the reliability of the study. However, the significant cluster numbers were a little bit less, this might be due to the statistical eects of less samples. Inconsistent ndings existed between ALFF/ fALFF and ReHo studies. This might be explained by the dierences in these two methods that ALFF/fALFF mainly measures the amplitude of uctuation of every single voxel, while ReHo reects the local synchronization of nearest neighboring voxels [37]. The meta-regression analysis indicated that the brain function alterations in the SLF.R II were positively corre- lated with the mean age and the PSQI of ID patients, and regional spontaneous activities in the right cerebellum were negatively correlated with the illness duration. Thus, the functional activities of SLF.R might be used to reect the severity of the disease. With the increase of the age and the progresses of duration, regional function alterations might continue exacerbating [2 ]. In OSA patients, neuro- imaging changes related with demographic variables were rather consistent. The mean age of the patients has posi- tive correlations with regional functional abnormalities in the right median cingulate/paracingulate gyri, PHG.L and CC. Aging is still one of the most important factors lead- ing to the chaos of brain function. The AHI was positively correlated with brain activity alterations in PHG.L and CC, and negatively correlated with brain function alterations in the SFG.R. Neural activities in these three regions were most closely associated with the severity of symptoms, and might be treated as important targets for non-invasive brain stimulation. The BMI had the maximum impacts on regional spontaneous brain activities, with a positive correlation with brain function alterations in right median cingulate/paracin- gulate gyri, PHG.L and CC, as well as a negative correlation with SFG.R. Obesity and higher BMI are considered to be vital risk factors of both adolescent and adult OSA patients [ 75– 77]. Our ndings provided a neurobiological theoreti- cal basis for the therapeutic strategies of weight control in OSA patients. Above meta-regression analysis brought inspirations to our future clinical work, that it is necessary to diagnose and treat both ID and OSA as early as possible, and to control the weight of OSA patients to alleviate their symptoms. Several limitations should be addressed in this current study. First, the data acquisition parameters and clinical vari- ables in the included studies were heterogeneous inescap- ably. It is hardly possible to eliminate these heterogeneities by statistical methods. Second, the present meta-analysis focused only on resting-state regional spontaneous brain activity changes in ID and OSA. Future studies need to include other approaches (i.e., FC, ICA, graph theory) as well as task-fMRI studies to provide a more comprehen- sive perspective of functional patterns of these two disor – ders. Third, it is meaningful to investigate the dynamicity and reversibility of neural activities, but the current meta- analysis and the literatures included in our research are all cross-sectional design. Longitudinal studies with respect to dynamicity of brain functions of ID and OSA are of great importance and should be explored in the future. Fourth, limited by the methodological shortcomings of nowadays analytical means, the study lacked a direct comparison between ID and OSA, which might be overcome by neuro- scientists and programmers in the future. Last but not least, the number of studies included in our meta-analysis was still insucient. The number of included subgroup studies was relatively small, so the interpretation of the subgroup nd- ings should be taken cautiously. Conclusions The AES-SDM approach served as a powerful meta-analysis method to synthesize neuroimaging ndings from dierent studies in a comprehensive way. In this present research, we performed a CBMA of rs-fMRI studies in ID and OSA to investigate the neurophysiological mechanisms of these two sleep disturbances simultaneously for a comparative perspective. We found distinct spontaneous brain activity alterations in these two diseases. These ndings improved our knowledge of the neuropathological mechanism of these two most prevalent sleep disorders, and also provided poten- tial guidance for future clinical application. The functionally altered brain regions might be served as biomarkers for more accurate and individualized diagnosis and treatment of ID or OSA in the future. Supplementary Information The online version contains supplemen- tary material available at https:// doi. org/ 10. 1007/ s00406- 022- 01485-7. Acknowledgements This study was supported by the Key Research and Development Plan of Jining City (2021YXNS024), the Medical and Health Science and Technology Development Plan of Shandong Province (202003061210), the Cultivation Plan of High-level Scientic Research Projects of Jining Medical University (JYGC2021KJ006), the National Natural Science Foundation of China (81901358), the Natural Science Foundation of Shandong Province (ZR2019BH001 and ZR2021YQ55), the Young Taishan Scholars of Shandong Province (tsqn201909146), and the Supporting Fund for Teachers’ Research of Jining Medical University (600903001). Funding Key Research and Development Plan of Jining City, 2021YXNS024, Cong Zhou, Medical and Health Science and Tech- nology Development Plan of Shandong Province, 202003061210, Cong Zhou, Cultivation Plan of High-level Scientic Research Projects of Jining Medical University, JYGC2021KJ006, Cong Zhou, National 1 3 European Archives of Psychiatry and Clinical Neuroscience Natural Science Foundation of China, 81901358, Hao Yu, Natural Science Foundation of Shandong Province, ZR2019BH001, Hao Yu,ZR2021YQ55, Hao Yu, Taishan Scholar Foundation of Shandong Province, tsqn201909146, Hao Yu, Supporting Fund for Teachers’ Research of Jining Medical University, 600903001, Cong Zhou Declarations Conflict of interest The authors declare that there is no conict of in- terest. Ethical approval This article is a meta-analysis with all analyses based on previously published studies; thus, no ethical approval and patient consent are required. References 1. 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Diagnosing Obstructive Sleep Apnea in a Residential Treatment Program for Veterans With Substance Use Disorder and PTSD Peter J. Colvonen 1, 2 , Guadalupe L. Rivera 1, Laura D. Straus 4, 5 , Jae E. Park 1, 2 , Moira Haller 1, 2 , Sonya B. Norman 1, 3 , and Sonia Ancoli-Israel 2 1VA San Diego Healthcare System, San Diego, California, United States 2University of California, San Diego 3National Center for PTSD, White River Junction, Vermont, United States4Department of Psychiatry, University of California, San Francisco 5San Francisco VA Healthcare System, San Francisco, California, United States Background:Obstructive sleep apnea (OSA) is often comorbid with both substance use disorders (SUD) and posttraumatic stress disorder (PTSD), yet frequently goes undiagnosed and untreated. We present data on the feasibility and acceptability of objective OSA diagnosis procedures,findings on OSA prevalence, and the relationship between OSA and baseline SUD/PTSD symptoms among veterans in residential treatment for comorbid PTSD/SUD.Methods:Participants were 47 veterans admitted to residential PTSD/SUD treatment. Participants completed questionnaires assessing PTSD and sleep symptoms, andfilled out a sleep diary for seven days. Apnea-hypopnea index (AHI) was recorded using the overnight Home Sleep Apnea test (HSAT; OSA was diagnosed with AHI$5).Results:Objective OSA diagnostic testing was successfully completed in 95.7% of participants. Of the 45 veterans who went through HSAT, 46.7% had no OSA, 35.6% received a new OSA diagnosis, and 8.9% were previ- ously diagnosed with OSA and were using positive airway pressure treatment (PAP); an additional 8.9% were previously diagnosed with OSA, reconfirmed with the HSAT, but were not using PAP. One hundred percent of respondents during follow-up deemed the testing protocol’s usefulness as“Good”or “Excellent.”Conclusion:OSA diagnostic testing on the residential unit was feasible and acceptable by participants and was effective in diagnosing OSA. OSA testing should be considered for everyone enter- ing a SUD and PTSD residential unit. Clinical Impact Statement Obstructive sleep apnea (OSA) is very often comorbid with both posttraumatic stress disorder (PTSD) and substance use disorders (SUD). Unfortunately, due to the limitations of self-report OSA screeners and atypical presentation of OSA in individuals with SUD/PTSD, OSA often goes undiag- nosed for individuals with SUD/PTSD. Our study found that OSA diagnostic testing was feasible and acceptable to participants in a residential treatment program for SUD/PTSD, and effective in diagnosing OSA. Diagnosing OSA on a residential unit for SUD/PTSD is a necessaryfirst step to treating OSA and may help improve long-term outcomes for individuals with SUD/PTSD. Keywords:veteran, PTSD, SUD, OSA, CPAP This article was published Online First September 2, 2021. Peter J. Colvonen https://orcid.org/0000-0003-0222-8781 Jae E. Park https://orcid.org/0000-0002-0197-0012 Sonya B. Norman https://orcid.org/0000-0002-4751-1882 This study was funded by Veterans Affairs RR&D CDA Grant #1lK2Rx002120-01 to Peter J. Colvonen and Veterans Affairs CSR&D merit Grant NURA-011-11F to Sonya B. Norman. This material is the result of work supported by a UCSD Academic Senate Pilot Grant. Peter J.Colvonen is partly funded by Veterans Affairs RR&D CDA Grant 1lK2Rx002120-01. The views expressed in this article are those of the authors only and do not reflect the official policy or position of the institutions with which the authors are affiliated, the Department of Veterans Affairs, nor the United States Government. None of the authors have any competingfinancial interests to disclose. Correspondence concerning this article should be addressed to Peter J. Colvonen, VA San Diego Healthcare System, 3350 La Jolla Village Drive (116B), San Diego, CA 92161, United States. Email:[email protected] 178 Psychological Trauma: Theory, Research, Practice, and Policy In the public domain2022, Vol. 14, No. 2, 178–185 ISSN: 1942-9681https://doi.org/10.1037/tra0001066 Substance use disorders (SUD) and posttraumatic stress disor- der (PTSD) are highly comorbid (Kessler et al., 2005;Najavits et al., 2010), and this comorbidity is associated with worse treatment outcomes for both disorders, greater risk of homelessness, increased disease burden, higher suicidal ideation and attempted suicide (Norman et al., 2018), and greater functional disability than having a single disorder (Calabrese et al., 2011;Driessen et al., 2008;Norman et al., 2016;Possemato et al., 2010). In addi- tion, both Veterans Affairs (VA) and community clinicians report significant challenges in treating comorbid SUD/PTSD individuals due to higher drop-out rates, more severe symptoms, and lower motivation (Najavits et al., 2010). Residential treatment is an appropriate level of care for individuals with severe PTSD and/or SUD (Haller et al., 2019) with upward of 40% of individuals seek- ing SUD treatment receiving residential care at some point (Stah- ler et al., 2016;Substance Abuse and Mental Health Services Administration, 2008). A residential setting offers an array of inte- grated treatment options to patients at a critical time in recovery and may be an optimal place to diagnose and treat comorbid disor- ders that can negatively affect both SUD and PTSD outcomes such as obstructive sleep apnea (OSA). Unfortunately, OSA diag- nostic testing is not a part of standard care in PTSD, SUD, or resi- dential treatment. Our study presents data on the feasibility and acceptability of implementing objective OSA diagnostic testing on a residential SUD treatment program for veterans with PTSD. Sleep disordered breathing is a spectrum (Schwab et al., 1998) ranging from mild upper airway resistance (e.g., snoring) to severe OSA. OSA is associated with sleep fragmentation and is defined by repeated episodes of apneas (pauses in breathing) and hypo- pneas (shallow breathing) with decreases in blood oxygenation during sleep. The apnea-hypopnea index (AHI) is derived by cal- culating the number of apneas and hypopneas per hour of sleep, and is the most commonly used metric of OSA severity, with mild OSA starting at AHI$5. OSA in veterans is associated with neu- rocognitive decline, hypertension, increased cardiovascular mor- tality, stroke, heart attacks, andfinancial burden on the health care system (Jennum & Kjellberg, 2011;Redline et al., 2010;Young et al., 2008). Furthermore, OSA is associated with more depression, anxiety, PTSD, SUD, psychosis, suicidal ideation, bipolar disor- der, and dementia compared to veterans without OSA (Sharafkha- neh et al., 2005). A systematic review of OSA prevalence in the general popula- tion found OSA ranged from 9% to 38%, and OSA risk increased with age and higher body mass index (BMI;Senaratna et al., 2017). Rates of OSA are significantly higher among veterans, with studies indicating diagnostic rates ranging between 67% to 83% (Krakow et al., 2006;Lettieri et al., 2016;Yesavage et al., 2012). Furthermore, both SUD and PTSD increase risk of OSA. A meta- analysis of veterans with PTSD found OSA prevalence was 75.7% (AHI$5;Zhang et al., 2017). Among individuals with any SUD, 53.3% were screened as being high risk for OSA (Mahfoud et al., 2009), with increased substance use severity increasing risk of OSA (Rose et al., 2014). While it is not entirely clear why veterans with PTSD present with higher rates of OSA compared to nonveterans without PTSD (Colvonen et al., 2015), there is convincing evidence that long- term alcohol ingestion and opioid use are important factors in pathogenesis of OSA (Le Bon et al., 1997;Vitiello et al., 1990; Wang & Teichtahl, 2007). For example, even after a single drink,normal sleepers can develop snoring and even exhibit breathing events resulting in oxygen desaturations (Block & Hellard, 1987). Alcohol relaxes upper airway dilator muscles, which increases air- way obstruction and increases nasal and pharyngeal resistance (Scanlan et al., 2000;Young et al., 2002) and prolongs the time required to arouse or awaken after an apnea occurs (Dawson et al., 1993;Robinson et al., 1985). Even during abstinence, individuals with SUD are more likely than controls to have OSA (Le Bon et al., 1997;Mamdani et al., 1989;Robinson et al., 1985). Research has demonstrated the detrimental impact of OSA on both SUD and PTSD outcomes. A retrospective study of veterans who had completed cognitive processing therapy, an evidence- based treatment for PTSD, found that those with untreated OSA (n= 69) showed less PTSD symptom improvement than those without OSA(N= 276;Mesa et al., 2017). However, those with OSA treated with positive airway pressure (PAP) showed more improvement in PTSD symptoms than those who were not treated (Reist et al., 2017). Both studies suggest that OSA screening/diag- nostic testing and treatment should be part of thefirst-line treat- ment for individuals with PTSD. There have been no studies examining the effect of untreated or treated OSA on relapse. However, there is circumstantial evidence that OSA may influence relapse rates. First, OSA is strongly linked to fragmented sleep (Antic et al., 2011), and it has been shown that disrupted sleep architecture predicts relapse among individuals abstinent from alcohol (Brower et al., 2001) and other substances (e.g., opioids and methamphetamines;Angarita et al., 2016). Second, OSA is linked with other factors involved in relapse, including deficits in most aspects of executive function- ing, decreased processing speed, increased perseverative responses or behaviors, impulsivity, and difficulty with problem solving (Gagnon et al., 2014). Finally, untreated OSA is linked to lower sleep efficiency (Williams et al., 2015), which is associated with more frequent and larger moodfluctuations (El-Ad & Lavie, 2005), thus potentially placing SUD patients further at risk for relapse (Brower, 2003). Studies are needed to clarify how OSA may influence relapse rates. Despite the detrimental effects of untreated OSA, it continues to be undiagnosed and untreated in many veterans, with estimates of 80% to 90% of veterans with OSA remaining undiagnosed (Alexander et al., 2016). There are two reasons for this: First, the symptoms of OSA (e.g., daytime fatigue, poor concentration, trou- ble sleeping, irritability) are often mistaken for the“primary disor- der”(e.g., SUD or PTSD), and thus OSA is not even considered as a contributor (Colvonen, Straus, et al., 2018). Second, there is sub- stantial evidence that OSA is increasing in younger veterans with co-occurring mental health disorders who do not have the classic risk factors (e.g., older age, overweight or obese per BMI), so OSA becomes difficult to identify (Colvonen et al., 2015;Rezaei- talab et al., 2018;Williams et al., 2015 ). As such, self-report OSA screening questionnaires, like the STOP-BANG or Berlin, that rely heavily on age, blood pressure, and BMI, are shown to be poor predictors of OSA in all veterans (Kunisaki et al., 2014; McMahon et al., 2017) as well as specifically among veterans with PTSD (Lyons et al., 2021). This suggests the need for objective OSA diagnostic testing among veterans. The literature suggests that residential treatment is effective in treating mental health disorders (Zhang et al., 2003) and is the appropriate level of care for individuals with severe SUD or PTSD DIAGNOSING OSA AMONG VETERANS WITH SUD AND PTSD 179 (Haller et al., 2019). More information is needed about specific programmatic elements that could increase effective outcomes and maximize successful long-term continued care (Proctor & Hersch- man, 2014). Due to the stable environment and frequent contact between the treatment team and the patient, the residential setting may be a more effective environment than outpatient settings for diagnosing and treating co-occurring OSA (Colvonen, Ellison, et al., 2018). PAP is the gold-standard treatment for OSA, with meta-analytic reports showing decreased sleep fragmentation and improvement in daytime sleepiness and functioning across a host of domains (Patil et al., 2019). Meta-analyses show significant decreases in apnea/hypopneas with PAP use with very large effect sizes (Schwartz et al., 2018). Increasing accessibility to evidence- based care for OSA in a residential setting may be a critical path- way for treating OSA and thereby potentially improving SUD/ PTSD treatment outcomes. However, it is unclear whether objec- tive testing of OSA, a necessaryfirst step to treatment, would be feasible on a residential unit for veterans with SUD and PTSD. Our study examined the feasibility and acceptability of objec- tive OSA diagnostic testing in a residential treatment unit for vet- erans with SUD and PTSD. We presentfindings on OSA prevalence and the relation between OSA and SUD/PTSD symp- toms. We hypothesized that objective OSA testing would be feasi- ble and acceptable. We also hypothesized that veterans with untreated OSA would have more severe SUD and PTSD symp- toms than those without OSA or with treated OSA. Finally, we make suggestions as to how residential units can implement OSA diagnostic testing and integrate PAP treatment. Methods Program Description The study took place in the Substance Abuse Residential Reha- bilitation Treatment Program (SARRTP) at the VA San Diego Health care System (VASDHS), a 14-bed residential substance use treatment program that also offers PTSD treatment for veter- ans with comorbid SUD and PTSD. The treatment team consisted of a clinical psychologist, psychiatrist, addiction therapists, nurs- ing staff, and social workers. The program was 28 to 35 days in duration (seven-day extensions were offered to veterans engaging in intensive individual PTSD treat- ment). Unit programming consists of cognitive–behavioral therapy groups for treating SUD, introducing new skills (e.g., anger manage- ment), engaging in experientially based activities (e.g., mindfulness/ relaxation), and other recovery-oriented programming (e.g., living skills, job skills). Patients diagnosed with PTSD related to any trauma type are offered services on the PTSD track and receive psy- choeducation about PTSD and the interplay of SUD and PTSD, attend a cognitive restructuring group where PTSD-related beliefs are addressed, and take part in an in-vivo group where they practice group exposures to commonly avoided situations (e.g., sitting in a crowded waiting room). Some veterans are offered intensive individ- ual evidence-based PTSD treatment three times a week. Participants All veterans participating in the PTSD track on the SARRTP unit at the VASDHS were offered participation in this study. Theonly exclusion criterion was unmanaged symptoms of psychosis, based on the discretion of the PTSD track clinical psychologist. Recruitment occurred between February 2019 and March 2020. Of the 60 veterans admitted to the unit, 47 veterans (78%) consented. Of the 47 veterans who consented, 2 veterans stated they did not want to be a part of the study after signing the consent. Data are presented on the remaining 45 veterans whofilled out question- naires and wore the OSA testing equipment. SeeTable 1for demographics. Procedures All research was approved by the institutional review board at the VASDHS. Veterans admitted onto the PTSD track on SARRTP were informed about the study from their SARRTP pro- vider during a one-on-one treatment planning session. Participants who expressed interest met with a study coordinator to learn more about OSA diagnostic testing procedures and were given the op- portunity to ask questions. Veterans who gave written consent to participate were given a home sleep apnea test (HSAT) overnight portable monitor for the diagnosis of OSA. We used the NOX T3 for our HSAT. Participants alsofilled out a daily sleep diary for seven days, and self-report measures (PTSD Checklist, Substance Use Inventory, Alcohol Use Disorder Identification Test, Client Table 1 Demographic and Baseline Characteristics (N = 45) Demographic variable Total% / M (SD) Age 42.9 (10.4) Sex Men 88.6% Women 11.4% Marital status Never married 22.7% Married 22.7% Divorced 47.7% Separated 4.5% Remarried 2.3% Substances used Alcohol 68.6% Marijuana 54.3% Sedatives/tranquilizers 15.2% Cocaine/crack 14.8% Opiates 17.1% IV opiate use 6.5% Service/branch Army 34.1% Navy 25.0% Marines 36.4% Reserves/National Guard 4.5% Ethnicity Hispanic 27.3% Non-Hispanic 72.7% Race White 72.7% Black 13.6% Bi/multi-racial 13.6% Pacific Islander/Asian 0% American Indian/Alaskan 0% Other 0% Height (inches) 69.2 (4.4) Weight (lbs) 180.8 (33.0) 180 COLVONEN ET AL. Satisfaction Questionnaire, demographics, Insomnia Severity Index, Epworth Sleepiness Scale, and the Pre-Sleep Arousal Scale). Participants were compensated $20. OSA diagnostic testing procedures were adapted with the help of doctors and staff on the unit to minimize patient burden and dis- ruption of current SARRTP procedures. All consenting and HSAT set-up were done at a time of day when no SARRTP classes were being held. Participants met with a study coordinator to set up and review procedures for the HSAT. All straps and nose cannulas were adjusted and prepared with study staff prior to the overnight testing, and equipment was put at veterans’bedside table. Medical tape was provided to keep the nose cannula andfinger clip in place. A pamphlet was given to participants with a step-by-step guide for setting up HSAT equipment. The HSAT was scored and reviewed by study staff using the American Academy of Medicine scoring rules (3% oxygen desaturation). Any participant with AHI $5 was asked if they wanted a referral to the Pulmonary Sleep Medicine clinic. If the participant consented to referral, HSAT summary data were sent to Sleep Medicine for review and possible PAP treatment. Measures OSA Diagnosis OSA was diagnosed using an HSAT portable recorder sleep monitoring systems. The HSAT AHI per hour output has anr=.93 when comparing the gold standard polysomnography (Cairns et al., 2014) and is approved for diagnosis in the American Academy of Sleep Medicine (Kapur et al., 2017). We used the NOX T3 HSAT, which has a simple monitor hook-up that the patients can use on their own with rip cords around the chest to measure breathing effort, a nose cannula to measure airflow and pauses in breathing, and afinger clip to measure oxygen desaturation. For individuals wearing a PAP device, the NOX T3 HSAT attaches to the PAP de- vice to capture residual AHI. All recorders were used for one night while on the SARRTP unit. An AHI$5 is considered to be mild, with those$15 being deemed moderate, and those$30 being severe. Historical OSA diagnosis was also retrieved from medical records to see newly diagnosed compared to previous diagnosis. Insomnia The Insomnia Severity Index (ISI;Morin et al., 2011)isa widely used measure of insomnia with well-established reliability and validity. The ISI consists of 7 items, three of which assess se- verity of insomnia (i.e., degree of difficulty falling asleep, staying asleep, and waking too early). The remaining questions tap satis- faction with sleep pattern, effect of sleep on daytime and social functioning, and concern about current sleep difficulties. Scores range from 0 (no clinically significant insomnia) to 28 (severe clinical insomnia), with a cut-off of 11 suggesting a diagnosis of insomnia (Morin et al., 2011). Daytime Sleepiness The Epworth Sleepiness Scale (ESS;Johns, 1991) is a validated 8-item questionnaire measuring daytime sleepiness. The questions ask individuals how likely they are to fall asleep, in eight different situations, on a scale of 0 to 3 (Would never dozetoHigh chance of dozing). Scores arefirst totaled, and higher scores indicatehigher severity of daytime sleepiness, with a cut-off of 10 suggest- ing clinically significant daytime sleepiness. Pre-Sleep Arousal The Pre-Sleep Arousal Scale (PSAS;Nicassio et al., 1985)rates the intensity of somatic (8 items) and cognitive (8 items) manifesta- tions of arousal prior to sleep. The PSAS shows strong internal con- sistency and reliability. The PSAS is a 16-item self-administered measure in which participants rate the intensity (1not at allto 5 extremely) of experienced arousal for somatic and cognitive sub- scales. Higher scores indicate higher intensities of pre-sleep arousal. Daily Sleep Diary Veterans completed a daily sleep diary at baseline and one week prior to discharge from the unit. Veteransfilled out daily in- formation on bedtime, sleep latency, number and duration of awakenings, wake time, total time in bed, sleep quality, and night- mares. Researchers then calculated two variables (total sleep time and sleep efficiency) based on participant daily entries. The pri- mary outcome measure used for this study was sleep efficiency, defined as the percent time spent sleeping given the number of hours in bed. PTSD Severity The PTSD Checklist (Weathers et al., 2013) is a 20-item self- report measure of PTSD symptoms with good psychometric prop- erties. The measure maps directly ontoDSM-Vdiagnostic criteria. Substance Use The Substance Use Inventory asks about the participant’s use of various substances, including alcohol, cocaine, heroin, marijuana, sedatives, PCP, stimulants, and hallucinogens, in the past 30 days, prior to SARRTP intake. The frequency, amount, and administra- tion route (smoked, oral, injected) were also assessed, along with questions about cravings and urges to use. Alcohol Use The Alcohol Use Disorders Identification test (Saunders, 1989) is a 10-item screening tool assessing alcohol consumption, drink- ing behaviors, and alcohol-related problems such as dependence or experience of alcohol-related harm in the month before SARRTP admission. Scores above 8 are considered hazardous or harmful alcohol use, while scores above 15 indicate high likeli- hood of alcohol dependence. Cannabis Use The Cannabis Use Disorder Identification Test—Revised (Ad- amson et al., 2010) is an 8-item self-report measuring marijuana use (e.g., yes/no) and behaviors regarding the use of marijuana. Scores above 8 are considered hazardous or harmful cannabis use, while scores above 12 indicate high likelihood of cannabis use disorder. Feasibility OSA testing feasibility was assessed via number of veterans that successfully completed OSA diagnostic testing with the HSAT. DIAGNOSING OSA AMONG VETERANS WITH SUD AND PTSD 181 Satisfaction The Client Satisfaction Questionnaire (Larsen et al., 1979) was revised by study staff to assess acceptability of OSA diagnostic testing. Individual questions asked the following: a) How useful was the Obstructive Sleep Apnea screening (NOX T-3) you received? b) Did you receive the information you wanted regard- ing your sleep? c) Would you recommend this process to other veterans on the unit? and d) How satisfied are you with the screen- ing process? Results were on a 4-point Likert scale ranging from 1 (Poor/No,Definitely Not/Quite Dissatisfied)to4(Excellent/Yes, Definitely/Very Satisfied). This instrument was used to measure participants’satisfaction with the intervention following HSAT testing, with higher score indicating higher satisfaction. Demographics Demographics questions were used to assess weight, ethnicity, race, height, relationship status, and service history. Data Analysis Data were analyzed with descriptive statistics and paired sample t-tests using SPSS Version 26. Results Feasibility Forty-five (95.7%) of the veterans successfully wore the HSAT for the testing night and successfully completed objective OSA di- agnosis testing; 2 veterans withdrew from the study after consent- ing. One hundred percent of the veterans who attempted the HSAT successfully completed objective OSA testing. Acceptability Of the 45 veterans who wore the HSAT, 82.2% (n=37) stated that both the usefulness of the OSA diagnosis and ease of the test- ing process were“Excellent,”with the remaining 17.8% of veter- ans (n=8) stating the process was“Good.”Thirty-three (73.3%) of the veterans said they would“Definitely”recommend the test- ing to other veterans, with the remaining 26.6% of veterans (n= 12)saying they“Probably”would recommend the OSA diagnostic testing. Finally, 44.4% (n= 20) of the veterans stated they were “Very Satisfied”with the process, and 55.6% (n= 25) of the veter- ans were“Mostly Satisfied.”Zero percent of respondents stated the process was“Not at all useful/feasible,”“Quite dissatisfied,” or“Would not recommend at all.” OSA Diagnoses Based on the overnight sleep studies, 53.3% of the veterans (n=24) met criteria for a diagnosis of OSA, although some veter- ans were already successfully treating their OSA with a PAP device on the unit. Specifically, 35.6% (n=16) were newly diag- nosed, 8.9% (n=4) were previously diagnosed with OSA and were actively using a PAP, 8.9% (n= 4) had previously been diag- nosed with the current recording confirming the diagnosis but were not using PAP, and 46.7% (n=21) had no OSA (SeeFigure 1). Finally, of the 20 veterans with untreated OSA (16 newly diagnosed and 4 veterans with reconfirmed OSA but not using their PAP treatment), 70.0% (n=14) consented to a pulmo- nary sleep clinic referral for PAP treatment. Baseline Differences by OSA Diagnosis Although the participants fell into four groups (newly diagnosed OSA, previously diagnosed OSA using PAP on unit, previously diagnosed OSA without a PAP on unit, and no OSA), we combined them into two groups based on their symptoms: OSA symptomatic group (AHI$5) and OSA negative/OSA treated group (AHI,5). The OSA symptomatic group included the newly diagnosed OSA veterans and the previously diagnosed OSA veterans who were not wearing PAP on the unit. The OSA negative/nonsymptomatic group included the veterans negative for OSA or previously diagnosed with OSA but actively using PAP. We examined baseline differences between these two groups and found no differences in PTSD, ESS, ISI, or sleep efficiency (seeTable 2). Discussion This study suggests that objective testing for OSA is feasible and acceptable for veterans with SUD and PTSD in a residential setting. Of the 47 veterans who consented, only 2 veterans declined to participate in the study and 45 veterans successfully received testing, showing 95.7% feasibility. We believe that the 2 veterans who withdrew before the overnight HSAT was scheduled withdrew due to study burden (questionnaires, sleep diary, and HSAT) at a vulnerable time in recovery. Of the veterans who attempted to wear the HSAT, 100% were successful in completing the overnight study and received accurate AHIs. Further, we received positive feedback on the acceptability of the overnight HSAT test, with 100% of respondents saying they were“mostly satisfied”or better with the overall process. We found that 53.3% of veterans had a diagnosis of OSA. While 55.6% of participants either did not have OSA or were suc- cessfully treating it with PAP, 44.5% of veterans would have been left untreated on the residential unit without HSAT testing. The number of veterans on the residential unit with untreated OSA is alarmingly high given the potential detrimental effects of untreated OSA on SUD and PTSD outcomes (Colvonen, Straus, et al., 2018; Figure 1 OSAObjectiveTestingWithVeteransontheSARRTP PTSD Track, Including Those With Positive Airway Pressure (PAP) Treatment 46.70% 35.60% 8.90%8.90% Diagnosis (Dx) of OSA No OSA New OSA Previous Dx w/ PAP* Previous Dx w/o PAP* 182 COLVONEN ET AL. Wang & Teichtahl, 2007). The large percentage of veterans in res- idential treatment with untreated OSA also offers a unique oppor- tunity for early evidence-based intervention. While PAP is the gold-standard treatment for OSA, adherence rates are low among veterans with PTSD (Colvonen, Straus, et al., 2018). For example, a recent meta-analysis found that PAP adherence was lower in patients with both OSA and PTSD than OSA alone (Zhang et al., 2017). Early adherence is key to long-term adherence rates for PAP (Budhiraja et al., 2007;Weaver et al., 1997), which suggests that patients should receive follow-ups early after PAP initiation to address any concerns (e.g., claustrophobia) and assist with titra- tion and maskfit(Drake et al., 2003). Due to the dose response of PAP with positive outcomes, increasing adherence to PAP with desensitization to the mask may be essential to help veterans with PTSD (Goldstein et al., 2017). Residential care may be a uniquely stable and supportive environment to initiate PAP therapy due to the reduced external stressors and distractions, controlled environ- ment with professional support, increased structure and account- ability (e.g., more likely to attend sessions and follow through on treatment planning), and increasing access to clinicians to help intervene and motivate individuals (Haller et al., 2019). Future studies should examine whether evidence-based treatment for OSA on a residential unit leads to improved SUD/PTSD treatment outcomes. While we hypothesized that veterans with untreated OSA would have worse SUD, PTSD, and sleep severity, our results did not support this. We found no differences between untreated OSA and the treated or no OSA group on any baseline measures of sleep, substance use, or PTSD severity. We believe that this has to do with the ceiling effects of SUD, PTSD, and sleep severity among veterans just entering residential care, minimizing the variability necessary tofind associations. Another possibility is our small sample size limiting the power necessary to detect differences. Thesefindings may suggest that, in certain settings, symptom se- verity cannot be used as an indicator of high risk for OSA.We recommend integrating objective OSA diagnostic testing into residential care for all residents whether or not they show classic risk factors for OSA (e.g., high BMI or older age). First, as previously mentioned, symptom severity does not discriminate between OSA positive/negative. Second, there is increasing evi- dence that self-report questionnaires for“high risk of OSA”are not accurate as screeners for veterans or PTSD (Kunisaki et al., 2014;Lyons et al., 2021;McMahon et al., 2017). Together, there are no predictable visual, symptomatic, or self-report screeners to indicate who is in need of PAP treatment. Our study has several limitations, including the small sample size and lack of follow-up data. As such, the long-term effects of PAP treatment on SUD and PTSD outcomes are unclear. Given these limitations, this study is best viewed as an objective testing protocol development and will require examination of PAP treat- ment and how that affects SUD and PTSD outcomes. However, this study offers strong support for the importance of diagnostic testing for OSA for individuals with SUD and PTSD while in a residential care setting. Objective testing, and possibly treatment, for OSA is feasible and acceptable in a residential care setting. References Adamson, S. J., Kay-Lambkin, F. J., Baker, A. L., Lewin, T. J., Thornton, L., Kelly, B. J., & Sellman, J. D. (2010). An improved brief measure of cannabis misuse: The Cannabis Use Disorders Identification Test-Re- vised (CUDIT-R).Drug and Alcohol Dependence,110(1–2), 137–143. https://doi.org/10.1016/j.drugalcdep.2010.02.017 Alexander, M., Ray, M. A., Hébert, J. R., Youngstedt, S. D., Zhang, H., Steck, S. E., Bogan R. K., & Burch, J. B. (2016). 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Literature Review Outline: To ensure that students are on track, submit a 2‐3 page double‐spaced APA formatted justification for your topic of interest. This justification should be written in formal
D re am in g Th e P ro p ortio n al E xp erie n ce o f D re am T yp es in R ela tio n t o P osttr a u m atic Str e ss D is o rd er a n d I n so m nia A m on g S urv iv o rs o f I n tim ate P artn er V io le n ce Alw in E . W ag en er Onlin e F ir s t P u b lic a tio n , O cto b er 1 3, 2 022. h ttp ://d x.d oi. o rg /1 0.1 037/d rm 0000227 CIT A TIO N Wag en er, A . E . ( 2 022, O cto b er 1 3). T h e P ro p ortio n al E xp erie n ce o f D re am T yp es in R ela tio n t o P o sttr a u m atic S tr e ss Dis o rd er a n d In so m nia A m on g S urv iv o rs o f In tim ate P a rtn er V io le n ce . Dre am in g . A dva n ce o n lin e p ub lic a tio n . http ://d x.d oi. o rg /1 0.1 037/d rm 0000227 The Proportional Experience of Dream Types in Relation to Posttraumatic Stress Disorder and Insomnia Among Survivors of Intimate Partner Violence Alwin E. Wagener Department of Psychology and Counseling, Fairleigh Dickinson University Survivors of intimate partner violence (IPV) commonly suffer from posttrau- matic stress disorder (PTSD), insomnia, and nightmares. Past studies demonstrate a link between replicative (i.e., replay the trauma) and recurrent (i.e., repeating) night- mares and PTSD and insomnia. However, there is a lack of research on the variety of dreams and nightmares experienced in relation to PTSD and insomnia. This study explored 5 types of dreams and nightmares among 499 IPV survivors recruited through social media to complete an online cross-sectional survey. The dream types were selected based on theories of dreaming, suggesting it exists on a continuum of both repetition and emotion (i.e., dream or nightmare) and that more severe PTSD and insomnia symptomology should be linked to repetitive nightmares. Dream types were transformed for each participant into ratios that showed the proportion of each type of dreaming in relation to all the dreaming reported by the participant over the past 3 days. Then, multiple regressions were used to examine whether those dream types were predictive of PTSD, insomnia, and PTSD symptom criteria. The results showed that only replicative nightmares and novel (i.e., new) dreams were predictive. Additionally, it was discovered that across PTSD and insomnia symptom severities, novel dreams remained relatively constant in number, whereas other types of dream- ing, particularly nightmares, increased in frequency. Keywords:intimate partner violence, dreams, nightmares, PTSD, insomnia Survivors of intimate partner violence (IPV) often experience nightmares along with other symptoms of posttraumatic stress disorder (PTSD) (Nathanson et al., 2012;Phelps et al., 2008). There is a growing understanding of the relationship between nightmares and PTSD in dream literature (Campbell & Germain, 2016; Lemyre et al., 2019). However, research on nightmares among trauma survivors Alwin E. Wagener https://orcid.org/0000-0002-9804-7274 This study was supported by a Grant from International Association for the Study of Dreams and Dream Science Foundation. Correspondence concerning this article should be addressed to Alwin E. Wagener, Department of Psychology and Counseling, Fairleigh Dickinson University, 285 Madison Avenue, Madison, NJ 07940, United States. Email:[email protected] 1 Dreaming ©2022 American Psychological Association ISSN: 1053-0797https://doi.org/10.1037/drm0000227 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. tends to differentiate types of nightmares without examining co-occurring nonnight- mare dreaming (de Dassel et al., 2018;Lemyre et al., 2019). For the above reasons, along with insight from research suggesting that dreaming experiences may exist on a continuum related to the emotion and repetition of dream types (Levin & Nielsen, 2007), theories suggesting dreaming may be part of an internal psychological healing process (Hartmann, 2011;Levin & Nielsen, 2007), and research indicating insomnia may be a co-occurring disorder linked to both PTSD and nightmares (Nappi et al., 2012;Pigeon et al., 2013), the present study investigated the relationship of PTSD and insomnia to a variety of types of dreaming, both nightmares and nonnightmare dreams, among IPV survivors to better understand relationships between partici- pants’oneiric experiences and symptoms. Intimate Partner Violence and Nightmares IPV is a traumatic experience with many serious adverse consequences includ- ing death, injury,financial hardships, mental illness, and social isolation (Coker et al., 2002;Lutwak, 2018;Spencer et al., 2019;Vos et al., 2006). One of the frequent mental illnesses related to experiencing IPV is PTSD (Golding, 1999;Nathanson et al., 2012;Spencer et al., 2019). A study byNathanson et al. (2012)using a diagnos- tic assessment interview found that 57.4% of a community sample of 101 IPV survi- vors had PTSD. Thisfinding is generally consistent with a prior meta-analysis by Golding (1999)showing PTSD rates among IPV survivors ranging from 31% to 84% depending on the IPV populations and assessment approach, though Nathan- son’sfinding has the benefit of being based on a diagnostic interview as opposed to self-assessments used by most of the studies in the meta-analysis. A common experience as part of PTSD is nightmares, often with elements of the traumatic experience being replayed within those nightmares (Phelps et al., 2008;Rasmussen, 2007). The frequency of these nightmares, which studies indicate may be present for 30% to 50% of IPV survivors (Pigeon et al., 2011;Rasmussen, 2007), and the reported negative impact of nightmares (Pigeon et al., 2011;Rasmus- sen, 2007), have been documented in studies but without the context of the entirety of oneiric experiences. Specifically, the occurrence of nonnightmare dreaming and the way dreams and nightmares may co-occur among those suffering from PTSD is not understood. Insomnia is another serious and negative symptom experienced by IPV survi- vors that is both a symptom of PTSD and an independent diagnosis (El-Solh et al., 2018;Nappi et al., 2012;Pigeon et al., 2011). There is support for insomnia some- times having an independent clinical course from PTSD, such that successfully addressing other symptoms of PTSD does not resolve the insomnia (Nappi et al., 2012;Pigeon et al., 2011,2013), though for some individuals, treating PTSD broadly will also resolve insomnia (Pigeon et al., 2011). Among IPV survivors, insomnia is frequently described and identified as a symptom and disorder that impairs both functioning and recovery from the trauma (Pigeon et al., 2011). There is research linking nightmares to insomnia (Habukawa et al., 2007;Woodward et al., 2000), but, as with PTSD, the overall dreaming experience for those suffering from insomnia is not well understood. 2WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. In addition to the links between PTSD and insomnia, nightmares also have a relationship to both PTSD and insomnia that defies a simple description of night- mares as a symptom (Phelps et al., 2008;Pigeon et al., 2011,2013). Just as with insomnia, for some individuals, many of the other symptoms of PTSD may be resolved while nightmares persist (Phelps et al., 2008;Pigeon et al., 2011). There is also some support for nightmares prompting individuals to fear sleeping resulting in insomnia, whereas for others, nightmares may occur without insomnia (Phelps et al., 2008;Pigeon et al., 2011). Overall, research broadly shows a complicated rela- tionship between nightmares, insomnia, and PTSD, but little research exists showing how differing types of dreams and nightmares, particularly dreams, are related to insomnia and PTSD. Dream Emotion and Repetition in Relation to PTSD and Insomnia Links between PTSD severity and nightmares that replicate (i.e., replay) trauma or recur (i.e., happen more than once without replaying trauma) are com- mon and well accounted for in research (de Dassel et al., 2018;Hartmann, 2011; Mellman et al., 2001). However, there is limited research differentiating nightmares based on whether they generate novel (i.e., new) content in relation to PTSD (Hart- mann, 2011;Nielsen & Levin, 2007). This distinction is highlighted as important by several recent theorists who propose that novel nightmares are part of a psychologi- cal recovery process, whereas recurring and replicative nightmares may indicate an impairment in recovery (Hartmann, 2011;Levin & Nielsen, 2007). These theories point to two areas of focus, namely, the emotion (i.e., nightmare or dream) and rep- etition of dreaming (i.e., replicative of a trauma experience, recurring, or novel), for understanding the role of nightmares and dreams in trauma recovery. Nielsen and Levin’s (2007)neurocognitive model of disturbed dreaming (NMDD) describes the occurrence of a fear extinction process in nightmares, whereasHartmann’s (2011)contemporary theory of dreaming (CTD) proposes that nightmares allow trauma-related emotions to be connected to other experiences, imagined and from memory, thereby lessening the intensity and disruptive quality of those emotions. These conceptualizations of nightmares suggest that replicative and recurring nightmares may be an impairment of the psychological healing pro- cess and that dreams (not nightmares) indicate there is a lower level of affective dis- tress. Both of these theories are based on observations of trauma recovery and understandings of neurological processes in dreaming. However, there is little em- pirical evidence of a healing process linked to nightmares or of whether nonnight- mare dreaming coexists with nightmares among individuals with PTSD. There have been some recent attempts to understand the relationship between types of nightmares and PTSD and insomnia symptoms (Davis et al., 2007;de Das- sel et al., 2018;Wagener, 2019). Across the studies, several trends are observed con- sistent with theories positing a continuum of nightmare experiences in relation to PTSD (Hartmann, 2011;Levin & Nielsen, 2007). Thefirst is that replicative dreams are most strongly linked to PTSD. The second is that recurrent dreams are also linked to PTSD just not as strongly, and the third is that nightmares with new con- tent are correlated to PTSD but not as strongly as the replicative and recurrent nightmares (Davis et al., 2007;de Dassel et al., 2018;Wagener, 2019). Though PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 3 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. studies suggest possible relationships between dream and nightmare types to PTSD and insomnia (Davis et al., 2007;Wagener, 2019), they notably do not explore whether nonnightmare dreaming occurs simultaneously with nightmares or varies in frequency with PTSD and insomnia as would be predicted by CTD and NMDD. This information would be valuable for researchers evaluating CTD and NMDD and for clinicians wanting to better understand how the oneiric experiences of patients may relate to PTSD and insomnia symptoms. Toward that end, a research study examining the relationship of PTSD symptoms and insomnia to the frequency of novel dreams, repeating dreams, novel nightmares, recurrent nightmares, and replicative nightmares was conducted to determine whether the previously observed trends remain and discover the relationship of repeating dreams and novel dreams to PTSD and insomnia. To generate a more detailed understanding of dream types to PTSD symptoms, the frequencies of dream types were also examined in relationship to PTSD symp- tom criteria (i.e., criteria B [reexperiencing], C [avoidance], D [negative cognitions and mood], and E [arousal]) (American Psychiatric Association, 2013). Because there is little specific research on those relationships, the hypotheses regarding those relationships were made tentatively, largely based on the NMDD typology of dreaming showing dream types in relation to affect distress and awakening (Levin & Nielsen, 2007, p. 486). Predictions for two types of dreaming, novel nightmares and recurrent dreams, were influenced by both NMDD and previous theorizing (Domhoff, 2000) that recurrent dreams and nightmares occur because what is caus- ing them is not being addressed. The lack of repetition in novel nightmares was therefore hypothesized to be unrelated to avoidance, whereas the repetition of dreams was hypothesized to be correlated with avoidance. Also, it is important to note that though novel nightmares are linked to trauma recovery by CTD and NMDD, they are also associated with trauma and indicate a reaction to trauma. Based on that, they are hypothesized to be positively correlated with PTSD and insomnia, though with less strength than replicative and recurrent nightmares. To better understand the relationship between types of dreaming experiences and PTSD and insomnia, ratios for dream experiences were created. The choice to use ratios instead of reported numbers of dream experiences is due to individual variations in the ability to remember dreams. Numerous studies demonstrate that individuals can learn to remember their dreams, and that as they practice, the num- ber of dreams they remember increases (Aspy et al., 2015;Beaulieu-Prévost & Zadra, 2005). This information indicates that by assessing and comparing the raw number of reported dreams, the memory and focus on remembering dreams is actually one aspect of what is being measured. By generating ratios of dream types for individual participants, it becomes possible to compare dream types more easily between individuals. These ratios were generated with the view that allfive types of dreaming are part of an overall oneiric experience, so each type of dreaming experi- ence was compared with the total reported number of dream experiences. Method This study used an IRB-approved, online, cross-sectional, survey design using Qualtrics, a survey design and distribution program. In Qualtrics, an online survey 4WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. security setting was used preventing multiple entries from the same Internet proto- col (IP) address. Participants were recruited through social media using a $25 dollar gift certificate drawing as an incentive. In the announcement, participants were informed that by completing the survey, they would be entered into a drawing for 20 gift certificates. Initially, the study announcement was sent to online Facebook groups focused on supporting survivors of IPV. This approach generated few responses. To increase recruitment, Facebook advertisements were purchased using funds from an IASD and Dream Science Foundation research grant. The use of advertisements on Facebook to recruit participants is a relatively new approach that demonstrates promise for reaching marginalized populations, increasing the num- ber of participants compared with traditional recruitment, and possibly generating better data than traditional approaches (Harris et al., 2015;Jones et al., 2017;Khatri et al., 2015;Thornton et al., 2016). The advertisements displayed the recruitment announcement that included a link for the online survey and were sent to Facebook members in the United States over 21 years of age who had expressed interest in groups related to IPV, including spousal abuse and domestic violence groups. This approach generated a large number of survey responses. Facebook pro- vided data on the advertisement that showed 1,893 individuals saw the invitation to the study, 1,179 individuals opened the survey, and 668 individuals met the require- ments for participation and agreed to participate (56.7% of those who opened the survey). Of those, 499 (74.7% of those who agreed to participate) completed the survey to the extent that their responses were used in analyses with 458 (68.6% of those who agreed to participate) fully completing it. Participant Demographics The average age of participants was 38.73 years (SD= 12.12). In terms of race/eth- nicity, 415 (83.2%) reported they were White, 10 (2%) African American, 16 (3.2%) Hispanic, four (.8%) Asian, seven (1.4%) Native American, one (.2%) Pacific Islander, two (.4%) declined to say, with the remaining 44 (8.8%) participants reporting a combi- nation of race/ethnicities. The gender of participants was predominantly female (N= 470, 94.2%), though also included seven (1.4%) males, four (.8%) trans males, one (.2%) trans female, 14 (2.8%) gender-nonconforming/gender-queer participants, and two(.4%)whoreported“other.” Participants reported a variety of abuse experiences. The categories of abuse were physical, emotional, verbal, and sexual. Only 26 (5.2%) participants reported only a single form of those four types of abuse. Among participants, 356 (71.3%) reported physical abuse, 488 (97.8%) reported emotional abuse, 446 (89.4%) reported verbal abuse, and 263 (52.7%) reported sexual abuse. The types of abuse are consistent with those that lead to PTSD among IPV survivors. TheDSM–5 requires“exposure to actual or threatened death, serious injury, or sexual violence in one (or more) of the following ways,”and those ways are direct experience, wit- nessing, learning that it happened to a close friend or family member, or through prolonged exposure to details of traumatic events (American Psychiatric Associa- tion, 2013). The nature of this study makes it impossible to assess whether partici- pants met this criterion, though even participants endorsing just emotional or verbal abuse mayfit the criteria based on having felt threatened with death, physical PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 5 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. violence, or sexual violence or through perceiving others such as their children as being threatened. Participants’abuse ended an average of 5.80 (SD= 7.16) years prior to complet- ing the survey. The abusive relationships had lasted an average of 7.40 (SD= 7.12) years before ending, with 74.1% of participants reporting they had been out of any abusive relationship for more than a year. Among participants, 62.7% reported more than just one abusive relationship in their lifetime with those reporting more than one relationship having an average of 2.13 (SD= 1.32) abusive relationships prior to the last one experienced. At the time they completed the survey, only 143 participants reported currently dating or being in a committed relationship, down from 480 who reported having been in a committed abusive relationship. Additional demographic information can be seen inTable 1. Taken together, the participants in this study suffered a variety of abuses from their intimate partner with whom they had been together for many years and from whom they have been apart for many years. Instruments Demographics The demographics instrument in this study included general demographics such as age, ethnicity, and gender along with more detailed information related their Table 1 Participant Demographics Demographic characteristicsn% Sexual orientation Heterosexual 375 75.3 Homosexual 13 2.6 Bisexual 88 17.7 Other 22 4.4 Total 498 100 Relationship status Married 194 38.9 Committed relationship Cohabitating 192 38.5 Living separately 75 15 Dating 14 2.8 Civil union 5 1 Total 480 96.2 Education level Some high school completed 13 2.6 High school diplomas or GEDs 155 31.1 Associates degree 103 20.7 Bachelor’s degree 117 23.5 Graduate or professional degree 60 12 Other 50 10 Total 498 99.9 Household income Under $30,000 296 59.3 $30,000–$59,000 147 29.5 $60,000–$100,000 47 9.4 Over $100,000 9 1.8 Total 499 100 Note. GED = general equivalency diploma. 6WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. experiences of IPV such as the types of abuse they experienced. The format and con- tent was adapted from previous studies of IPV survivors (Flasch et al., 2017;Murray et al., 2015;Wagener, 2019) and was used to contextualize the results of the study. Posttraumatic Stress Disorder Checklist for DSM–5 The Posttraumatic Stress Disorder Checklist forDSM–5(PCL-5) was used to assess PTSD symptoms among participants. The PCL-5 uses 20,five-point Likert questions to assess for PTSD symptoms experienced over the last month. It is a self- report assessment aligned with theDiagnostic and Statistical Manual of Mental Dis- orders(DSM–5) symptom criteria (U.S. Department of Veterans Affairs, 2014; Weathers et al., 2013). The PCL-5 has strong psychometrics, with a recent study showing a Cronbach’s alpha of .96 and test–retest reliability of .84 over a period ranging from 22 to 48 days (Bovin et al., 2016). Additionally, it has been found to have strong construct validity. The PCL-5 has a cutpoint of 31–33 (Bovin et al., 2016;Weathers et al., 2013). In the present study, the Cronbach’s alpha was found to be .912, which is in line with previous studies. To eliminate autocorrelations, the PCL-5 question asking about“repeated, disturbing dreams of the stressful experi- ence”was not used in the primary statistical analyses. Pittsburgh Sleep Quality Index The Pittsburgh Sleep Quality Index (PSQI) is an instrument used to assess sleep quality and quantity using a 19 item self-assessed questionnaire with open and multiple-choice questions. It uses a scoring system producing results of 1–21, with 5 being a cut-score above which is indicative of impaired sleep quality (Backhaus et al., 2002;Carpenter & Andrykowski, 1998). Studies have shown a Cronbach’s alpha of .80–.85 and test–retest reliability of .86 (45.6618 days) to .90 (2-day inter- val) along with good construct validity (Backhaus et al., 2002;Carpenter & Andry- kowski, 1998). The Cronbach’s alpha for the current study in lower than those found in the studies ofBackhaus et al. (2002)andCarpenter and Andrykowski (1998)but still acceptable at .725 (461). Before using the PSQI in the primary statis- tical analyses, the question in it asking if sleep difficulties are related to“bad dreams”was removed to eliminate autocorrelation. Types of Dreams and Nightmares Survey The Types of Dreams and Nightmares Survey contains six questions and is adapted from a previous instrument (seeWagener, 2019) that was reviewed by four mental health counseling experts with experience and knowledge related to IPV, dreams, and nightmares. The adaptations were designed tofit the broader explora- tion of dream and nightmare types but retained language from the previous instru- ment where possible. The survey defines thefive types of dreams and nightmares assessed in the survey and asks participants to select the number of each type of dream or nightmare experienced over the last three days. Asking about the types of dreams experienced over the past three days was a modification done to enable participants to better recall and differentiate between the types of dreams and nightmares they experienced. The short time-period was prompted by observing that studies using a week-long time span generated fewer dream reports than studies in sleep labs recording dreams upon awakening (Krakow et al., 2002;Schredl & Olbrich, 2019;Van Schagen et al., 2016). Three days was a compromise between the two approaches. It was hoped that it was enough time to PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 7 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. gather an adequate number of responses, while not being so long as to make it diffi- cult for participants to recall and identify the type of dreams experienced. Thefive dream types used in the study were generated based on CTD and NMDD and for the ability to differentiate them in a survey design. A typology for dreaming is found inLevin and Nielsen (2007, p. 486), which was helpful for developing the categories, but the different dream types were weighed against likely participants’ abilities to name and properly categorize their dream types. The currentDSM–5defini- tion of nightmares along with the popular definition of nightmares does not use being awakened by the nightmare as definitional criteria (American Psychiatric Association, 2013;Collins English Dictionary, n.d.), so that differentiation between bad dreams (also called disturbed dreaming) and nightmares was not used. Instead, based on repe- tition of content being linked to greater severity according to CTD and NMDD, bad dreams and nightmares were combined and a differentiation between novel and recur- rent nightmares was used (Hartmann, 2011;Levin & Nielsen, 2007). Each type of dream and nightmare was named and defined in the survey. An example of how replicative nightmares were presented in the survey was with the description,“Nightmares That Replay Frightening or Disturbing Waking Life Experiences.”Novel nightmares were presented as,“New Nightmares- Nightmares that Do not Repeat and Do Not Replay Abuse you have experienced.”The choices provided for participants to report how many of each type of dream they experi- enced in the past three days were“not at all,”“once,”“twice,”“three times,”“four times,”“five times,”“six times,”or“more than six times.”For those who answered more than six times, they were directed tofill in the specific number of that type of dream or nightmare they experienced over the past three days. The number of dreams for each type of dream or nightmare was then used in the study. Data Analyses All data were analyzed using the Statistical Package for the Social Sciences (SPSS). There were six dependent variables, PTSD, PTSD symptom criteria B, C, D, and E, and insomnia, used in the analyses andfive independent variables, the ra- tio score for each dream type. Stepwise multivariate regression analyses were used to determine which of the independent variables significantly predicted each of the dependent variables. Becausefive predictor variables were used in the multiple regressions, a Bonferroni correction was applied, and thepvalue was changed from .05 to .001 for all the analyses. Based on the primary analyses, post hoc multiple regression analyses of the raw frequencies of thefive types of dreams in relation to PTSD and insomnia were run after 14 outliers had been removed using a Mahalano- bis Distance Test (Tabachnick & Fidell, 2013). Finally, a multiple regression analysis was used to understand the relationship between the total number of reported dreams and nightmares and PTSD and insomnia in response to a post hoc question. Results Preliminary Analyses Initial analyses of the study variables showed most of the participant popula- tion had symptoms consistent with PTSD (M= 46.88,SD= 14.49). The cutoff score 8WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. can be interpreted as being between 31 and 33, so using the conservative, higher cut- off score of 33, 82% of participants had symptoms consistent with a diagnosis of PTSD. There were even more participants with symptoms consistent with insomnia. The cutoff score for the PSQI is 5, above which participants have symptoms consist- ent with insomnia. The mean PSQI score in this study was 13.05 (SD= 3.72), and 98.1% of participants scored above the cutoff. The psychometrics for the variables are inTable 2. In an analysis of dream and nightmare types, most participants reported having each of the dream and nightmare types within the past three days. It is also notewor- thy that of the 499 participants reporting the types of dreaming they experienced, 178 (35.7%) reported no replicative nightmares, 156 (31.3%) no recurrent night- mares, 134 (26.9%) no novel nightmares, 239 (47.9%) no repeating dreams, and 176 (35.3%) no new dreams. Out of 499 participants, the average number of total night- mares and dreams reported by participants was 6.28 (SD= 4.04, Skew = .11, Kurto- sis = .19), meaning that participants reported an average of two dreams per night. This frequency of dreams and nightmares is surprising and higher than shown in other studies of trauma survivors, with studies of sexual assault survivors reporting between 5.21 and 6.54 nightmares/week (Krakow et al., 2000,2002) and a recent study of participants with diverse mental disorders recording 4.84 (SD= 3.16) night- mares per week (Van Schagen et al., 2016). It is important to note those studies looked at nightmares and did not include dreams. However, when looking at only nightmares in the current study the mean nightmares per three days was 3.97 (SD= 2.96) which translates to 9.26 per week. A difference between this study and many others is that participants were asked about specific dream and nightmare types experienced within the past three days. Requesting for recall within three days instead of a more typical week time-period may, as was intended in the study design, aid memory of oneiric experiences, and prompting recall of specific dreams and nightmares may also aid recall, though that is speculative. Another possibility is that the high level of insomnia among the participants leads to an experience more akin to sleep studies that ask participants to recall dreams after awakening them from REM sleep. In those studies, dream recall rates are higher. A recent study by Schredl and Olbrich (2019)found a mean of 3.67 (SD= 1.76) dreams being remem- bered among 24 participants with insomnia and restless legs syndrome diagnoses Table 2 Variable Psychometrics VariablesNM5% Trimmed meanSDRange Skew Kurt PTSD 499 46.88 47.14 14.49 6–80 0.21 0.47 PSQI 455 13.09 13.16 3.71 2–21 0.25 0.47 Replicative nightmares 495 1.44 1.20 2.17 0–31 6.61 75.75 Recurrent nightmares 495 1.41 1.26 1.50 0–11 1.56 3.83 Novel nightmares 495 1.50 1.34 1.50 0–8 1.38 1.89 Repeating dreams 495 1.14 0.95 1.57 0–12 2.01 6.08 Novel dreams 495 1.43 1.26 1.69 0–18 2.68 18.04 Criterion B 499 11.58 11.62 4.48 0–20 0.14 0.65 Criterion C 499 5.35 5.46 2.06 0–8 0.58 0.37 Criterion D 499 16.54 16.64 6.13 0–28 0.24 0.70 Criterion E 499 13.42 13.47 4.78 1–24 0.15 0.46 Note. PSQI = Pittsburgh Sleep Quality Index; PTSD = posttraumatic stress disorder. PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 9 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. during a two night period, translating to 12.85 per week. This is an area where future research may be helpful in better understanding dream recall among this popula- tion. Overall, the demographic information indicates that participants in this study were suffering from high levels of PTSD symptoms and insomnia and experiencing frequent nightmares and dreams. Dream Types in Relation to PTSD and Insomnia SeeTable 3for statistical data from the regressions showing which dream types predict PTSD and insomnia andTable 4for statistical data from regressions show- ing which dream types predict each of the PTSD symptom criteria. Based on these analyses, most hypotheses were not supported. The only dream types to significantly predict PTSD, PTSD symptom criteria, and insomnia were novel nightmares and replicative nightmares. Post Hoc Analyses Most studies examining dreams use the reported number of dreams, the raw frequencies. To make thefindings in this study more relatable to previous studies, the analyses were run again with raw frequency scores as seen inTable 5. Only two variables were significant in relation to PTSD and insomnia, replicative and recur- rent nightmares. The disappearance of novel dreams from the raw score analyses prompted curiosity as to why novel dreams only appear as significant when put into context with other dream types using the ratio score. To better understand why that difference exists, scatterplot graphs were examined (seeFigure 1). Those graphs demonstrated that the frequency of novel dreams is relatively consistent across PTSD and insomnia scores even as the ratio of novel dreams noticeably decreased as PTSD and insomnia scores increase. Based onfinding both a lack of change in number of novel dreams and a change in the proportion of novel dreams to all dreams and nightmares in relation to PTSD and insomnia, it seems clear that the number of total dreams and nightmares must increase with PTSD and insomnia severity. To confirm that, multiple regression Table 3 Stepwise Regressions Results Between Dream-Type Ratio Scores and PTSD and Insomnia Variable B 95% CI for BSEBbR 2 DR 2 PTSD (N= 490) Model 1 Constant 41.629 [39.983, 43.275] 0.838 Replicative nightmares 17.267 [11.432, 23.103] 2.970 0.255** 0.065** 0.063** Model 2 Constant 44.297 [41.987, 46.607] 1.176 Replicative nightmares 13.691 [7.509, 19.874] 3.147 0.202** 0.084* 0.08* Novel dreams 9.153 [ 14.765, 3.542] 2.856 0.149* Insomnia (N= 447) Model 1 Constant 13.713 [13.252, 14.174] 0.234 Novel dreams 4.223 [ 5.706, 2.740] 0.755 0.256** 0.066** 0.064** Note. CI = confidence interval; PTSD = posttraumatic stress disorder. *p= .001. **p,.001. 10WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. analyses were conducted. The regressions showed total dreams and nightmares had a significant, positive relationship with both PTSD (R 2= .12,b= .35,F(1, 478) = 66.12,p,.001, 95% CI [.82, 1.34]) and insomnia (R2= .04,b= .21,F(1, 436) = 19.136,p,.001, 95% CI [.09, .25]), consistent with the graphs. Discussion Understanding the results of this study are aided by recognizing the dream types used in the main analyses are proportions of total dreaming. With that in mind, the results indicate that as the proportion of novel dreams decreases, replaced by the other forms of dreaming, there is a significant and moderate increase in insomnia symptoms and the PTSD criterion E, symptoms of alterations in arousal Table 4 Stepwise Regressions Results Between Dream-Type Ratio Scores and PTSD Symptom Criteria Variable B 95% CI for BSEBbR 2 DR 2 Criterion B with nightmare question removed Model 1 Constant 8.669 [8.240, 9.097] 0.218 Replicative nightmares 5.294 [3.776, 6.813] 0.773 0.296* 0.088* 0.086* Criterion C Model 1 Constant 5.670 [5.420, 5.920] 0.127 Novel dreams 1.492 [ 2.296, 0.688] 0.409 0.163* 0.026* 0.024* Criterion D Model 1 Constant 15.453 [14.712, 16.195] 0.378 Replicative nightmares 5.534 [2.904, 8.163] 1.338 0.184* 0.034* 0.032* Criterion E Model 1 Constant 14.370 [13.794, 14.947] 0.293 Novel Dreams 4.432 [ 6.287, 2.578] 0.944 0.208* 0.043* 0.041* Note. N= 490. CI = confidence interval; PTSD = posttraumatic stress disorder. *p,.001. Table 5 Stepwise Regressions Results Between Raw Frequency of Dream-Types and PTSD and Insomnia Variable B 95% CI for BSEBbR 2 DR 2 PTSD (N= 481) Model 1 Constant 40.401 [38.823, 41.980] 0.803 Replicative nightmares 3.597 [2.758, 4.435] 0.427 0.360* 0.129* 0.128* Model 2 Constant 38.370 [36.653, 40.087] 0.874 Replicative nightmares 2.706 [1.823, 3.589] 0.449 0.271* 0.176* 0.173* Recurrent nightmares 2.380 [1.483, 3.276] 0.456 0.234* Insomnia (N= 438) Model 1 Constant 11.849 [11.394, 12.303] 0.231 Replicative nightmares 0.747 [0.505, 0.989] 0.123 0.279* 0.078* 0.076* Note. CI = confidence interval; PTSD = posttraumatic stress disorder. *p,.001. PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 11 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. and reactivity, and a weak but significant increase in PTSD symptoms and PTSD cri- terion C, symptoms of avoidance. Thefinding that a decreasing proportion of novel dreams was the best predictor for insomnia and those two PTSD criteria is surpris- ing and suggests that it may be the appearance of the range of nightmares and not a specific kind that best relates to insomnia and the PTSD linked symptoms of avoid- ance, arousal, and reactivity. Replicative nightmares are the other type of dreaming ratio found to be signifi- cant in the study. As the proportion of replicative nightmares increases, PTSD and the PTSD symptom criterion B, intrusion symptoms, significantly and moderately increase, whereas criterion D, symptoms of negative alterations in cognitions or mood, significantly and weakly increases. These relationships are in line with expectations but weaker than would be expected. It is also surprising that the pro- portion of recurrent nightmares demonstrates no significant relationship and is not Figure 1 Scatterplot of Raw Frequencies of Novel Dreams to PTSD and Insomnia Note. PTSD = posttraumatic stress disorder. 12WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. included in any of the models. That the two ends of the spectrum of dreaming, novel dreams and replicative nightmares, are the two types that proportionally are most significantly related to PTSD and insomnia generallyfits with current understand- ings (Lemyre et al., 2019;Levin & Nielsen, 2007). In the regressions, the variance of scores is not well explained by the predictor variables. In the context of the study, there are several reasons that may account for this. A likely reason is that in this cross-sectional study, participants were asked about dreaming over the past three nights. In a population in which both dreams and nightmares are occurring frequently, which this study demonstrates is happen- ing, it may be that the variety of dream experiences is difficult to capture in a short period of assessment, particularly coupled with the challenges that may occur in remembering them over the past three days. However, additional research is needed to make sense of thisfinding. The post hoc analyses provided slightly different results than the main predic- tions and were in line with past studies showing replicative and recurrent nightmares significantly, positively predictive of PTSD and replicative nightmares significantly, positively predictive of insomnia (Davis et al., 2007;de Dassel et al., 2018;Wagener, 2019). Thefindings from this study reinforce the existingfindings supporting rela- tionships between those variables, but this study also demonstrates there is another way to look at reports of dream frequencies. Particularly for evaluating theories of dreaming, it may be more important to understand how different dreaming types relate to the overall dreaming frequency. There are a few importantfindings from this study. One of the most interesting is how frequently types of nightmares and dreams co-occur. There are suggestions from literature that this sometimes happens (Rasmussen, 2007;Wagener, 2019), but tofind the majority of participants having elevated levels of PTSD and insomnia symptoms along with frequent co-occurring dreams and nightmares within a three- day time period is a novelfinding. It is also unexpected tofind that not only do novel dreams co-occur but remain relatively consistent in frequency. Thesefindings indi- cate an inner landscape that is more complex than is generally captured by research- ers looking at nightmares in relation to trauma. Furthermore, it challenges CTD and NMDD regarding how trauma affects dreaming. With NMDD, it prompts the question, if affect load and distress lead to the inability to generate novel dreaming, as proposed, how can novel dreams be co-occurring, at least within a three-day pe- riod, with replicative nightmares (Levin & Nielsen, 2007). With CTD, it challenges the conceptualization of a gradual transformation from replicative, to recurrent, to novel nightmares, and back to novel dreaming that CTD proposes occurs in reaction to trauma and as part of a healing process from trauma (Hartmann, 2011). With both CTD and NMDD, the general trends proposed by those theories are consistent with thefindings in this study, but the co-occurrences of dream types are not. It may be that the trends described in CTD and NMDD are what generally occurs after trauma, but in a population such as the one in this study, in which most participants are suffering from chronic, high levels of PTSD and insomnia, there is a different manifestation of dreaming. Regardless, the consistency of novel dreams for those experiencing trauma is a newfinding, so it needs to be reproduced in additional studies, but if it holds, it creates new questions related to the formation and function of dreams and nightmares. PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 13 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Clinical Implications There are clinical implications from this study, though the context of this study, which looked at survivors of IPV, the majority of whom had symptoms of PTSD and insomnia consistent with clinical significance, must be recognized. For counselors and psychotherapists, a primary implication is that the presence of replicative and recurrent nightmares should prompt an exploration of trauma history and PTSD and insomnia symptoms. Replicative nightmares in particular are strongly corre- lated with higher PTSD and insomnia scores. This is not a new implication as similar guidance stems from a variety of other studies (Davis et al., 2007;de Dassel et al., 2018;Pillar et al., 2000). A relatedfinding for clinical work is that if clients report experiencing novel dreams, it does not mean that replicative and recurrent nightmares are not co- occurring, as this study showed the consistent occurrence of novel dreams even with elevated PTSD levels and co-occurring replicative and recurrent nightmares. This is a new recommendation based on this study. It is not known if mental health professionals make assumptions about psychological health based on a report of a novel dream, but if any clinicians do make such assumptions, which seems reason- able based on the strong associations between nightmares and problematic mental health (Lancee & Schrijnemaekers, 2013;Lemyre et al., 2019;Levin & Nielsen, 2007;Swart et al., 2013) and the lack of studies suggesting that novel dreams and nightmares co-occur, this study indicates that the presence of dreams may not be a reliable indicator that nightmares are not present. Future Directions Future research should look to confirm thefindings of this study, as it both used a new proportional approach to understanding dream experience and found novel dreams remaining relatively consistent in frequency even as more nightmares appeared for those with higher PTSD and insomnia scores. Future longitudinal stud- ies would allow for a better evaluation of the relationship of novel dreams to total dreaming. It would be informative to observe the proportions of dream types over time, as those observations might better explain the variance in scores observed in the study and allow for better evaluation of trends in dreaming related to PTSD and insomnia recovery. The latter information would be beneficial for evaluating CTD and NMDD. One additional area of focus for future research is better differentiating and assessing dream types. The differentiation based on emotion is currently dichoto- mous (i.e., dreams or nightmares), which does not adequately account for the range of emotional experiences in dreaming making it difficult to assess a continuum of emotion as proposed in CTD and NMDD (Hartmann, 2011;Levin & Nielsen, 2007). Repetition suffers from a similar simplification that does not match the real- ities of dreaming. The categories of replicative, repeating, and novel, are useful, but afiner gradation, just as with emotion would be beneficial and allow for a better evaluation of whether it too is part of a continuum as suggested by CTD and NMDD (Hartmann, 2011;Levin & Nielsen, 2007). 14WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Limitations There are a few limitations to this study. It is a cross-sectional study, so it is impossible to know how the relationships change over time based on this study, and the results may not fully capture the range of dream types experienced by partici- pants. The online recruitment approach using a monetary inducement could have led to inaccurate responses, though the length of the survey, knowledge that the inducements were limited and would be provided at the end of the study by a draw- ing, and the ability of Qualtrics to prevent the same IP address from doing the sur- vey more than once likely reduced fraudulent survey completion. The sole use of online recruitment is another potential limitation, though the ubiquity of smart phones and other technology makes this limitation far less significant than in the past, as recent surveys suggest that even among the poor and elderly, a large propor- tion of the population has access to both the technology and the Internet and regu- larly use both (Perrin & Atske, 2021). There are additional limitations related to studying dreams. Dreams involve remembered experiences that may vary from the reality of what was experienced, so whether what was recorded actually reflects what was experienced is uncertain. The dream frequency assessment questions were generated for this study by the study author. The study author took a direct approach, asking for specific types of dreams and nightmares, but because it is a novel assessment, there is the potential for structural issues or wording to affect the generated responses. Finally, it must be acknowledged that there are cultural limita- tions to the study. The results were found among a largely white and female partici- pant population actively using social media in the United States. Therefore, the results may not be generalizable to other populations and locations. References American Psychiatric Association. 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Sleep and Biological Rhythms (2019) 17:447–454 https://doi.org/10.1007/s41105-019-00237-w ORIGINAL ARTICLE The in uential factor of narcolepsy on quality of life: compared to obstructive sleep apnea with somnolence or insomnia Mei Ling Song 1 · Keun Tae Kim 2 · Gholam K. Motamedi 3 · Yong Won Cho 2 Received: 28 October 2018 / Accepted: 14 August 2019 / Published online: 26 August 2019 © Japanese Society of Sleep Research 2019 Abstract Narcoleptics tend to have a low quality of life (QoL). Few studies have compared QoL in narcolepsy against other sleep disorders. The purpose of this study was to investigate QoL and its inuential factors in narcolepsy patients compared to obstructive sleep apnea (OSA) with somnolence and primary insomnia. We enrolled 63 narcoleptics (33 type 1, 30 type 2), 49 patients with OSA with somnolence, and 87 insomniacs. All patients were diagnosed through detailed clinical face-to-face interviews and polysomnography and had no other comorbid sleep disorders or medical diseases. All patients completed the Korean-version of the Short-Form 36-Item Health Survey (K-SF36) and a series of standard sleep-related questionnaires. The QoL of the narcolepsy group was comparable to the OSA with somnolence and insomnia groups. There was no signicant dierence between type 1 and type 2 narcolepsy on the total score of the K-SF36. However, factors that had the most impact on QoL included anxiety followed by depressive mood for narcoleptics, depressive mood followed by severity of insomnia for OSA with somnolence, and insomnia severity followed by depressive mood for insomniacs. Mood disturbances, mainly anxiety, aected QoL most in narcolepsy patients. Excessive daytime sleepiness and nocturnal sleep disturbance did not directly aect QoL of narcoleptics. To improve QoL in narcoleptics, proper management of anxiety should be considered as part of the treatment. Keywords Narcolepsy · Quality of life · Cataplexy · Excessive daytime sleepiness Introduction Narcolepsy is a neurological sleep disorder characterized by excessive daytime sleepiness (EDS) caused by loss of hypo- cretin (orexin) in postero-lateral hypothalamus. Narcolepsy is classied as narcolepsy with cataplexy (type 1, NT1), or without cataplexy (type 2, NT2) [1 ]. The prevalence of type 2 and type 1 narcolepsy lies between 0.16–0.66% and 0.025–0.05%, respectively [2 ]. While there is no reported prevalence for narcolepsy in the Korean adult population, in a study of 20,407 Korean adolescents the prevalence of narcolepsy with cataplexy in the younger age groups was determined as 0.015% [3 ]. Narcoleptics often experience more di culties in their social, vocational, and personal lives. Patients with narco- lepsy report higher rates of comorbid medical and/or psychi- atric problems, and often present with additional sleep disor – ders [4 , 5 ]. Thus, it can be understood that narcolepsy has a negative inuence on quality of life (QoL) [6 , 7 ]. However, the exact factors that adversely aect QoL in these patients are not well known. Excessive daytime sleepiness, a major symptom of narcolepsy with considerable effects on QoL, has been reported in 91% of narcoleptics [8 ]. In obstructive sleep apnea (OSA), EDS/somnolence is also commonly reported [ 9 ] with a negative inuence on QoL [10]. A previous study reported that the QoL in narcolepsy patients was lower than in OSA patients [11]; however, it did not exactly dierenti- ate between narcolepsy and OSA with somnolence (OSA- som), and to our knowledge no other study has compared QoL between narcolepsy and OSA-som (Korean version of Epworth sleepiness scale, K-ESS ≥ 10). Therefore, it would * Yong Won Cho [email protected] 1 College of Nursing, Daegu Health College, Daegu, South Korea 2 Department of Neurology, Dongsan Medical Center, Keimyung University School of Medicine, 56 Dalseong-ro, Jung-gu, Daegu 41931, South Korea 3 Department of Neurology, Georgetown University Hospital, Washington, DC, USA Vol.:(0123456789) 1 3 448 Sleep and Biological Rhythms (2019) 17:447–454 be valuable to compare QoL between narcolepsy and OSA- som, in addition to investigating the impact of EDS on QoL. Fatigue, poor memory, social or vocational dysfunction, and mood disturbances are common complaints in chronic insomnia, which are recognized to have a negative inuence on QoL [12, 13]. Despite di culty sleeping at night, insom- niacs do not exhibit heightened levels of EDS [14]. On the other hand, comparing dierent sleep-related symptoms in narcolepsy and insomnia, EDS has been established as the predominant symptom of narcolepsy. Therefore, given the eects of sleep disturbance on daytime activity and wors- ening the QoL, to compare narcolepsy and insomnia, we investigated the degree of sleep disturbance and its inuence on QoL. Cataplexy often happens without notice and can be harm- ful to patients. It commonly interferes with the patients’ social life. The lack of hypocretin is reported to be associ- ated with more EDS and wake intruding sleep in NT1 [15]. In fact, some studies have indicated the involvement of hypocretinergic system in emotional and psychiatric symp- toms such as anxiety [16] and depression [17]. In addition, cataplexy often happens in with the setting of emotional behaviors such as laughter. Despite some dierences in day – time functioning between NT1 and NT2, they both present with EDS. The purpose of this study was to investigate QoL in nar – colepsy focusing on its inuential factors. In particular, to control for sleep-related factors especially EDS, we com- pared QoL in patients with narcolepsy (both NT1 and NT2), OSA-som, and insomnia. Methods We retrospectively screened 148 patients with possible narcolepsy who had visited a tertiary sleep center between August 2011 and August 2016. All subjects had been eval- uated by a sleep specialist and had completed a series of standard sleep-related questionnaires. According to the International Classication of Sleep Disorders (ICSD) 3rd edition [1 ], narcolepsy was dened by a mean sleep onset latency of less than 8 min and two or more sleep onset REM periods (SOREMPs) during a standard multiple sleep latency test (MSLT). In the right clinical setting with cor – roborative ndings on the sleep studies, we do not routinely check the cerebrospinal uid hypocretin level. During the screening process, we excluded 48 patients who did not meet the diagnostic criteria for narcolepsy (13 patients with mean sleep onset latency of more than 8 min, and 35 patients with less than 2 SOREMs), 28 patients who did not provide data for QoL, and 9 patients with other comorbid medical diseases (Fig. 1). We successfully enrolled 63 narcoleptics. All of the patients were diagnosed with narcolepsy for the rst time and the data were collected before starting the treatment. The controls included 87 patients with insomnia, and 49 patients with OSA-som out of 165 total OSA patients. Of that total, 116 were OSA patients without somnolence. All subjects completed a PSG. Insomnia was diagnosed accord- ing to the ICSD 3 [1 ], and patients with OSA had respiratory disturbance index (RDI) score of 5 or higher [1 ]. Patients were categorized as OSA-som if their Korean version of Epworth Sleepiness Scale (K-ESS) score was ≥ 10 [ 18]. We excluded patients younger than 18 years, who did not do quality of life questionnaires, and who were comorbid with medical diseases, psychiatric disorders, or other sleep disor – ders such as restless legs syndrome and parasomnia. This study was approved by the institutional ethics com- mittee of the regional hospital. Measurements Quality of life was assessed using the Korean version of the Short-Form 36-Item Health Survey (K-SF36) [19]. K-SF36 is a common measure of health that is often used to deter – mine the cost-eectiveness of treatment. All of the subjects completed sleep- and psychiatric-related questionnaires including Korean versions of the Insomnia Severity Index (K-ISI) [20], the K-ESS [18], and the Hospital Anxiety and Depression Scale (HAS, HDS) [21]. Statistics analysis Data analysis was performed using SPSS version 25.0, and p < 0.05 was considered statistically signicant. We com – pared narcolepsy with OSA-som, and insomnia patients. The Chi-square test was used for dierences in gender, ANOVA was used to analyze age and BMI (Body Mass Index) data, Fig. 1 Narcolepsy patient screening ow chart 1 3 449 Sleep and Biological Rhythms (2019) 17:447–454 and Schee test for post hoc testing. ANCOVA was used to analyze K-ISI, K-ESS, and K-SF36 scores after adjusting for age and gender, and pair-wise comparisons were used for dierences found between the groups. Pearson correlation was used for analyzing the correlation between age, gender, BMI, K-SF36, K-ISI, K-ESS, HAS, HDS, and MSLT/RDI. Multiple linear regression was used to investigate the predic- tors of QoL. Results Demographic and clinical characteristics among narcoleptics, OSA‑som patients, and insomniacs We studied 63 narcoleptics (33 NT1 and 30 NT2), 49 OSA- som patients, and 87 insomniacs. Narcoleptics were signi- cantly younger than both OSA-som patients and insomniacs. Narcoleptics were more likely to be female than OSA-som patients (31.7% vs. 12.2%, respectively), but less likely to be female compared to insomniacs (31.7% vs. 63.2%, respectively). There was no signicant dierence in BMI among the groups (Table 1). Severity of insomnia was worse in narcoleptics compared to OSA-som patients; however, this dierence did not reach statistical signicance. Narcoleptics had signicantly less severe insomnia compared to insomniacs. The severity of EDS was signicantly higher in narcoleptics than OSA-som patients and insomniacs. The mean of RDI from narcolepsy patients and insomniacs was signicantly less than OSA- som patients, and the RDI score from all narcolepsy patients and insomniacs was less than 5. In terms of anxiety and depression, there were no signi- cant dierences among the three groups (Table 1). Comparing QoL of narcoleptics, OSA‑som patients, and insomniacs There were no signicant dierences in QoL between the three groups. However, the mental component of QoL and total QoL were signicantly higher in OSA patients than in insomniacs (Table 1). Table 1 Demographic and clinical characteristics between groups Adjusted age and gender ANCOVA were used for analysis K-ISI, K-ESS, HAS, HDS, K-SF36 K-ISI Korean version of Insomnia Severity Index, K-ESS Korean version of Epworth Sleepiness Scale, RDI Respiratory Disturbance Index, HAS Hospital Anxiety scale, HDS Hospital Depressive Scale, K-SF36 Korean version of 36-item short-form health survey, PCS physical component summary, MCS mental component summery a Narcolepsy patientsb OSA with somnolence patientsc Insomniacs Narcolepsy a (N = 63) OSA with somnolence b (N = 49) Insomnia c (N = 87) F/x 2 pSchee/pairwise Age (years) 27.03 ± 9.2939.20 ± 11.8347.25 ± 13.05 54.81< 0.001 a < b < c Gender (% female) 20 (31.7)6 (12.2)55 (63.2) 36.80< 0.001 BMI (kg/m 2) 24.80 ± 3.90 25.33 ± 2.4823.85 ± 12.46 0.510.600 Narcolepsy (% Type 1) 33 (52.4)–– –– Sleep measure K-ISI 12.75 ± 6.107511.27 ± 4.8919.39 ± 5.74 21.05< 0.001 a < c, b < c K-ESS 15.17 ± 4.2613.43 ± 2.575.06 ± 4.50 82.69< 0.001 a > b > c RDI 1.55 ± 2.5836.97 ± 26.400.50 ± 1.02 136.67< 0.001 b > a, b > c 5 ≤ RDI < 15 (%) 12 (24.5) 15 ≤ RDI < 30 (%) 14 (28.6) RDI ≥ 30 (%) 23 (46.9) HAS 6.78 ± 3.215.98 ± 3.887.43 ± 4.70 2.760.065 HDS 8.73 ± 3.707.90 ± 3.358.54 ± 4.09 0.700.497 K-SF36 PCS 66.83 ± 16.5670.12 ± 13.9262.24 ± 17.90 2.820.062 MCS 61.25 ± 15.9967.65 ± 15.6556.91 ± 20.39 4.040.019 b > c SF36 total 67.41 ± 15.7872.84 ± 14.1762.69 ± 19.27 4.520.012 b > c 1 3 450 Sleep and Biological Rhythms (2019) 17:447–454 Correlation among severity of insomnia, EDS, anxiety, depression and QoL in each group of narcoleptics, OSA‑som, and insomniacs In narcoleptic patients, EDS showed significant posi- tive correlation with severity of insomnia (r = 0.366), age (r = 0.359), BMI (r = 0.353), and anxiety (r = 0.316). Severity of insomnia showed signicant positive corre- lation with anxiety (r = 0.285), depression(r = 0.336), and age (r = 0.446). The QoL showed signicant nega- tive correlation with anxiety (r = − 0.631) and depression ( r = − 0.501), however QoL showed no signicant correla- tion with EDS and severity of insomnia (Table 2; Fig. 2). Table 2 Correlation among severity of insomnia, EDS, anxiety, depression and QoL in each group of narcoleptics, OSA with somnolence, and insomniacs BMI Body Mass Index, RDI Respiratory Disturbance Index, K-SF36 Korean version of 36-item short-form health survey, K-ISI Korean version of Insomnia Severity Index, K-ESS Korean version of Epworth Sleepiness Scale, HAS Hospital Anxiety Scale, HDS Hospital Depression Scale, MSLT multiple sleep latency test *Correlation is signicant at the 0.05 level (two-tailed) **Correlation is signicant at the 0.01 level (two-tailed) Age GenderBMIK-ISI K-ESSHAS HDSMSLT/RDI SF36 total Narcoleptics Age 1 Gender − 0.1171 BMI 0.256*− 0.472**1 K-ISI 0.446**− 0.1630.2441 K-ESS 0.359**− 0.0780.353**0.366** 1 HAS − 0.0830.0800.0930.285* 0.316*1 HDS 0.158− 0.0710.2250.336** 0.1690.408** 1 MSLT − 0.0700.140− 0.165− 0.039 − 0.126− 0.077 − 0.0721 K-SF36 total 0.113− 0.038− 0.107− 0.226 − 0.122− 0.631** − 0.501**0.108 1 OSA with somnolence Age 1 Gender 0.557**1 BMI − 0.054− 0.0401 K-ISI 0.0110.275− 0.0651 K-ESS 0.0960.1330.0880.270 1 HAS 0.0200.067− 0.131− 0.131 − 0.1471 HDS − 0.1240.1140.069− 0.114 0.0180.601** 1 RDI − 0.056− 0.007− 0.0240.106 0.1340.103 0.2641 K-SF36 total 0.041− 0.0580.075− 0.255 0.003− 0.445** − 0.571**− 0.185 1 Insomniacs Age 1 Gender 0.1271 BMI − 0.0370.0321 K-ISI 0.1400.1610.1231 K-ESS − 0.317**− 0.264*− 0.110− 0.090 1 HAS − 0.167− 0.124− 0.1400.310** 0.1581 HDS − 0.013− 0.033− 0.0110.253* 0.0720.646** 1 K-SF36 total 0.171− 0.129− 0.126− 0.540** − 0.030− 0.530** − 0.542** 1 Fig. 2 Correlation in narcoleptics 1 3 451 Sleep and Biological Rhythms (2019) 17:447–454 In OSA-som patients, there were significant positive correlation between anxiety and depression (r = 0.601). There was a negative correlation between QoL and anxiety ( r = − 0.445), and depression (r = − 0.571). There was no signicant correlation between EDS and anxiety, depression, severity of insomnia. Severity of insomnia also showed no signicant correlation with anxiety and depression (Table 2, Fig. 3). In insomniacs, there was signicant positive correlation between severity of insomnia and anxiety (r = 0.310) and depression (r = 0.253). There was a signicant negative cor – relation between severity of insomnia and QoL (r = − 0.540). There were signicant negative correlation between QoL and anxiety (r = − 0.530), depression (r = − 0.542). There was no signicant correlation between EDS and any of other variables (Table 2, Fig. 4). Predictors of QoL in narcolepsy, OSA‑som, and insomnia There was no signicant dierence between NT1 and NT2 in on the total score of the K-SF36. In order to analyze the predictors of QoL in each group, we considered age, gen – der, BMI, severity of insomnia, EDS, anxiety, and depressed mood as independent variables. In narcoleptics, anxiety and depressive mood signi- cantly aected QoL, however, anxiety (β = − 0.51) had a greater impact on the total QoL than depressive mood did ( β = − 0.29) (Table 3). Fig. 3 Correlation in OSA with somnolence Fig. 4 Correlation in insomniacs Table 3 Predictors of total QoL in narcoleptics, OSA with somnolence, and insomniacs Age, gender, BMI, K-ISI, K-ESS, HAS, and HDS as independent variables, for narcolepsy patients added MSLT, for OSA patients added RDI as independent variable Stepwise method was used for analysis BMI Body Mass Index, QoL quality of life, K-SF36 Korean version of 36-item short-form health survey, PCS physical component summary, MCS mental component summery, K-ISI Korean version of Insomnia Severity Index, K-ESS Korean version of Epworth Sleepiness Scale, HAS Hospital Anxiety Scale, HDS Hospital Depression Scale, MSLT multiple sleep latency test, RDI Respiratory Disturbance Index BS.Eβ R 2 change tp VIF Narcoleptics Constant 95.334.22 22.54< 0.001 HAS − 2.510.50− 0.51 0.399 − 4.97< 0.001 1.20 HDS − 1.240.43− 0.29 0.071 − 2.830.006 1.20 R 2 = 0.470, F = 26.58, p < 0.001 OSA with somnolence Constant 102.005.51 18.50< 0.001 HDS − 2.350.43− 0.60 0.326 − 5.420.001 1.20 K-ISI − 0.930.32− 0.32 0.104 − 2.890.006 1.20 R 2 = 0.430, F = 17.32, p < 0.001 Insomniacs Constant 107.265.66 18.94< 0.001 HDS − 1.440.48− 0.30 0.294 − 2.990.004 1.72 K-ISI − 1.330.27− 0.39 0.174 − 4.82< 0.001 1.11 Age 0.350.110.23 0.052 0.2990.004 1.02 R 2 = 0.519, F = 29.91, p < 0.001 1 3 452 Sleep and Biological Rhythms (2019) 17:447–454 In OSA-som patients, QoL was signicantly aected by depressive mood and severity of insomnia, and the depres- sive mood ( β = − 0.60) had greater impact on QoL than insomnia (β = − 0.32) did (Table 3). In insomniacs, QoL were signicantly aected by depres – sive mood, the severity of insomnia, and age but the sever – ity of insomnia ( β = − 0.39) had a greater impact on QoL than depressive mood (β = − 0.30) and age (β = 0.23) did (Table 3). Discussion The mean age of diagnosis for patients with narcolepsy was 27.03 (± 9.29) which was less than that of OSA patients, and insomniacs. This is consistent with the fact that narcolepsy starts at an early age [22– 24] while OSA and insomnia often start later in life. We found gender dierences in narcolepsy i.e., predominantly aecting the male. This is in contrast to most reports indicating no signicant gender dierence in narcolepsy [6 , 11, 25], however it is supported by a previous study reporting 78% male dominance in narcolepsy [26]. We found gender dierences in OSA and insomnia both consist- ent with previous studies reporting more than 70% of OSA patients being males [27, 28], and females being 1.6 times more likely than men to develop insomnia [28– 30]. Narcoleptics showed more severe insomnia than seen in OSA patients although this dierence did not reach statisti- cal signicance. Compared to insomniacs, both narcoleptics and OSA patients showed signicantly less severe insom- nia than insomniacs, however the mean score of severity of insomnia in narcoleptics (12.75) was higher than the cut o threshold (7) indicating subthreshold insomnia [20]. Previ- ous studies have reported that improving nocturnal sleep in narcolepsy would increase daytime alertness [31, 32]. There- fore, nocturnal sleep disturbance in patients with narcolepsy may well impact their QoL, although the eect may not be as severe as in insomniacs. This study showed that narcoleptics have signicantly more EDS than patients with OSA-som and insomniacs do. Consistent with previous reports, we found EDS a common complaint in sleep disordered patients [33], however, we identied EDS as a symptom associated with poor QoL and worse psychological parameters in narcoleptics [34, 35]. For further investigation, we compared NT1 and NT2. Fifty-two percent of narcoleptics in our study had NT1. We found that the type of narcolepsy did not have a signicant impact on QoL. It has been reported that narcolepsy symp- tomatology (narcolepsy with cataplexy-like symptoms, hyp- nagogic or hypnopompic hallucination, and sleep paralysis) is associated with poor QoL, symptoms of depression, and anxiety [34], yet our ndings indicated that cataplexy-like symptoms have no signicant eect on QoL. Previous studies have also associated QoL of narcoleptics with the degree of EDS and psychological variables such as depressive mood [7 , 26, 36]. We found that both anxiety and depression have a negative impact on QoL in narcolep- tics, but the total QoL were more aected by anxiety than depressive mood. The EDS did not have a signicant impact on QoL, contrary to other studies reporting a signicant association between EDS and psychiatric symptoms such as depression and anxiety [34, 37]. Although EDS did not impact QoL, it showed signicant correlation with anxiety. Also, nocturnal sleep disturbance was not a factor impacting QoL, nocturnal sleep disturbance showed signicant correla- tion with anxiety, depression, and EDS. It is possible that QoL is indirectly aected by both EDS and nocturnal sleep disturbance through psychiatric factors as a downstream eect. Overall, we found anxiety the most impactful fac- tor in QoL of narcoleptics. A previous study reported more that anxiety than depressive mood in narcolepsy patients. Additionally, many patients reported noticeable impairment in daily functioning due to anxiety and mood problems. We suggest that physicians consider this notion along with the sleep symptoms [38]. There were some dierences between narcolepsy patients and those with OSA-som, as well as between narcolepsy and insomnia patients. In both OSA-som and insomnia groups, the QoL was aected by severity of insomnia and depres- sive mood, however EDS did not correlate with severity of insomnia or any psychiatric factors. Some studies have reported coexisting with OSA as a contributing factor to EDS with negative inuence on QoL [39]. However, our ndings did not support these reports. In our results, the OSA-som was dened by K-ESS scale, so it should inuence the correlation between EDS and QoL. On the other hand, other study has reported a strong correlation between depres- sive mood and low QoL, although EDS might have a small eect on QoL [22]. That ndings may partially support our results. In OSA-som patients, the severity of insomnia did not show a signicant correlation with QoL, however in fac- tor analysis, controlling the eect of depression, severity of insomnia was included as one of the eect factors, and depressive mood seemed to aect QoL the most. Previous studies have reported that men with comorbid OSA and insomnia have greater prevalence and severity of depression, and lower QoL [40, 41] which is in line with our results. In addition, other studies reported that anxiety had strong cor – relation with depression, and was more common in female patients [42]. Our study only included six females, so we were unable to conrm these particular correlations. Further research is needed to determine the signicance of anxiety as an inuence on QoL. In current study the RDI did not show any correlation with QoL. This was supported by pre- vious studies which found no correlation between AHI and QoL and concluded that the eect of OSA on QoL cannot 1 3 453 Sleep and Biological Rhythms (2019) 17:447–454 be explored with PSG or by utilizing commonly used QoL questionnaires [10, 43]. In insomniacs, there were signicant correlations among severity of insomnia, depressive mood, anxiety, and QoL. The factors impacting QoL were the severity of insomnia and depressive mood, with the severity of insomnia having the higher impact (β = − 0.39, vs β = − 0.30). In patients with insomnia, the severity of insomnia has been associated with psychiatric symptoms [13, 44], and QoL [45]. Our ndings could be supported by these data. In insomniacs, EDS did not show any correlation with severity of insomnia, psychi- atric factors, and QoL. In contrast to our results, some of previous studies have reported that EDS could be considered secondary to chronic insomnia and depression [46]. Also, EDS would correlate with lower QoL and depressive mood [ 47, 48]. The signicance of this study was to compare the factors inuencing the QoL of the narcolepsy group against the two groups each consisting of OSA-som and insomnia, in addi- tion to comparing NT1 and NT2 with each other. Thus, we investigated the dierence in the degrees of inuence fac- tors, such as EDS, sleep disturbance, cataplexy symptoms, and mood disturbance (depression and anxiety) on QoL. There were several limitations of our study. The rst is its retrospective design in addition to the data having been obtained from a pool of patients referred to a tertiary sleep center. We could not control nor include other variables that may have an eect on QoL. In addition, the diagno- ses of NT1 were merely based on patient reports with no CSF hypocretin (orexin) levels checked. Third, the OSA patients were rst diagnosed with OSA using PSG. A patient interview and sleep questionnaire were also given, which eliminated the need for further testing for comorbidities such as NT2, idiopathic hypersomnia, or insu cient sleep syndrome. Also, although this is a single center study, the results are not signicantly dierent from those reported by multicenter studies. In conclusion, the QoL of the total narcolepsy group was comparable to the OSA-som and insomnia groups. There are dierent inuential factors involved in QoL of narcolep- tics, OSA-som patients, and insomniacs. The EDS did not directly aect QoL, in fact, the most inuential factor in QoL in narcoleptics was anxiety while EDS showed a signicant correlation with anxiety. In OSA-som patients, the most sig- nicant factor impacting QoL was depressive mood, while for the insomniacs, it was severity of insomnia. Although, there were some dierences in the degree of eect, ulti- mately the QoL was aected most by psychiatric variables, directly or indirectly. Therefore, to improve the QoL in these patients, pharmacological or non-pharmacological psychiatric treatments should be considered. In particular, patients with narcolepsy, should prioritize anxiety manage- ment for the purpose of improving QoL. It should be noted that anxiety in narcolepsy patients may also be reduced as a result of narcolepsy treatment. Further research is needed to evaluate the eect of narcolepsy treatment on anxiety levels. Funding No funding was received in this study. Compliance with ethical standards Conflict of interest None of the authors have potential conicts of in- terest to be disclosed. Ethical standards This study was approved by the institutional review board of a regional university hospital and patient consent was exempt due to the retrospective nature of the study (#2017-02-009). 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Diagnosing Obstructive Sleep Apnea in a Residential Treatment Program for Veterans With Substance Use Disorder and PTSD Peter J. Colvonen 1, 2 , Guadalupe L. Rivera 1, Laura D. Straus 4, 5 , Jae E. Park 1, 2 , Moira Haller 1, 2 , Sonya B. Norman 1, 3 , and Sonia Ancoli-Israel 2 1VA San Diego Healthcare System, San Diego, California, United States 2University of California, San Diego 3National Center for PTSD, White River Junction, Vermont, United States4Department of Psychiatry, University of California, San Francisco 5San Francisco VA Healthcare System, San Francisco, California, United States Background:Obstructive sleep apnea (OSA) is often comorbid with both substance use disorders (SUD) and posttraumatic stress disorder (PTSD), yet frequently goes undiagnosed and untreated. We present data on the feasibility and acceptability of objective OSA diagnosis procedures,findings on OSA prevalence, and the relationship between OSA and baseline SUD/PTSD symptoms among veterans in residential treatment for comorbid PTSD/SUD.Methods:Participants were 47 veterans admitted to residential PTSD/SUD treatment. Participants completed questionnaires assessing PTSD and sleep symptoms, andfilled out a sleep diary for seven days. Apnea-hypopnea index (AHI) was recorded using the overnight Home Sleep Apnea test (HSAT; OSA was diagnosed with AHI$5).Results:Objective OSA diagnostic testing was successfully completed in 95.7% of participants. Of the 45 veterans who went through HSAT, 46.7% had no OSA, 35.6% received a new OSA diagnosis, and 8.9% were previ- ously diagnosed with OSA and were using positive airway pressure treatment (PAP); an additional 8.9% were previously diagnosed with OSA, reconfirmed with the HSAT, but were not using PAP. One hundred percent of respondents during follow-up deemed the testing protocol’s usefulness as“Good”or “Excellent.”Conclusion:OSA diagnostic testing on the residential unit was feasible and acceptable by participants and was effective in diagnosing OSA. OSA testing should be considered for everyone enter- ing a SUD and PTSD residential unit. Clinical Impact Statement Obstructive sleep apnea (OSA) is very often comorbid with both posttraumatic stress disorder (PTSD) and substance use disorders (SUD). Unfortunately, due to the limitations of self-report OSA screeners and atypical presentation of OSA in individuals with SUD/PTSD, OSA often goes undiag- nosed for individuals with SUD/PTSD. Our study found that OSA diagnostic testing was feasible and acceptable to participants in a residential treatment program for SUD/PTSD, and effective in diagnosing OSA. Diagnosing OSA on a residential unit for SUD/PTSD is a necessaryfirst step to treating OSA and may help improve long-term outcomes for individuals with SUD/PTSD. Keywords:veteran, PTSD, SUD, OSA, CPAP This article was published Online First September 2, 2021. Peter J. Colvonen https://orcid.org/0000-0003-0222-8781 Jae E. Park https://orcid.org/0000-0002-0197-0012 Sonya B. Norman https://orcid.org/0000-0002-4751-1882 This study was funded by Veterans Affairs RR&D CDA Grant #1lK2Rx002120-01 to Peter J. Colvonen and Veterans Affairs CSR&D merit Grant NURA-011-11F to Sonya B. Norman. This material is the result of work supported by a UCSD Academic Senate Pilot Grant. Peter J.Colvonen is partly funded by Veterans Affairs RR&D CDA Grant 1lK2Rx002120-01. The views expressed in this article are those of the authors only and do not reflect the official policy or position of the institutions with which the authors are affiliated, the Department of Veterans Affairs, nor the United States Government. None of the authors have any competingfinancial interests to disclose. Correspondence concerning this article should be addressed to Peter J. Colvonen, VA San Diego Healthcare System, 3350 La Jolla Village Drive (116B), San Diego, CA 92161, United States. Email:[email protected] 178 Psychological Trauma: Theory, Research, Practice, and Policy In the public domain2022, Vol. 14, No. 2, 178–185 ISSN: 1942-9681https://doi.org/10.1037/tra0001066 Substance use disorders (SUD) and posttraumatic stress disor- der (PTSD) are highly comorbid (Kessler et al., 2005;Najavits et al., 2010), and this comorbidity is associated with worse treatment outcomes for both disorders, greater risk of homelessness, increased disease burden, higher suicidal ideation and attempted suicide (Norman et al., 2018), and greater functional disability than having a single disorder (Calabrese et al., 2011;Driessen et al., 2008;Norman et al., 2016;Possemato et al., 2010). In addi- tion, both Veterans Affairs (VA) and community clinicians report significant challenges in treating comorbid SUD/PTSD individuals due to higher drop-out rates, more severe symptoms, and lower motivation (Najavits et al., 2010). Residential treatment is an appropriate level of care for individuals with severe PTSD and/or SUD (Haller et al., 2019) with upward of 40% of individuals seek- ing SUD treatment receiving residential care at some point (Stah- ler et al., 2016;Substance Abuse and Mental Health Services Administration, 2008). A residential setting offers an array of inte- grated treatment options to patients at a critical time in recovery and may be an optimal place to diagnose and treat comorbid disor- ders that can negatively affect both SUD and PTSD outcomes such as obstructive sleep apnea (OSA). Unfortunately, OSA diag- nostic testing is not a part of standard care in PTSD, SUD, or resi- dential treatment. Our study presents data on the feasibility and acceptability of implementing objective OSA diagnostic testing on a residential SUD treatment program for veterans with PTSD. Sleep disordered breathing is a spectrum (Schwab et al., 1998) ranging from mild upper airway resistance (e.g., snoring) to severe OSA. OSA is associated with sleep fragmentation and is defined by repeated episodes of apneas (pauses in breathing) and hypo- pneas (shallow breathing) with decreases in blood oxygenation during sleep. The apnea-hypopnea index (AHI) is derived by cal- culating the number of apneas and hypopneas per hour of sleep, and is the most commonly used metric of OSA severity, with mild OSA starting at AHI$5. OSA in veterans is associated with neu- rocognitive decline, hypertension, increased cardiovascular mor- tality, stroke, heart attacks, andfinancial burden on the health care system (Jennum & Kjellberg, 2011;Redline et al., 2010;Young et al., 2008). Furthermore, OSA is associated with more depression, anxiety, PTSD, SUD, psychosis, suicidal ideation, bipolar disor- der, and dementia compared to veterans without OSA (Sharafkha- neh et al., 2005). A systematic review of OSA prevalence in the general popula- tion found OSA ranged from 9% to 38%, and OSA risk increased with age and higher body mass index (BMI;Senaratna et al., 2017). Rates of OSA are significantly higher among veterans, with studies indicating diagnostic rates ranging between 67% to 83% (Krakow et al., 2006;Lettieri et al., 2016;Yesavage et al., 2012). Furthermore, both SUD and PTSD increase risk of OSA. A meta- analysis of veterans with PTSD found OSA prevalence was 75.7% (AHI$5;Zhang et al., 2017). Among individuals with any SUD, 53.3% were screened as being high risk for OSA (Mahfoud et al., 2009), with increased substance use severity increasing risk of OSA (Rose et al., 2014). While it is not entirely clear why veterans with PTSD present with higher rates of OSA compared to nonveterans without PTSD (Colvonen et al., 2015), there is convincing evidence that long- term alcohol ingestion and opioid use are important factors in pathogenesis of OSA (Le Bon et al., 1997;Vitiello et al., 1990; Wang & Teichtahl, 2007). For example, even after a single drink,normal sleepers can develop snoring and even exhibit breathing events resulting in oxygen desaturations (Block & Hellard, 1987). Alcohol relaxes upper airway dilator muscles, which increases air- way obstruction and increases nasal and pharyngeal resistance (Scanlan et al., 2000;Young et al., 2002) and prolongs the time required to arouse or awaken after an apnea occurs (Dawson et al., 1993;Robinson et al., 1985). Even during abstinence, individuals with SUD are more likely than controls to have OSA (Le Bon et al., 1997;Mamdani et al., 1989;Robinson et al., 1985). Research has demonstrated the detrimental impact of OSA on both SUD and PTSD outcomes. A retrospective study of veterans who had completed cognitive processing therapy, an evidence- based treatment for PTSD, found that those with untreated OSA (n= 69) showed less PTSD symptom improvement than those without OSA(N= 276;Mesa et al., 2017). However, those with OSA treated with positive airway pressure (PAP) showed more improvement in PTSD symptoms than those who were not treated (Reist et al., 2017). Both studies suggest that OSA screening/diag- nostic testing and treatment should be part of thefirst-line treat- ment for individuals with PTSD. There have been no studies examining the effect of untreated or treated OSA on relapse. However, there is circumstantial evidence that OSA may influence relapse rates. First, OSA is strongly linked to fragmented sleep (Antic et al., 2011), and it has been shown that disrupted sleep architecture predicts relapse among individuals abstinent from alcohol (Brower et al., 2001) and other substances (e.g., opioids and methamphetamines;Angarita et al., 2016). Second, OSA is linked with other factors involved in relapse, including deficits in most aspects of executive function- ing, decreased processing speed, increased perseverative responses or behaviors, impulsivity, and difficulty with problem solving (Gagnon et al., 2014). Finally, untreated OSA is linked to lower sleep efficiency (Williams et al., 2015), which is associated with more frequent and larger moodfluctuations (El-Ad & Lavie, 2005), thus potentially placing SUD patients further at risk for relapse (Brower, 2003). Studies are needed to clarify how OSA may influence relapse rates. Despite the detrimental effects of untreated OSA, it continues to be undiagnosed and untreated in many veterans, with estimates of 80% to 90% of veterans with OSA remaining undiagnosed (Alexander et al., 2016). There are two reasons for this: First, the symptoms of OSA (e.g., daytime fatigue, poor concentration, trou- ble sleeping, irritability) are often mistaken for the“primary disor- der”(e.g., SUD or PTSD), and thus OSA is not even considered as a contributor (Colvonen, Straus, et al., 2018). Second, there is sub- stantial evidence that OSA is increasing in younger veterans with co-occurring mental health disorders who do not have the classic risk factors (e.g., older age, overweight or obese per BMI), so OSA becomes difficult to identify (Colvonen et al., 2015;Rezaei- talab et al., 2018;Williams et al., 2015 ). As such, self-report OSA screening questionnaires, like the STOP-BANG or Berlin, that rely heavily on age, blood pressure, and BMI, are shown to be poor predictors of OSA in all veterans (Kunisaki et al., 2014; McMahon et al., 2017) as well as specifically among veterans with PTSD (Lyons et al., 2021). This suggests the need for objective OSA diagnostic testing among veterans. The literature suggests that residential treatment is effective in treating mental health disorders (Zhang et al., 2003) and is the appropriate level of care for individuals with severe SUD or PTSD DIAGNOSING OSA AMONG VETERANS WITH SUD AND PTSD 179 (Haller et al., 2019). More information is needed about specific programmatic elements that could increase effective outcomes and maximize successful long-term continued care (Proctor & Hersch- man, 2014). Due to the stable environment and frequent contact between the treatment team and the patient, the residential setting may be a more effective environment than outpatient settings for diagnosing and treating co-occurring OSA (Colvonen, Ellison, et al., 2018). PAP is the gold-standard treatment for OSA, with meta-analytic reports showing decreased sleep fragmentation and improvement in daytime sleepiness and functioning across a host of domains (Patil et al., 2019). Meta-analyses show significant decreases in apnea/hypopneas with PAP use with very large effect sizes (Schwartz et al., 2018). Increasing accessibility to evidence- based care for OSA in a residential setting may be a critical path- way for treating OSA and thereby potentially improving SUD/ PTSD treatment outcomes. However, it is unclear whether objec- tive testing of OSA, a necessaryfirst step to treatment, would be feasible on a residential unit for veterans with SUD and PTSD. Our study examined the feasibility and acceptability of objec- tive OSA diagnostic testing in a residential treatment unit for vet- erans with SUD and PTSD. We presentfindings on OSA prevalence and the relation between OSA and SUD/PTSD symp- toms. We hypothesized that objective OSA testing would be feasi- ble and acceptable. We also hypothesized that veterans with untreated OSA would have more severe SUD and PTSD symp- toms than those without OSA or with treated OSA. Finally, we make suggestions as to how residential units can implement OSA diagnostic testing and integrate PAP treatment. Methods Program Description The study took place in the Substance Abuse Residential Reha- bilitation Treatment Program (SARRTP) at the VA San Diego Health care System (VASDHS), a 14-bed residential substance use treatment program that also offers PTSD treatment for veter- ans with comorbid SUD and PTSD. The treatment team consisted of a clinical psychologist, psychiatrist, addiction therapists, nurs- ing staff, and social workers. The program was 28 to 35 days in duration (seven-day extensions were offered to veterans engaging in intensive individual PTSD treat- ment). Unit programming consists of cognitive–behavioral therapy groups for treating SUD, introducing new skills (e.g., anger manage- ment), engaging in experientially based activities (e.g., mindfulness/ relaxation), and other recovery-oriented programming (e.g., living skills, job skills). Patients diagnosed with PTSD related to any trauma type are offered services on the PTSD track and receive psy- choeducation about PTSD and the interplay of SUD and PTSD, attend a cognitive restructuring group where PTSD-related beliefs are addressed, and take part in an in-vivo group where they practice group exposures to commonly avoided situations (e.g., sitting in a crowded waiting room). Some veterans are offered intensive individ- ual evidence-based PTSD treatment three times a week. Participants All veterans participating in the PTSD track on the SARRTP unit at the VASDHS were offered participation in this study. Theonly exclusion criterion was unmanaged symptoms of psychosis, based on the discretion of the PTSD track clinical psychologist. Recruitment occurred between February 2019 and March 2020. Of the 60 veterans admitted to the unit, 47 veterans (78%) consented. Of the 47 veterans who consented, 2 veterans stated they did not want to be a part of the study after signing the consent. Data are presented on the remaining 45 veterans whofilled out question- naires and wore the OSA testing equipment. SeeTable 1for demographics. Procedures All research was approved by the institutional review board at the VASDHS. Veterans admitted onto the PTSD track on SARRTP were informed about the study from their SARRTP pro- vider during a one-on-one treatment planning session. Participants who expressed interest met with a study coordinator to learn more about OSA diagnostic testing procedures and were given the op- portunity to ask questions. Veterans who gave written consent to participate were given a home sleep apnea test (HSAT) overnight portable monitor for the diagnosis of OSA. We used the NOX T3 for our HSAT. Participants alsofilled out a daily sleep diary for seven days, and self-report measures (PTSD Checklist, Substance Use Inventory, Alcohol Use Disorder Identification Test, Client Table 1 Demographic and Baseline Characteristics (N = 45) Demographic variable Total% / M (SD) Age 42.9 (10.4) Sex Men 88.6% Women 11.4% Marital status Never married 22.7% Married 22.7% Divorced 47.7% Separated 4.5% Remarried 2.3% Substances used Alcohol 68.6% Marijuana 54.3% Sedatives/tranquilizers 15.2% Cocaine/crack 14.8% Opiates 17.1% IV opiate use 6.5% Service/branch Army 34.1% Navy 25.0% Marines 36.4% Reserves/National Guard 4.5% Ethnicity Hispanic 27.3% Non-Hispanic 72.7% Race White 72.7% Black 13.6% Bi/multi-racial 13.6% Pacific Islander/Asian 0% American Indian/Alaskan 0% Other 0% Height (inches) 69.2 (4.4) Weight (lbs) 180.8 (33.0) 180 COLVONEN ET AL. Satisfaction Questionnaire, demographics, Insomnia Severity Index, Epworth Sleepiness Scale, and the Pre-Sleep Arousal Scale). Participants were compensated $20. OSA diagnostic testing procedures were adapted with the help of doctors and staff on the unit to minimize patient burden and dis- ruption of current SARRTP procedures. All consenting and HSAT set-up were done at a time of day when no SARRTP classes were being held. Participants met with a study coordinator to set up and review procedures for the HSAT. All straps and nose cannulas were adjusted and prepared with study staff prior to the overnight testing, and equipment was put at veterans’bedside table. Medical tape was provided to keep the nose cannula andfinger clip in place. A pamphlet was given to participants with a step-by-step guide for setting up HSAT equipment. The HSAT was scored and reviewed by study staff using the American Academy of Medicine scoring rules (3% oxygen desaturation). Any participant with AHI $5 was asked if they wanted a referral to the Pulmonary Sleep Medicine clinic. If the participant consented to referral, HSAT summary data were sent to Sleep Medicine for review and possible PAP treatment. Measures OSA Diagnosis OSA was diagnosed using an HSAT portable recorder sleep monitoring systems. The HSAT AHI per hour output has anr=.93 when comparing the gold standard polysomnography (Cairns et al., 2014) and is approved for diagnosis in the American Academy of Sleep Medicine (Kapur et al., 2017). We used the NOX T3 HSAT, which has a simple monitor hook-up that the patients can use on their own with rip cords around the chest to measure breathing effort, a nose cannula to measure airflow and pauses in breathing, and afinger clip to measure oxygen desaturation. For individuals wearing a PAP device, the NOX T3 HSAT attaches to the PAP de- vice to capture residual AHI. All recorders were used for one night while on the SARRTP unit. An AHI$5 is considered to be mild, with those$15 being deemed moderate, and those$30 being severe. Historical OSA diagnosis was also retrieved from medical records to see newly diagnosed compared to previous diagnosis. Insomnia The Insomnia Severity Index (ISI;Morin et al., 2011)isa widely used measure of insomnia with well-established reliability and validity. The ISI consists of 7 items, three of which assess se- verity of insomnia (i.e., degree of difficulty falling asleep, staying asleep, and waking too early). The remaining questions tap satis- faction with sleep pattern, effect of sleep on daytime and social functioning, and concern about current sleep difficulties. Scores range from 0 (no clinically significant insomnia) to 28 (severe clinical insomnia), with a cut-off of 11 suggesting a diagnosis of insomnia (Morin et al., 2011). Daytime Sleepiness The Epworth Sleepiness Scale (ESS;Johns, 1991) is a validated 8-item questionnaire measuring daytime sleepiness. The questions ask individuals how likely they are to fall asleep, in eight different situations, on a scale of 0 to 3 (Would never dozetoHigh chance of dozing). Scores arefirst totaled, and higher scores indicatehigher severity of daytime sleepiness, with a cut-off of 10 suggest- ing clinically significant daytime sleepiness. Pre-Sleep Arousal The Pre-Sleep Arousal Scale (PSAS;Nicassio et al., 1985)rates the intensity of somatic (8 items) and cognitive (8 items) manifesta- tions of arousal prior to sleep. The PSAS shows strong internal con- sistency and reliability. The PSAS is a 16-item self-administered measure in which participants rate the intensity (1not at allto 5 extremely) of experienced arousal for somatic and cognitive sub- scales. Higher scores indicate higher intensities of pre-sleep arousal. Daily Sleep Diary Veterans completed a daily sleep diary at baseline and one week prior to discharge from the unit. Veteransfilled out daily in- formation on bedtime, sleep latency, number and duration of awakenings, wake time, total time in bed, sleep quality, and night- mares. Researchers then calculated two variables (total sleep time and sleep efficiency) based on participant daily entries. The pri- mary outcome measure used for this study was sleep efficiency, defined as the percent time spent sleeping given the number of hours in bed. PTSD Severity The PTSD Checklist (Weathers et al., 2013) is a 20-item self- report measure of PTSD symptoms with good psychometric prop- erties. The measure maps directly ontoDSM-Vdiagnostic criteria. Substance Use The Substance Use Inventory asks about the participant’s use of various substances, including alcohol, cocaine, heroin, marijuana, sedatives, PCP, stimulants, and hallucinogens, in the past 30 days, prior to SARRTP intake. The frequency, amount, and administra- tion route (smoked, oral, injected) were also assessed, along with questions about cravings and urges to use. Alcohol Use The Alcohol Use Disorders Identification test (Saunders, 1989) is a 10-item screening tool assessing alcohol consumption, drink- ing behaviors, and alcohol-related problems such as dependence or experience of alcohol-related harm in the month before SARRTP admission. Scores above 8 are considered hazardous or harmful alcohol use, while scores above 15 indicate high likeli- hood of alcohol dependence. Cannabis Use The Cannabis Use Disorder Identification Test—Revised (Ad- amson et al., 2010) is an 8-item self-report measuring marijuana use (e.g., yes/no) and behaviors regarding the use of marijuana. Scores above 8 are considered hazardous or harmful cannabis use, while scores above 12 indicate high likelihood of cannabis use disorder. Feasibility OSA testing feasibility was assessed via number of veterans that successfully completed OSA diagnostic testing with the HSAT. DIAGNOSING OSA AMONG VETERANS WITH SUD AND PTSD 181 Satisfaction The Client Satisfaction Questionnaire (Larsen et al., 1979) was revised by study staff to assess acceptability of OSA diagnostic testing. Individual questions asked the following: a) How useful was the Obstructive Sleep Apnea screening (NOX T-3) you received? b) Did you receive the information you wanted regard- ing your sleep? c) Would you recommend this process to other veterans on the unit? and d) How satisfied are you with the screen- ing process? Results were on a 4-point Likert scale ranging from 1 (Poor/No,Definitely Not/Quite Dissatisfied)to4(Excellent/Yes, Definitely/Very Satisfied). This instrument was used to measure participants’satisfaction with the intervention following HSAT testing, with higher score indicating higher satisfaction. Demographics Demographics questions were used to assess weight, ethnicity, race, height, relationship status, and service history. Data Analysis Data were analyzed with descriptive statistics and paired sample t-tests using SPSS Version 26. Results Feasibility Forty-five (95.7%) of the veterans successfully wore the HSAT for the testing night and successfully completed objective OSA di- agnosis testing; 2 veterans withdrew from the study after consent- ing. One hundred percent of the veterans who attempted the HSAT successfully completed objective OSA testing. Acceptability Of the 45 veterans who wore the HSAT, 82.2% (n=37) stated that both the usefulness of the OSA diagnosis and ease of the test- ing process were“Excellent,”with the remaining 17.8% of veter- ans (n=8) stating the process was“Good.”Thirty-three (73.3%) of the veterans said they would“Definitely”recommend the test- ing to other veterans, with the remaining 26.6% of veterans (n= 12)saying they“Probably”would recommend the OSA diagnostic testing. Finally, 44.4% (n= 20) of the veterans stated they were “Very Satisfied”with the process, and 55.6% (n= 25) of the veter- ans were“Mostly Satisfied.”Zero percent of respondents stated the process was“Not at all useful/feasible,”“Quite dissatisfied,” or“Would not recommend at all.” OSA Diagnoses Based on the overnight sleep studies, 53.3% of the veterans (n=24) met criteria for a diagnosis of OSA, although some veter- ans were already successfully treating their OSA with a PAP device on the unit. Specifically, 35.6% (n=16) were newly diag- nosed, 8.9% (n=4) were previously diagnosed with OSA and were actively using a PAP, 8.9% (n= 4) had previously been diag- nosed with the current recording confirming the diagnosis but were not using PAP, and 46.7% (n=21) had no OSA (SeeFigure 1). Finally, of the 20 veterans with untreated OSA (16 newly diagnosed and 4 veterans with reconfirmed OSA but not using their PAP treatment), 70.0% (n=14) consented to a pulmo- nary sleep clinic referral for PAP treatment. Baseline Differences by OSA Diagnosis Although the participants fell into four groups (newly diagnosed OSA, previously diagnosed OSA using PAP on unit, previously diagnosed OSA without a PAP on unit, and no OSA), we combined them into two groups based on their symptoms: OSA symptomatic group (AHI$5) and OSA negative/OSA treated group (AHI,5). The OSA symptomatic group included the newly diagnosed OSA veterans and the previously diagnosed OSA veterans who were not wearing PAP on the unit. The OSA negative/nonsymptomatic group included the veterans negative for OSA or previously diagnosed with OSA but actively using PAP. We examined baseline differences between these two groups and found no differences in PTSD, ESS, ISI, or sleep efficiency (seeTable 2). Discussion This study suggests that objective testing for OSA is feasible and acceptable for veterans with SUD and PTSD in a residential setting. Of the 47 veterans who consented, only 2 veterans declined to participate in the study and 45 veterans successfully received testing, showing 95.7% feasibility. We believe that the 2 veterans who withdrew before the overnight HSAT was scheduled withdrew due to study burden (questionnaires, sleep diary, and HSAT) at a vulnerable time in recovery. Of the veterans who attempted to wear the HSAT, 100% were successful in completing the overnight study and received accurate AHIs. Further, we received positive feedback on the acceptability of the overnight HSAT test, with 100% of respondents saying they were“mostly satisfied”or better with the overall process. We found that 53.3% of veterans had a diagnosis of OSA. While 55.6% of participants either did not have OSA or were suc- cessfully treating it with PAP, 44.5% of veterans would have been left untreated on the residential unit without HSAT testing. The number of veterans on the residential unit with untreated OSA is alarmingly high given the potential detrimental effects of untreated OSA on SUD and PTSD outcomes (Colvonen, Straus, et al., 2018; Figure 1 OSAObjectiveTestingWithVeteransontheSARRTP PTSD Track, Including Those With Positive Airway Pressure (PAP) Treatment 46.70% 35.60% 8.90%8.90% Diagnosis (Dx) of OSA No OSA New OSA Previous Dx w/ PAP* Previous Dx w/o PAP* 182 COLVONEN ET AL. Wang & Teichtahl, 2007). The large percentage of veterans in res- idential treatment with untreated OSA also offers a unique oppor- tunity for early evidence-based intervention. While PAP is the gold-standard treatment for OSA, adherence rates are low among veterans with PTSD (Colvonen, Straus, et al., 2018). For example, a recent meta-analysis found that PAP adherence was lower in patients with both OSA and PTSD than OSA alone (Zhang et al., 2017). Early adherence is key to long-term adherence rates for PAP (Budhiraja et al., 2007;Weaver et al., 1997), which suggests that patients should receive follow-ups early after PAP initiation to address any concerns (e.g., claustrophobia) and assist with titra- tion and maskfit(Drake et al., 2003). Due to the dose response of PAP with positive outcomes, increasing adherence to PAP with desensitization to the mask may be essential to help veterans with PTSD (Goldstein et al., 2017). Residential care may be a uniquely stable and supportive environment to initiate PAP therapy due to the reduced external stressors and distractions, controlled environ- ment with professional support, increased structure and account- ability (e.g., more likely to attend sessions and follow through on treatment planning), and increasing access to clinicians to help intervene and motivate individuals (Haller et al., 2019). Future studies should examine whether evidence-based treatment for OSA on a residential unit leads to improved SUD/PTSD treatment outcomes. While we hypothesized that veterans with untreated OSA would have worse SUD, PTSD, and sleep severity, our results did not support this. We found no differences between untreated OSA and the treated or no OSA group on any baseline measures of sleep, substance use, or PTSD severity. We believe that this has to do with the ceiling effects of SUD, PTSD, and sleep severity among veterans just entering residential care, minimizing the variability necessary tofind associations. Another possibility is our small sample size limiting the power necessary to detect differences. Thesefindings may suggest that, in certain settings, symptom se- verity cannot be used as an indicator of high risk for OSA.We recommend integrating objective OSA diagnostic testing into residential care for all residents whether or not they show classic risk factors for OSA (e.g., high BMI or older age). First, as previously mentioned, symptom severity does not discriminate between OSA positive/negative. Second, there is increasing evi- dence that self-report questionnaires for“high risk of OSA”are not accurate as screeners for veterans or PTSD (Kunisaki et al., 2014;Lyons et al., 2021;McMahon et al., 2017). Together, there are no predictable visual, symptomatic, or self-report screeners to indicate who is in need of PAP treatment. Our study has several limitations, including the small sample size and lack of follow-up data. As such, the long-term effects of PAP treatment on SUD and PTSD outcomes are unclear. Given these limitations, this study is best viewed as an objective testing protocol development and will require examination of PAP treat- ment and how that affects SUD and PTSD outcomes. However, this study offers strong support for the importance of diagnostic testing for OSA for individuals with SUD and PTSD while in a residential care setting. Objective testing, and possibly treatment, for OSA is feasible and acceptable in a residential care setting. References Adamson, S. J., Kay-Lambkin, F. J., Baker, A. L., Lewin, T. J., Thornton, L., Kelly, B. J., & Sellman, J. D. (2010). An improved brief measure of cannabis misuse: The Cannabis Use Disorders Identification Test-Re- vised (CUDIT-R).Drug and Alcohol Dependence,110(1–2), 137–143. https://doi.org/10.1016/j.drugalcdep.2010.02.017 Alexander, M., Ray, M. A., Hébert, J. R., Youngstedt, S. D., Zhang, H., Steck, S. E., Bogan R. K., & Burch, J. B. (2016). 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Literature Review Outline: To ensure that students are on track, submit a 2‐3 page double‐spaced APA formatted justification for your topic of interest. This justification should be written in formal
D re am in g Th e P ro p ortio n al E xp erie n ce o f D re am T yp es in R ela tio n t o P osttr a u m atic Str e ss D is o rd er a n d I n so m nia A m on g S urv iv o rs o f I n tim ate P artn er V io le n ce Alw in E . W ag en er Onlin e F ir s t P u b lic a tio n , O cto b er 1 3, 2 022. h ttp ://d x.d oi. o rg /1 0.1 037/d rm 0000227 CIT A TIO N Wag en er, A . E . ( 2 022, O cto b er 1 3). T h e P ro p ortio n al E xp erie n ce o f D re am T yp es in R ela tio n t o P o sttr a u m atic S tr e ss Dis o rd er a n d In so m nia A m on g S urv iv o rs o f In tim ate P a rtn er V io le n ce . Dre am in g . A dva n ce o n lin e p ub lic a tio n . http ://d x.d oi. o rg /1 0.1 037/d rm 0000227 The Proportional Experience of Dream Types in Relation to Posttraumatic Stress Disorder and Insomnia Among Survivors of Intimate Partner Violence Alwin E. Wagener Department of Psychology and Counseling, Fairleigh Dickinson University Survivors of intimate partner violence (IPV) commonly suffer from posttrau- matic stress disorder (PTSD), insomnia, and nightmares. Past studies demonstrate a link between replicative (i.e., replay the trauma) and recurrent (i.e., repeating) night- mares and PTSD and insomnia. However, there is a lack of research on the variety of dreams and nightmares experienced in relation to PTSD and insomnia. This study explored 5 types of dreams and nightmares among 499 IPV survivors recruited through social media to complete an online cross-sectional survey. The dream types were selected based on theories of dreaming, suggesting it exists on a continuum of both repetition and emotion (i.e., dream or nightmare) and that more severe PTSD and insomnia symptomology should be linked to repetitive nightmares. Dream types were transformed for each participant into ratios that showed the proportion of each type of dreaming in relation to all the dreaming reported by the participant over the past 3 days. Then, multiple regressions were used to examine whether those dream types were predictive of PTSD, insomnia, and PTSD symptom criteria. The results showed that only replicative nightmares and novel (i.e., new) dreams were predictive. Additionally, it was discovered that across PTSD and insomnia symptom severities, novel dreams remained relatively constant in number, whereas other types of dream- ing, particularly nightmares, increased in frequency. Keywords:intimate partner violence, dreams, nightmares, PTSD, insomnia Survivors of intimate partner violence (IPV) often experience nightmares along with other symptoms of posttraumatic stress disorder (PTSD) (Nathanson et al., 2012;Phelps et al., 2008). There is a growing understanding of the relationship between nightmares and PTSD in dream literature (Campbell & Germain, 2016; Lemyre et al., 2019). However, research on nightmares among trauma survivors Alwin E. Wagener https://orcid.org/0000-0002-9804-7274 This study was supported by a Grant from International Association for the Study of Dreams and Dream Science Foundation. Correspondence concerning this article should be addressed to Alwin E. Wagener, Department of Psychology and Counseling, Fairleigh Dickinson University, 285 Madison Avenue, Madison, NJ 07940, United States. Email:[email protected] 1 Dreaming ©2022 American Psychological Association ISSN: 1053-0797https://doi.org/10.1037/drm0000227 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. tends to differentiate types of nightmares without examining co-occurring nonnight- mare dreaming (de Dassel et al., 2018;Lemyre et al., 2019). For the above reasons, along with insight from research suggesting that dreaming experiences may exist on a continuum related to the emotion and repetition of dream types (Levin & Nielsen, 2007), theories suggesting dreaming may be part of an internal psychological healing process (Hartmann, 2011;Levin & Nielsen, 2007), and research indicating insomnia may be a co-occurring disorder linked to both PTSD and nightmares (Nappi et al., 2012;Pigeon et al., 2013), the present study investigated the relationship of PTSD and insomnia to a variety of types of dreaming, both nightmares and nonnightmare dreams, among IPV survivors to better understand relationships between partici- pants’oneiric experiences and symptoms. Intimate Partner Violence and Nightmares IPV is a traumatic experience with many serious adverse consequences includ- ing death, injury,financial hardships, mental illness, and social isolation (Coker et al., 2002;Lutwak, 2018;Spencer et al., 2019;Vos et al., 2006). One of the frequent mental illnesses related to experiencing IPV is PTSD (Golding, 1999;Nathanson et al., 2012;Spencer et al., 2019). A study byNathanson et al. (2012)using a diagnos- tic assessment interview found that 57.4% of a community sample of 101 IPV survi- vors had PTSD. Thisfinding is generally consistent with a prior meta-analysis by Golding (1999)showing PTSD rates among IPV survivors ranging from 31% to 84% depending on the IPV populations and assessment approach, though Nathan- son’sfinding has the benefit of being based on a diagnostic interview as opposed to self-assessments used by most of the studies in the meta-analysis. A common experience as part of PTSD is nightmares, often with elements of the traumatic experience being replayed within those nightmares (Phelps et al., 2008;Rasmussen, 2007). The frequency of these nightmares, which studies indicate may be present for 30% to 50% of IPV survivors (Pigeon et al., 2011;Rasmussen, 2007), and the reported negative impact of nightmares (Pigeon et al., 2011;Rasmus- sen, 2007), have been documented in studies but without the context of the entirety of oneiric experiences. Specifically, the occurrence of nonnightmare dreaming and the way dreams and nightmares may co-occur among those suffering from PTSD is not understood. Insomnia is another serious and negative symptom experienced by IPV survi- vors that is both a symptom of PTSD and an independent diagnosis (El-Solh et al., 2018;Nappi et al., 2012;Pigeon et al., 2011). There is support for insomnia some- times having an independent clinical course from PTSD, such that successfully addressing other symptoms of PTSD does not resolve the insomnia (Nappi et al., 2012;Pigeon et al., 2011,2013), though for some individuals, treating PTSD broadly will also resolve insomnia (Pigeon et al., 2011). Among IPV survivors, insomnia is frequently described and identified as a symptom and disorder that impairs both functioning and recovery from the trauma (Pigeon et al., 2011). There is research linking nightmares to insomnia (Habukawa et al., 2007;Woodward et al., 2000), but, as with PTSD, the overall dreaming experience for those suffering from insomnia is not well understood. 2WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. In addition to the links between PTSD and insomnia, nightmares also have a relationship to both PTSD and insomnia that defies a simple description of night- mares as a symptom (Phelps et al., 2008;Pigeon et al., 2011,2013). Just as with insomnia, for some individuals, many of the other symptoms of PTSD may be resolved while nightmares persist (Phelps et al., 2008;Pigeon et al., 2011). There is also some support for nightmares prompting individuals to fear sleeping resulting in insomnia, whereas for others, nightmares may occur without insomnia (Phelps et al., 2008;Pigeon et al., 2011). Overall, research broadly shows a complicated rela- tionship between nightmares, insomnia, and PTSD, but little research exists showing how differing types of dreams and nightmares, particularly dreams, are related to insomnia and PTSD. Dream Emotion and Repetition in Relation to PTSD and Insomnia Links between PTSD severity and nightmares that replicate (i.e., replay) trauma or recur (i.e., happen more than once without replaying trauma) are com- mon and well accounted for in research (de Dassel et al., 2018;Hartmann, 2011; Mellman et al., 2001). However, there is limited research differentiating nightmares based on whether they generate novel (i.e., new) content in relation to PTSD (Hart- mann, 2011;Nielsen & Levin, 2007). This distinction is highlighted as important by several recent theorists who propose that novel nightmares are part of a psychologi- cal recovery process, whereas recurring and replicative nightmares may indicate an impairment in recovery (Hartmann, 2011;Levin & Nielsen, 2007). These theories point to two areas of focus, namely, the emotion (i.e., nightmare or dream) and rep- etition of dreaming (i.e., replicative of a trauma experience, recurring, or novel), for understanding the role of nightmares and dreams in trauma recovery. Nielsen and Levin’s (2007)neurocognitive model of disturbed dreaming (NMDD) describes the occurrence of a fear extinction process in nightmares, whereasHartmann’s (2011)contemporary theory of dreaming (CTD) proposes that nightmares allow trauma-related emotions to be connected to other experiences, imagined and from memory, thereby lessening the intensity and disruptive quality of those emotions. These conceptualizations of nightmares suggest that replicative and recurring nightmares may be an impairment of the psychological healing pro- cess and that dreams (not nightmares) indicate there is a lower level of affective dis- tress. Both of these theories are based on observations of trauma recovery and understandings of neurological processes in dreaming. However, there is little em- pirical evidence of a healing process linked to nightmares or of whether nonnight- mare dreaming coexists with nightmares among individuals with PTSD. There have been some recent attempts to understand the relationship between types of nightmares and PTSD and insomnia symptoms (Davis et al., 2007;de Das- sel et al., 2018;Wagener, 2019). Across the studies, several trends are observed con- sistent with theories positing a continuum of nightmare experiences in relation to PTSD (Hartmann, 2011;Levin & Nielsen, 2007). Thefirst is that replicative dreams are most strongly linked to PTSD. The second is that recurrent dreams are also linked to PTSD just not as strongly, and the third is that nightmares with new con- tent are correlated to PTSD but not as strongly as the replicative and recurrent nightmares (Davis et al., 2007;de Dassel et al., 2018;Wagener, 2019). Though PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 3 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. studies suggest possible relationships between dream and nightmare types to PTSD and insomnia (Davis et al., 2007;Wagener, 2019), they notably do not explore whether nonnightmare dreaming occurs simultaneously with nightmares or varies in frequency with PTSD and insomnia as would be predicted by CTD and NMDD. This information would be valuable for researchers evaluating CTD and NMDD and for clinicians wanting to better understand how the oneiric experiences of patients may relate to PTSD and insomnia symptoms. Toward that end, a research study examining the relationship of PTSD symptoms and insomnia to the frequency of novel dreams, repeating dreams, novel nightmares, recurrent nightmares, and replicative nightmares was conducted to determine whether the previously observed trends remain and discover the relationship of repeating dreams and novel dreams to PTSD and insomnia. To generate a more detailed understanding of dream types to PTSD symptoms, the frequencies of dream types were also examined in relationship to PTSD symp- tom criteria (i.e., criteria B [reexperiencing], C [avoidance], D [negative cognitions and mood], and E [arousal]) (American Psychiatric Association, 2013). Because there is little specific research on those relationships, the hypotheses regarding those relationships were made tentatively, largely based on the NMDD typology of dreaming showing dream types in relation to affect distress and awakening (Levin & Nielsen, 2007, p. 486). Predictions for two types of dreaming, novel nightmares and recurrent dreams, were influenced by both NMDD and previous theorizing (Domhoff, 2000) that recurrent dreams and nightmares occur because what is caus- ing them is not being addressed. The lack of repetition in novel nightmares was therefore hypothesized to be unrelated to avoidance, whereas the repetition of dreams was hypothesized to be correlated with avoidance. Also, it is important to note that though novel nightmares are linked to trauma recovery by CTD and NMDD, they are also associated with trauma and indicate a reaction to trauma. Based on that, they are hypothesized to be positively correlated with PTSD and insomnia, though with less strength than replicative and recurrent nightmares. To better understand the relationship between types of dreaming experiences and PTSD and insomnia, ratios for dream experiences were created. The choice to use ratios instead of reported numbers of dream experiences is due to individual variations in the ability to remember dreams. Numerous studies demonstrate that individuals can learn to remember their dreams, and that as they practice, the num- ber of dreams they remember increases (Aspy et al., 2015;Beaulieu-Prévost & Zadra, 2005). This information indicates that by assessing and comparing the raw number of reported dreams, the memory and focus on remembering dreams is actually one aspect of what is being measured. By generating ratios of dream types for individual participants, it becomes possible to compare dream types more easily between individuals. These ratios were generated with the view that allfive types of dreaming are part of an overall oneiric experience, so each type of dreaming experi- ence was compared with the total reported number of dream experiences. Method This study used an IRB-approved, online, cross-sectional, survey design using Qualtrics, a survey design and distribution program. In Qualtrics, an online survey 4WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. security setting was used preventing multiple entries from the same Internet proto- col (IP) address. Participants were recruited through social media using a $25 dollar gift certificate drawing as an incentive. In the announcement, participants were informed that by completing the survey, they would be entered into a drawing for 20 gift certificates. Initially, the study announcement was sent to online Facebook groups focused on supporting survivors of IPV. This approach generated few responses. To increase recruitment, Facebook advertisements were purchased using funds from an IASD and Dream Science Foundation research grant. The use of advertisements on Facebook to recruit participants is a relatively new approach that demonstrates promise for reaching marginalized populations, increasing the num- ber of participants compared with traditional recruitment, and possibly generating better data than traditional approaches (Harris et al., 2015;Jones et al., 2017;Khatri et al., 2015;Thornton et al., 2016). The advertisements displayed the recruitment announcement that included a link for the online survey and were sent to Facebook members in the United States over 21 years of age who had expressed interest in groups related to IPV, including spousal abuse and domestic violence groups. This approach generated a large number of survey responses. Facebook pro- vided data on the advertisement that showed 1,893 individuals saw the invitation to the study, 1,179 individuals opened the survey, and 668 individuals met the require- ments for participation and agreed to participate (56.7% of those who opened the survey). Of those, 499 (74.7% of those who agreed to participate) completed the survey to the extent that their responses were used in analyses with 458 (68.6% of those who agreed to participate) fully completing it. Participant Demographics The average age of participants was 38.73 years (SD= 12.12). In terms of race/eth- nicity, 415 (83.2%) reported they were White, 10 (2%) African American, 16 (3.2%) Hispanic, four (.8%) Asian, seven (1.4%) Native American, one (.2%) Pacific Islander, two (.4%) declined to say, with the remaining 44 (8.8%) participants reporting a combi- nation of race/ethnicities. The gender of participants was predominantly female (N= 470, 94.2%), though also included seven (1.4%) males, four (.8%) trans males, one (.2%) trans female, 14 (2.8%) gender-nonconforming/gender-queer participants, and two(.4%)whoreported“other.” Participants reported a variety of abuse experiences. The categories of abuse were physical, emotional, verbal, and sexual. Only 26 (5.2%) participants reported only a single form of those four types of abuse. Among participants, 356 (71.3%) reported physical abuse, 488 (97.8%) reported emotional abuse, 446 (89.4%) reported verbal abuse, and 263 (52.7%) reported sexual abuse. The types of abuse are consistent with those that lead to PTSD among IPV survivors. TheDSM–5 requires“exposure to actual or threatened death, serious injury, or sexual violence in one (or more) of the following ways,”and those ways are direct experience, wit- nessing, learning that it happened to a close friend or family member, or through prolonged exposure to details of traumatic events (American Psychiatric Associa- tion, 2013). The nature of this study makes it impossible to assess whether partici- pants met this criterion, though even participants endorsing just emotional or verbal abuse mayfit the criteria based on having felt threatened with death, physical PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 5 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. violence, or sexual violence or through perceiving others such as their children as being threatened. Participants’abuse ended an average of 5.80 (SD= 7.16) years prior to complet- ing the survey. The abusive relationships had lasted an average of 7.40 (SD= 7.12) years before ending, with 74.1% of participants reporting they had been out of any abusive relationship for more than a year. Among participants, 62.7% reported more than just one abusive relationship in their lifetime with those reporting more than one relationship having an average of 2.13 (SD= 1.32) abusive relationships prior to the last one experienced. At the time they completed the survey, only 143 participants reported currently dating or being in a committed relationship, down from 480 who reported having been in a committed abusive relationship. Additional demographic information can be seen inTable 1. Taken together, the participants in this study suffered a variety of abuses from their intimate partner with whom they had been together for many years and from whom they have been apart for many years. Instruments Demographics The demographics instrument in this study included general demographics such as age, ethnicity, and gender along with more detailed information related their Table 1 Participant Demographics Demographic characteristicsn% Sexual orientation Heterosexual 375 75.3 Homosexual 13 2.6 Bisexual 88 17.7 Other 22 4.4 Total 498 100 Relationship status Married 194 38.9 Committed relationship Cohabitating 192 38.5 Living separately 75 15 Dating 14 2.8 Civil union 5 1 Total 480 96.2 Education level Some high school completed 13 2.6 High school diplomas or GEDs 155 31.1 Associates degree 103 20.7 Bachelor’s degree 117 23.5 Graduate or professional degree 60 12 Other 50 10 Total 498 99.9 Household income Under $30,000 296 59.3 $30,000–$59,000 147 29.5 $60,000–$100,000 47 9.4 Over $100,000 9 1.8 Total 499 100 Note. GED = general equivalency diploma. 6WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. experiences of IPV such as the types of abuse they experienced. The format and con- tent was adapted from previous studies of IPV survivors (Flasch et al., 2017;Murray et al., 2015;Wagener, 2019) and was used to contextualize the results of the study. Posttraumatic Stress Disorder Checklist for DSM–5 The Posttraumatic Stress Disorder Checklist forDSM–5(PCL-5) was used to assess PTSD symptoms among participants. The PCL-5 uses 20,five-point Likert questions to assess for PTSD symptoms experienced over the last month. It is a self- report assessment aligned with theDiagnostic and Statistical Manual of Mental Dis- orders(DSM–5) symptom criteria (U.S. Department of Veterans Affairs, 2014; Weathers et al., 2013). The PCL-5 has strong psychometrics, with a recent study showing a Cronbach’s alpha of .96 and test–retest reliability of .84 over a period ranging from 22 to 48 days (Bovin et al., 2016). Additionally, it has been found to have strong construct validity. The PCL-5 has a cutpoint of 31–33 (Bovin et al., 2016;Weathers et al., 2013). In the present study, the Cronbach’s alpha was found to be .912, which is in line with previous studies. To eliminate autocorrelations, the PCL-5 question asking about“repeated, disturbing dreams of the stressful experi- ence”was not used in the primary statistical analyses. Pittsburgh Sleep Quality Index The Pittsburgh Sleep Quality Index (PSQI) is an instrument used to assess sleep quality and quantity using a 19 item self-assessed questionnaire with open and multiple-choice questions. It uses a scoring system producing results of 1–21, with 5 being a cut-score above which is indicative of impaired sleep quality (Backhaus et al., 2002;Carpenter & Andrykowski, 1998). Studies have shown a Cronbach’s alpha of .80–.85 and test–retest reliability of .86 (45.6618 days) to .90 (2-day inter- val) along with good construct validity (Backhaus et al., 2002;Carpenter & Andry- kowski, 1998). The Cronbach’s alpha for the current study in lower than those found in the studies ofBackhaus et al. (2002)andCarpenter and Andrykowski (1998)but still acceptable at .725 (461). Before using the PSQI in the primary statis- tical analyses, the question in it asking if sleep difficulties are related to“bad dreams”was removed to eliminate autocorrelation. Types of Dreams and Nightmares Survey The Types of Dreams and Nightmares Survey contains six questions and is adapted from a previous instrument (seeWagener, 2019) that was reviewed by four mental health counseling experts with experience and knowledge related to IPV, dreams, and nightmares. The adaptations were designed tofit the broader explora- tion of dream and nightmare types but retained language from the previous instru- ment where possible. The survey defines thefive types of dreams and nightmares assessed in the survey and asks participants to select the number of each type of dream or nightmare experienced over the last three days. Asking about the types of dreams experienced over the past three days was a modification done to enable participants to better recall and differentiate between the types of dreams and nightmares they experienced. The short time-period was prompted by observing that studies using a week-long time span generated fewer dream reports than studies in sleep labs recording dreams upon awakening (Krakow et al., 2002;Schredl & Olbrich, 2019;Van Schagen et al., 2016). Three days was a compromise between the two approaches. It was hoped that it was enough time to PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 7 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. gather an adequate number of responses, while not being so long as to make it diffi- cult for participants to recall and identify the type of dreams experienced. Thefive dream types used in the study were generated based on CTD and NMDD and for the ability to differentiate them in a survey design. A typology for dreaming is found inLevin and Nielsen (2007, p. 486), which was helpful for developing the categories, but the different dream types were weighed against likely participants’ abilities to name and properly categorize their dream types. The currentDSM–5defini- tion of nightmares along with the popular definition of nightmares does not use being awakened by the nightmare as definitional criteria (American Psychiatric Association, 2013;Collins English Dictionary, n.d.), so that differentiation between bad dreams (also called disturbed dreaming) and nightmares was not used. Instead, based on repe- tition of content being linked to greater severity according to CTD and NMDD, bad dreams and nightmares were combined and a differentiation between novel and recur- rent nightmares was used (Hartmann, 2011;Levin & Nielsen, 2007). Each type of dream and nightmare was named and defined in the survey. An example of how replicative nightmares were presented in the survey was with the description,“Nightmares That Replay Frightening or Disturbing Waking Life Experiences.”Novel nightmares were presented as,“New Nightmares- Nightmares that Do not Repeat and Do Not Replay Abuse you have experienced.”The choices provided for participants to report how many of each type of dream they experi- enced in the past three days were“not at all,”“once,”“twice,”“three times,”“four times,”“five times,”“six times,”or“more than six times.”For those who answered more than six times, they were directed tofill in the specific number of that type of dream or nightmare they experienced over the past three days. The number of dreams for each type of dream or nightmare was then used in the study. Data Analyses All data were analyzed using the Statistical Package for the Social Sciences (SPSS). There were six dependent variables, PTSD, PTSD symptom criteria B, C, D, and E, and insomnia, used in the analyses andfive independent variables, the ra- tio score for each dream type. Stepwise multivariate regression analyses were used to determine which of the independent variables significantly predicted each of the dependent variables. Becausefive predictor variables were used in the multiple regressions, a Bonferroni correction was applied, and thepvalue was changed from .05 to .001 for all the analyses. Based on the primary analyses, post hoc multiple regression analyses of the raw frequencies of thefive types of dreams in relation to PTSD and insomnia were run after 14 outliers had been removed using a Mahalano- bis Distance Test (Tabachnick & Fidell, 2013). Finally, a multiple regression analysis was used to understand the relationship between the total number of reported dreams and nightmares and PTSD and insomnia in response to a post hoc question. Results Preliminary Analyses Initial analyses of the study variables showed most of the participant popula- tion had symptoms consistent with PTSD (M= 46.88,SD= 14.49). The cutoff score 8WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. can be interpreted as being between 31 and 33, so using the conservative, higher cut- off score of 33, 82% of participants had symptoms consistent with a diagnosis of PTSD. There were even more participants with symptoms consistent with insomnia. The cutoff score for the PSQI is 5, above which participants have symptoms consist- ent with insomnia. The mean PSQI score in this study was 13.05 (SD= 3.72), and 98.1% of participants scored above the cutoff. The psychometrics for the variables are inTable 2. In an analysis of dream and nightmare types, most participants reported having each of the dream and nightmare types within the past three days. It is also notewor- thy that of the 499 participants reporting the types of dreaming they experienced, 178 (35.7%) reported no replicative nightmares, 156 (31.3%) no recurrent night- mares, 134 (26.9%) no novel nightmares, 239 (47.9%) no repeating dreams, and 176 (35.3%) no new dreams. Out of 499 participants, the average number of total night- mares and dreams reported by participants was 6.28 (SD= 4.04, Skew = .11, Kurto- sis = .19), meaning that participants reported an average of two dreams per night. This frequency of dreams and nightmares is surprising and higher than shown in other studies of trauma survivors, with studies of sexual assault survivors reporting between 5.21 and 6.54 nightmares/week (Krakow et al., 2000,2002) and a recent study of participants with diverse mental disorders recording 4.84 (SD= 3.16) night- mares per week (Van Schagen et al., 2016). It is important to note those studies looked at nightmares and did not include dreams. However, when looking at only nightmares in the current study the mean nightmares per three days was 3.97 (SD= 2.96) which translates to 9.26 per week. A difference between this study and many others is that participants were asked about specific dream and nightmare types experienced within the past three days. Requesting for recall within three days instead of a more typical week time-period may, as was intended in the study design, aid memory of oneiric experiences, and prompting recall of specific dreams and nightmares may also aid recall, though that is speculative. Another possibility is that the high level of insomnia among the participants leads to an experience more akin to sleep studies that ask participants to recall dreams after awakening them from REM sleep. In those studies, dream recall rates are higher. A recent study by Schredl and Olbrich (2019)found a mean of 3.67 (SD= 1.76) dreams being remem- bered among 24 participants with insomnia and restless legs syndrome diagnoses Table 2 Variable Psychometrics VariablesNM5% Trimmed meanSDRange Skew Kurt PTSD 499 46.88 47.14 14.49 6–80 0.21 0.47 PSQI 455 13.09 13.16 3.71 2–21 0.25 0.47 Replicative nightmares 495 1.44 1.20 2.17 0–31 6.61 75.75 Recurrent nightmares 495 1.41 1.26 1.50 0–11 1.56 3.83 Novel nightmares 495 1.50 1.34 1.50 0–8 1.38 1.89 Repeating dreams 495 1.14 0.95 1.57 0–12 2.01 6.08 Novel dreams 495 1.43 1.26 1.69 0–18 2.68 18.04 Criterion B 499 11.58 11.62 4.48 0–20 0.14 0.65 Criterion C 499 5.35 5.46 2.06 0–8 0.58 0.37 Criterion D 499 16.54 16.64 6.13 0–28 0.24 0.70 Criterion E 499 13.42 13.47 4.78 1–24 0.15 0.46 Note. PSQI = Pittsburgh Sleep Quality Index; PTSD = posttraumatic stress disorder. PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 9 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. during a two night period, translating to 12.85 per week. This is an area where future research may be helpful in better understanding dream recall among this popula- tion. Overall, the demographic information indicates that participants in this study were suffering from high levels of PTSD symptoms and insomnia and experiencing frequent nightmares and dreams. Dream Types in Relation to PTSD and Insomnia SeeTable 3for statistical data from the regressions showing which dream types predict PTSD and insomnia andTable 4for statistical data from regressions show- ing which dream types predict each of the PTSD symptom criteria. Based on these analyses, most hypotheses were not supported. The only dream types to significantly predict PTSD, PTSD symptom criteria, and insomnia were novel nightmares and replicative nightmares. Post Hoc Analyses Most studies examining dreams use the reported number of dreams, the raw frequencies. To make thefindings in this study more relatable to previous studies, the analyses were run again with raw frequency scores as seen inTable 5. Only two variables were significant in relation to PTSD and insomnia, replicative and recur- rent nightmares. The disappearance of novel dreams from the raw score analyses prompted curiosity as to why novel dreams only appear as significant when put into context with other dream types using the ratio score. To better understand why that difference exists, scatterplot graphs were examined (seeFigure 1). Those graphs demonstrated that the frequency of novel dreams is relatively consistent across PTSD and insomnia scores even as the ratio of novel dreams noticeably decreased as PTSD and insomnia scores increase. Based onfinding both a lack of change in number of novel dreams and a change in the proportion of novel dreams to all dreams and nightmares in relation to PTSD and insomnia, it seems clear that the number of total dreams and nightmares must increase with PTSD and insomnia severity. To confirm that, multiple regression Table 3 Stepwise Regressions Results Between Dream-Type Ratio Scores and PTSD and Insomnia Variable B 95% CI for BSEBbR 2 DR 2 PTSD (N= 490) Model 1 Constant 41.629 [39.983, 43.275] 0.838 Replicative nightmares 17.267 [11.432, 23.103] 2.970 0.255** 0.065** 0.063** Model 2 Constant 44.297 [41.987, 46.607] 1.176 Replicative nightmares 13.691 [7.509, 19.874] 3.147 0.202** 0.084* 0.08* Novel dreams 9.153 [ 14.765, 3.542] 2.856 0.149* Insomnia (N= 447) Model 1 Constant 13.713 [13.252, 14.174] 0.234 Novel dreams 4.223 [ 5.706, 2.740] 0.755 0.256** 0.066** 0.064** Note. CI = confidence interval; PTSD = posttraumatic stress disorder. *p= .001. **p,.001. 10WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. analyses were conducted. The regressions showed total dreams and nightmares had a significant, positive relationship with both PTSD (R 2= .12,b= .35,F(1, 478) = 66.12,p,.001, 95% CI [.82, 1.34]) and insomnia (R2= .04,b= .21,F(1, 436) = 19.136,p,.001, 95% CI [.09, .25]), consistent with the graphs. Discussion Understanding the results of this study are aided by recognizing the dream types used in the main analyses are proportions of total dreaming. With that in mind, the results indicate that as the proportion of novel dreams decreases, replaced by the other forms of dreaming, there is a significant and moderate increase in insomnia symptoms and the PTSD criterion E, symptoms of alterations in arousal Table 4 Stepwise Regressions Results Between Dream-Type Ratio Scores and PTSD Symptom Criteria Variable B 95% CI for BSEBbR 2 DR 2 Criterion B with nightmare question removed Model 1 Constant 8.669 [8.240, 9.097] 0.218 Replicative nightmares 5.294 [3.776, 6.813] 0.773 0.296* 0.088* 0.086* Criterion C Model 1 Constant 5.670 [5.420, 5.920] 0.127 Novel dreams 1.492 [ 2.296, 0.688] 0.409 0.163* 0.026* 0.024* Criterion D Model 1 Constant 15.453 [14.712, 16.195] 0.378 Replicative nightmares 5.534 [2.904, 8.163] 1.338 0.184* 0.034* 0.032* Criterion E Model 1 Constant 14.370 [13.794, 14.947] 0.293 Novel Dreams 4.432 [ 6.287, 2.578] 0.944 0.208* 0.043* 0.041* Note. N= 490. CI = confidence interval; PTSD = posttraumatic stress disorder. *p,.001. Table 5 Stepwise Regressions Results Between Raw Frequency of Dream-Types and PTSD and Insomnia Variable B 95% CI for BSEBbR 2 DR 2 PTSD (N= 481) Model 1 Constant 40.401 [38.823, 41.980] 0.803 Replicative nightmares 3.597 [2.758, 4.435] 0.427 0.360* 0.129* 0.128* Model 2 Constant 38.370 [36.653, 40.087] 0.874 Replicative nightmares 2.706 [1.823, 3.589] 0.449 0.271* 0.176* 0.173* Recurrent nightmares 2.380 [1.483, 3.276] 0.456 0.234* Insomnia (N= 438) Model 1 Constant 11.849 [11.394, 12.303] 0.231 Replicative nightmares 0.747 [0.505, 0.989] 0.123 0.279* 0.078* 0.076* Note. CI = confidence interval; PTSD = posttraumatic stress disorder. *p,.001. PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 11 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. and reactivity, and a weak but significant increase in PTSD symptoms and PTSD cri- terion C, symptoms of avoidance. Thefinding that a decreasing proportion of novel dreams was the best predictor for insomnia and those two PTSD criteria is surpris- ing and suggests that it may be the appearance of the range of nightmares and not a specific kind that best relates to insomnia and the PTSD linked symptoms of avoid- ance, arousal, and reactivity. Replicative nightmares are the other type of dreaming ratio found to be signifi- cant in the study. As the proportion of replicative nightmares increases, PTSD and the PTSD symptom criterion B, intrusion symptoms, significantly and moderately increase, whereas criterion D, symptoms of negative alterations in cognitions or mood, significantly and weakly increases. These relationships are in line with expectations but weaker than would be expected. It is also surprising that the pro- portion of recurrent nightmares demonstrates no significant relationship and is not Figure 1 Scatterplot of Raw Frequencies of Novel Dreams to PTSD and Insomnia Note. PTSD = posttraumatic stress disorder. 12WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. included in any of the models. That the two ends of the spectrum of dreaming, novel dreams and replicative nightmares, are the two types that proportionally are most significantly related to PTSD and insomnia generallyfits with current understand- ings (Lemyre et al., 2019;Levin & Nielsen, 2007). In the regressions, the variance of scores is not well explained by the predictor variables. In the context of the study, there are several reasons that may account for this. A likely reason is that in this cross-sectional study, participants were asked about dreaming over the past three nights. In a population in which both dreams and nightmares are occurring frequently, which this study demonstrates is happen- ing, it may be that the variety of dream experiences is difficult to capture in a short period of assessment, particularly coupled with the challenges that may occur in remembering them over the past three days. However, additional research is needed to make sense of thisfinding. The post hoc analyses provided slightly different results than the main predic- tions and were in line with past studies showing replicative and recurrent nightmares significantly, positively predictive of PTSD and replicative nightmares significantly, positively predictive of insomnia (Davis et al., 2007;de Dassel et al., 2018;Wagener, 2019). Thefindings from this study reinforce the existingfindings supporting rela- tionships between those variables, but this study also demonstrates there is another way to look at reports of dream frequencies. Particularly for evaluating theories of dreaming, it may be more important to understand how different dreaming types relate to the overall dreaming frequency. There are a few importantfindings from this study. One of the most interesting is how frequently types of nightmares and dreams co-occur. There are suggestions from literature that this sometimes happens (Rasmussen, 2007;Wagener, 2019), but tofind the majority of participants having elevated levels of PTSD and insomnia symptoms along with frequent co-occurring dreams and nightmares within a three- day time period is a novelfinding. It is also unexpected tofind that not only do novel dreams co-occur but remain relatively consistent in frequency. Thesefindings indi- cate an inner landscape that is more complex than is generally captured by research- ers looking at nightmares in relation to trauma. Furthermore, it challenges CTD and NMDD regarding how trauma affects dreaming. With NMDD, it prompts the question, if affect load and distress lead to the inability to generate novel dreaming, as proposed, how can novel dreams be co-occurring, at least within a three-day pe- riod, with replicative nightmares (Levin & Nielsen, 2007). With CTD, it challenges the conceptualization of a gradual transformation from replicative, to recurrent, to novel nightmares, and back to novel dreaming that CTD proposes occurs in reaction to trauma and as part of a healing process from trauma (Hartmann, 2011). With both CTD and NMDD, the general trends proposed by those theories are consistent with thefindings in this study, but the co-occurrences of dream types are not. It may be that the trends described in CTD and NMDD are what generally occurs after trauma, but in a population such as the one in this study, in which most participants are suffering from chronic, high levels of PTSD and insomnia, there is a different manifestation of dreaming. Regardless, the consistency of novel dreams for those experiencing trauma is a newfinding, so it needs to be reproduced in additional studies, but if it holds, it creates new questions related to the formation and function of dreams and nightmares. PROPORTION OF DREAM TYPES TO PTSD AND INSOMNIA 13 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Clinical Implications There are clinical implications from this study, though the context of this study, which looked at survivors of IPV, the majority of whom had symptoms of PTSD and insomnia consistent with clinical significance, must be recognized. For counselors and psychotherapists, a primary implication is that the presence of replicative and recurrent nightmares should prompt an exploration of trauma history and PTSD and insomnia symptoms. Replicative nightmares in particular are strongly corre- lated with higher PTSD and insomnia scores. This is not a new implication as similar guidance stems from a variety of other studies (Davis et al., 2007;de Dassel et al., 2018;Pillar et al., 2000). A relatedfinding for clinical work is that if clients report experiencing novel dreams, it does not mean that replicative and recurrent nightmares are not co- occurring, as this study showed the consistent occurrence of novel dreams even with elevated PTSD levels and co-occurring replicative and recurrent nightmares. This is a new recommendation based on this study. It is not known if mental health professionals make assumptions about psychological health based on a report of a novel dream, but if any clinicians do make such assumptions, which seems reason- able based on the strong associations between nightmares and problematic mental health (Lancee & Schrijnemaekers, 2013;Lemyre et al., 2019;Levin & Nielsen, 2007;Swart et al., 2013) and the lack of studies suggesting that novel dreams and nightmares co-occur, this study indicates that the presence of dreams may not be a reliable indicator that nightmares are not present. Future Directions Future research should look to confirm thefindings of this study, as it both used a new proportional approach to understanding dream experience and found novel dreams remaining relatively consistent in frequency even as more nightmares appeared for those with higher PTSD and insomnia scores. Future longitudinal stud- ies would allow for a better evaluation of the relationship of novel dreams to total dreaming. It would be informative to observe the proportions of dream types over time, as those observations might better explain the variance in scores observed in the study and allow for better evaluation of trends in dreaming related to PTSD and insomnia recovery. The latter information would be beneficial for evaluating CTD and NMDD. One additional area of focus for future research is better differentiating and assessing dream types. The differentiation based on emotion is currently dichoto- mous (i.e., dreams or nightmares), which does not adequately account for the range of emotional experiences in dreaming making it difficult to assess a continuum of emotion as proposed in CTD and NMDD (Hartmann, 2011;Levin & Nielsen, 2007). Repetition suffers from a similar simplification that does not match the real- ities of dreaming. The categories of replicative, repeating, and novel, are useful, but afiner gradation, just as with emotion would be beneficial and allow for a better evaluation of whether it too is part of a continuum as suggested by CTD and NMDD (Hartmann, 2011;Levin & Nielsen, 2007). 14WAGENER This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Limitations There are a few limitations to this study. It is a cross-sectional study, so it is impossible to know how the relationships change over time based on this study, and the results may not fully capture the range of dream types experienced by partici- pants. The online recruitment approach using a monetary inducement could have led to inaccurate responses, though the length of the survey, knowledge that the inducements were limited and would be provided at the end of the study by a draw- ing, and the ability of Qualtrics to prevent the same IP address from doing the sur- vey more than once likely reduced fraudulent survey completion. The sole use of online recruitment is another potential limitation, though the ubiquity of smart phones and other technology makes this limitation far less significant than in the past, as recent surveys suggest that even among the poor and elderly, a large propor- tion of the population has access to both the technology and the Internet and regu- larly use both (Perrin & Atske, 2021). There are additional limitations related to studying dreams. Dreams involve remembered experiences that may vary from the reality of what was experienced, so whether what was recorded actually reflects what was experienced is uncertain. The dream frequency assessment questions were generated for this study by the study author. The study author took a direct approach, asking for specific types of dreams and nightmares, but because it is a novel assessment, there is the potential for structural issues or wording to affect the generated responses. Finally, it must be acknowledged that there are cultural limita- tions to the study. The results were found among a largely white and female partici- pant population actively using social media in the United States. Therefore, the results may not be generalizable to other populations and locations. References American Psychiatric Association. 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Sleep and Biological Rhythms (2019) 17:447–454 https://doi.org/10.1007/s41105-019-00237-w ORIGINAL ARTICLE The in uential factor of narcolepsy on quality of life: compared to obstructive sleep apnea with somnolence or insomnia Mei Ling Song 1 · Keun Tae Kim 2 · Gholam K. Motamedi 3 · Yong Won Cho 2 Received: 28 October 2018 / Accepted: 14 August 2019 / Published online: 26 August 2019 © Japanese Society of Sleep Research 2019 Abstract Narcoleptics tend to have a low quality of life (QoL). Few studies have compared QoL in narcolepsy against other sleep disorders. The purpose of this study was to investigate QoL and its inuential factors in narcolepsy patients compared to obstructive sleep apnea (OSA) with somnolence and primary insomnia. We enrolled 63 narcoleptics (33 type 1, 30 type 2), 49 patients with OSA with somnolence, and 87 insomniacs. All patients were diagnosed through detailed clinical face-to-face interviews and polysomnography and had no other comorbid sleep disorders or medical diseases. All patients completed the Korean-version of the Short-Form 36-Item Health Survey (K-SF36) and a series of standard sleep-related questionnaires. The QoL of the narcolepsy group was comparable to the OSA with somnolence and insomnia groups. There was no signicant dierence between type 1 and type 2 narcolepsy on the total score of the K-SF36. However, factors that had the most impact on QoL included anxiety followed by depressive mood for narcoleptics, depressive mood followed by severity of insomnia for OSA with somnolence, and insomnia severity followed by depressive mood for insomniacs. Mood disturbances, mainly anxiety, aected QoL most in narcolepsy patients. Excessive daytime sleepiness and nocturnal sleep disturbance did not directly aect QoL of narcoleptics. To improve QoL in narcoleptics, proper management of anxiety should be considered as part of the treatment. Keywords Narcolepsy · Quality of life · Cataplexy · Excessive daytime sleepiness Introduction Narcolepsy is a neurological sleep disorder characterized by excessive daytime sleepiness (EDS) caused by loss of hypo- cretin (orexin) in postero-lateral hypothalamus. Narcolepsy is classied as narcolepsy with cataplexy (type 1, NT1), or without cataplexy (type 2, NT2) [1 ]. The prevalence of type 2 and type 1 narcolepsy lies between 0.16–0.66% and 0.025–0.05%, respectively [2 ]. While there is no reported prevalence for narcolepsy in the Korean adult population, in a study of 20,407 Korean adolescents the prevalence of narcolepsy with cataplexy in the younger age groups was determined as 0.015% [3 ]. Narcoleptics often experience more di culties in their social, vocational, and personal lives. Patients with narco- lepsy report higher rates of comorbid medical and/or psychi- atric problems, and often present with additional sleep disor – ders [4 , 5 ]. Thus, it can be understood that narcolepsy has a negative inuence on quality of life (QoL) [6 , 7 ]. However, the exact factors that adversely aect QoL in these patients are not well known. Excessive daytime sleepiness, a major symptom of narcolepsy with considerable effects on QoL, has been reported in 91% of narcoleptics [8 ]. In obstructive sleep apnea (OSA), EDS/somnolence is also commonly reported [ 9 ] with a negative inuence on QoL [10]. A previous study reported that the QoL in narcolepsy patients was lower than in OSA patients [11]; however, it did not exactly dierenti- ate between narcolepsy and OSA with somnolence (OSA- som), and to our knowledge no other study has compared QoL between narcolepsy and OSA-som (Korean version of Epworth sleepiness scale, K-ESS ≥ 10). Therefore, it would * Yong Won Cho [email protected] 1 College of Nursing, Daegu Health College, Daegu, South Korea 2 Department of Neurology, Dongsan Medical Center, Keimyung University School of Medicine, 56 Dalseong-ro, Jung-gu, Daegu 41931, South Korea 3 Department of Neurology, Georgetown University Hospital, Washington, DC, USA Vol.:(0123456789) 1 3 448 Sleep and Biological Rhythms (2019) 17:447–454 be valuable to compare QoL between narcolepsy and OSA- som, in addition to investigating the impact of EDS on QoL. Fatigue, poor memory, social or vocational dysfunction, and mood disturbances are common complaints in chronic insomnia, which are recognized to have a negative inuence on QoL [12, 13]. Despite di culty sleeping at night, insom- niacs do not exhibit heightened levels of EDS [14]. On the other hand, comparing dierent sleep-related symptoms in narcolepsy and insomnia, EDS has been established as the predominant symptom of narcolepsy. Therefore, given the eects of sleep disturbance on daytime activity and wors- ening the QoL, to compare narcolepsy and insomnia, we investigated the degree of sleep disturbance and its inuence on QoL. Cataplexy often happens without notice and can be harm- ful to patients. It commonly interferes with the patients’ social life. The lack of hypocretin is reported to be associ- ated with more EDS and wake intruding sleep in NT1 [15]. In fact, some studies have indicated the involvement of hypocretinergic system in emotional and psychiatric symp- toms such as anxiety [16] and depression [17]. In addition, cataplexy often happens in with the setting of emotional behaviors such as laughter. Despite some dierences in day – time functioning between NT1 and NT2, they both present with EDS. The purpose of this study was to investigate QoL in nar – colepsy focusing on its inuential factors. In particular, to control for sleep-related factors especially EDS, we com- pared QoL in patients with narcolepsy (both NT1 and NT2), OSA-som, and insomnia. Methods We retrospectively screened 148 patients with possible narcolepsy who had visited a tertiary sleep center between August 2011 and August 2016. All subjects had been eval- uated by a sleep specialist and had completed a series of standard sleep-related questionnaires. According to the International Classication of Sleep Disorders (ICSD) 3rd edition [1 ], narcolepsy was dened by a mean sleep onset latency of less than 8 min and two or more sleep onset REM periods (SOREMPs) during a standard multiple sleep latency test (MSLT). In the right clinical setting with cor – roborative ndings on the sleep studies, we do not routinely check the cerebrospinal uid hypocretin level. During the screening process, we excluded 48 patients who did not meet the diagnostic criteria for narcolepsy (13 patients with mean sleep onset latency of more than 8 min, and 35 patients with less than 2 SOREMs), 28 patients who did not provide data for QoL, and 9 patients with other comorbid medical diseases (Fig. 1). We successfully enrolled 63 narcoleptics. All of the patients were diagnosed with narcolepsy for the rst time and the data were collected before starting the treatment. The controls included 87 patients with insomnia, and 49 patients with OSA-som out of 165 total OSA patients. Of that total, 116 were OSA patients without somnolence. All subjects completed a PSG. Insomnia was diagnosed accord- ing to the ICSD 3 [1 ], and patients with OSA had respiratory disturbance index (RDI) score of 5 or higher [1 ]. Patients were categorized as OSA-som if their Korean version of Epworth Sleepiness Scale (K-ESS) score was ≥ 10 [ 18]. We excluded patients younger than 18 years, who did not do quality of life questionnaires, and who were comorbid with medical diseases, psychiatric disorders, or other sleep disor – ders such as restless legs syndrome and parasomnia. This study was approved by the institutional ethics com- mittee of the regional hospital. Measurements Quality of life was assessed using the Korean version of the Short-Form 36-Item Health Survey (K-SF36) [19]. K-SF36 is a common measure of health that is often used to deter – mine the cost-eectiveness of treatment. All of the subjects completed sleep- and psychiatric-related questionnaires including Korean versions of the Insomnia Severity Index (K-ISI) [20], the K-ESS [18], and the Hospital Anxiety and Depression Scale (HAS, HDS) [21]. Statistics analysis Data analysis was performed using SPSS version 25.0, and p < 0.05 was considered statistically signicant. We com – pared narcolepsy with OSA-som, and insomnia patients. The Chi-square test was used for dierences in gender, ANOVA was used to analyze age and BMI (Body Mass Index) data, Fig. 1 Narcolepsy patient screening ow chart 1 3 449 Sleep and Biological Rhythms (2019) 17:447–454 and Schee test for post hoc testing. ANCOVA was used to analyze K-ISI, K-ESS, and K-SF36 scores after adjusting for age and gender, and pair-wise comparisons were used for dierences found between the groups. Pearson correlation was used for analyzing the correlation between age, gender, BMI, K-SF36, K-ISI, K-ESS, HAS, HDS, and MSLT/RDI. Multiple linear regression was used to investigate the predic- tors of QoL. Results Demographic and clinical characteristics among narcoleptics, OSA‑som patients, and insomniacs We studied 63 narcoleptics (33 NT1 and 30 NT2), 49 OSA- som patients, and 87 insomniacs. Narcoleptics were signi- cantly younger than both OSA-som patients and insomniacs. Narcoleptics were more likely to be female than OSA-som patients (31.7% vs. 12.2%, respectively), but less likely to be female compared to insomniacs (31.7% vs. 63.2%, respectively). There was no signicant dierence in BMI among the groups (Table 1). Severity of insomnia was worse in narcoleptics compared to OSA-som patients; however, this dierence did not reach statistical signicance. Narcoleptics had signicantly less severe insomnia compared to insomniacs. The severity of EDS was signicantly higher in narcoleptics than OSA-som patients and insomniacs. The mean of RDI from narcolepsy patients and insomniacs was signicantly less than OSA- som patients, and the RDI score from all narcolepsy patients and insomniacs was less than 5. In terms of anxiety and depression, there were no signi- cant dierences among the three groups (Table 1). Comparing QoL of narcoleptics, OSA‑som patients, and insomniacs There were no signicant dierences in QoL between the three groups. However, the mental component of QoL and total QoL were signicantly higher in OSA patients than in insomniacs (Table 1). Table 1 Demographic and clinical characteristics between groups Adjusted age and gender ANCOVA were used for analysis K-ISI, K-ESS, HAS, HDS, K-SF36 K-ISI Korean version of Insomnia Severity Index, K-ESS Korean version of Epworth Sleepiness Scale, RDI Respiratory Disturbance Index, HAS Hospital Anxiety scale, HDS Hospital Depressive Scale, K-SF36 Korean version of 36-item short-form health survey, PCS physical component summary, MCS mental component summery a Narcolepsy patientsb OSA with somnolence patientsc Insomniacs Narcolepsy a (N = 63) OSA with somnolence b (N = 49) Insomnia c (N = 87) F/x 2 pSchee/pairwise Age (years) 27.03 ± 9.2939.20 ± 11.8347.25 ± 13.05 54.81< 0.001 a < b < c Gender (% female) 20 (31.7)6 (12.2)55 (63.2) 36.80< 0.001 BMI (kg/m 2) 24.80 ± 3.90 25.33 ± 2.4823.85 ± 12.46 0.510.600 Narcolepsy (% Type 1) 33 (52.4)–– –– Sleep measure K-ISI 12.75 ± 6.107511.27 ± 4.8919.39 ± 5.74 21.05< 0.001 a < c, b < c K-ESS 15.17 ± 4.2613.43 ± 2.575.06 ± 4.50 82.69< 0.001 a > b > c RDI 1.55 ± 2.5836.97 ± 26.400.50 ± 1.02 136.67< 0.001 b > a, b > c 5 ≤ RDI < 15 (%) 12 (24.5) 15 ≤ RDI < 30 (%) 14 (28.6) RDI ≥ 30 (%) 23 (46.9) HAS 6.78 ± 3.215.98 ± 3.887.43 ± 4.70 2.760.065 HDS 8.73 ± 3.707.90 ± 3.358.54 ± 4.09 0.700.497 K-SF36 PCS 66.83 ± 16.5670.12 ± 13.9262.24 ± 17.90 2.820.062 MCS 61.25 ± 15.9967.65 ± 15.6556.91 ± 20.39 4.040.019 b > c SF36 total 67.41 ± 15.7872.84 ± 14.1762.69 ± 19.27 4.520.012 b > c 1 3 450 Sleep and Biological Rhythms (2019) 17:447–454 Correlation among severity of insomnia, EDS, anxiety, depression and QoL in each group of narcoleptics, OSA‑som, and insomniacs In narcoleptic patients, EDS showed significant posi- tive correlation with severity of insomnia (r = 0.366), age (r = 0.359), BMI (r = 0.353), and anxiety (r = 0.316). Severity of insomnia showed signicant positive corre- lation with anxiety (r = 0.285), depression(r = 0.336), and age (r = 0.446). The QoL showed signicant nega- tive correlation with anxiety (r = − 0.631) and depression ( r = − 0.501), however QoL showed no signicant correla- tion with EDS and severity of insomnia (Table 2; Fig. 2). Table 2 Correlation among severity of insomnia, EDS, anxiety, depression and QoL in each group of narcoleptics, OSA with somnolence, and insomniacs BMI Body Mass Index, RDI Respiratory Disturbance Index, K-SF36 Korean version of 36-item short-form health survey, K-ISI Korean version of Insomnia Severity Index, K-ESS Korean version of Epworth Sleepiness Scale, HAS Hospital Anxiety Scale, HDS Hospital Depression Scale, MSLT multiple sleep latency test *Correlation is signicant at the 0.05 level (two-tailed) **Correlation is signicant at the 0.01 level (two-tailed) Age GenderBMIK-ISI K-ESSHAS HDSMSLT/RDI SF36 total Narcoleptics Age 1 Gender − 0.1171 BMI 0.256*− 0.472**1 K-ISI 0.446**− 0.1630.2441 K-ESS 0.359**− 0.0780.353**0.366** 1 HAS − 0.0830.0800.0930.285* 0.316*1 HDS 0.158− 0.0710.2250.336** 0.1690.408** 1 MSLT − 0.0700.140− 0.165− 0.039 − 0.126− 0.077 − 0.0721 K-SF36 total 0.113− 0.038− 0.107− 0.226 − 0.122− 0.631** − 0.501**0.108 1 OSA with somnolence Age 1 Gender 0.557**1 BMI − 0.054− 0.0401 K-ISI 0.0110.275− 0.0651 K-ESS 0.0960.1330.0880.270 1 HAS 0.0200.067− 0.131− 0.131 − 0.1471 HDS − 0.1240.1140.069− 0.114 0.0180.601** 1 RDI − 0.056− 0.007− 0.0240.106 0.1340.103 0.2641 K-SF36 total 0.041− 0.0580.075− 0.255 0.003− 0.445** − 0.571**− 0.185 1 Insomniacs Age 1 Gender 0.1271 BMI − 0.0370.0321 K-ISI 0.1400.1610.1231 K-ESS − 0.317**− 0.264*− 0.110− 0.090 1 HAS − 0.167− 0.124− 0.1400.310** 0.1581 HDS − 0.013− 0.033− 0.0110.253* 0.0720.646** 1 K-SF36 total 0.171− 0.129− 0.126− 0.540** − 0.030− 0.530** − 0.542** 1 Fig. 2 Correlation in narcoleptics 1 3 451 Sleep and Biological Rhythms (2019) 17:447–454 In OSA-som patients, there were significant positive correlation between anxiety and depression (r = 0.601). There was a negative correlation between QoL and anxiety ( r = − 0.445), and depression (r = − 0.571). There was no signicant correlation between EDS and anxiety, depression, severity of insomnia. Severity of insomnia also showed no signicant correlation with anxiety and depression (Table 2, Fig. 3). In insomniacs, there was signicant positive correlation between severity of insomnia and anxiety (r = 0.310) and depression (r = 0.253). There was a signicant negative cor – relation between severity of insomnia and QoL (r = − 0.540). There were signicant negative correlation between QoL and anxiety (r = − 0.530), depression (r = − 0.542). There was no signicant correlation between EDS and any of other variables (Table 2, Fig. 4). Predictors of QoL in narcolepsy, OSA‑som, and insomnia There was no signicant dierence between NT1 and NT2 in on the total score of the K-SF36. In order to analyze the predictors of QoL in each group, we considered age, gen – der, BMI, severity of insomnia, EDS, anxiety, and depressed mood as independent variables. In narcoleptics, anxiety and depressive mood signi- cantly aected QoL, however, anxiety (β = − 0.51) had a greater impact on the total QoL than depressive mood did ( β = − 0.29) (Table 3). Fig. 3 Correlation in OSA with somnolence Fig. 4 Correlation in insomniacs Table 3 Predictors of total QoL in narcoleptics, OSA with somnolence, and insomniacs Age, gender, BMI, K-ISI, K-ESS, HAS, and HDS as independent variables, for narcolepsy patients added MSLT, for OSA patients added RDI as independent variable Stepwise method was used for analysis BMI Body Mass Index, QoL quality of life, K-SF36 Korean version of 36-item short-form health survey, PCS physical component summary, MCS mental component summery, K-ISI Korean version of Insomnia Severity Index, K-ESS Korean version of Epworth Sleepiness Scale, HAS Hospital Anxiety Scale, HDS Hospital Depression Scale, MSLT multiple sleep latency test, RDI Respiratory Disturbance Index BS.Eβ R 2 change tp VIF Narcoleptics Constant 95.334.22 22.54< 0.001 HAS − 2.510.50− 0.51 0.399 − 4.97< 0.001 1.20 HDS − 1.240.43− 0.29 0.071 − 2.830.006 1.20 R 2 = 0.470, F = 26.58, p < 0.001 OSA with somnolence Constant 102.005.51 18.50< 0.001 HDS − 2.350.43− 0.60 0.326 − 5.420.001 1.20 K-ISI − 0.930.32− 0.32 0.104 − 2.890.006 1.20 R 2 = 0.430, F = 17.32, p < 0.001 Insomniacs Constant 107.265.66 18.94< 0.001 HDS − 1.440.48− 0.30 0.294 − 2.990.004 1.72 K-ISI − 1.330.27− 0.39 0.174 − 4.82< 0.001 1.11 Age 0.350.110.23 0.052 0.2990.004 1.02 R 2 = 0.519, F = 29.91, p < 0.001 1 3 452 Sleep and Biological Rhythms (2019) 17:447–454 In OSA-som patients, QoL was signicantly aected by depressive mood and severity of insomnia, and the depres- sive mood ( β = − 0.60) had greater impact on QoL than insomnia (β = − 0.32) did (Table 3). In insomniacs, QoL were signicantly aected by depres – sive mood, the severity of insomnia, and age but the sever – ity of insomnia ( β = − 0.39) had a greater impact on QoL than depressive mood (β = − 0.30) and age (β = 0.23) did (Table 3). Discussion The mean age of diagnosis for patients with narcolepsy was 27.03 (± 9.29) which was less than that of OSA patients, and insomniacs. This is consistent with the fact that narcolepsy starts at an early age [22– 24] while OSA and insomnia often start later in life. We found gender dierences in narcolepsy i.e., predominantly aecting the male. This is in contrast to most reports indicating no signicant gender dierence in narcolepsy [6 , 11, 25], however it is supported by a previous study reporting 78% male dominance in narcolepsy [26]. We found gender dierences in OSA and insomnia both consist- ent with previous studies reporting more than 70% of OSA patients being males [27, 28], and females being 1.6 times more likely than men to develop insomnia [28– 30]. Narcoleptics showed more severe insomnia than seen in OSA patients although this dierence did not reach statisti- cal signicance. Compared to insomniacs, both narcoleptics and OSA patients showed signicantly less severe insom- nia than insomniacs, however the mean score of severity of insomnia in narcoleptics (12.75) was higher than the cut o threshold (7) indicating subthreshold insomnia [20]. Previ- ous studies have reported that improving nocturnal sleep in narcolepsy would increase daytime alertness [31, 32]. There- fore, nocturnal sleep disturbance in patients with narcolepsy may well impact their QoL, although the eect may not be as severe as in insomniacs. This study showed that narcoleptics have signicantly more EDS than patients with OSA-som and insomniacs do. Consistent with previous reports, we found EDS a common complaint in sleep disordered patients [33], however, we identied EDS as a symptom associated with poor QoL and worse psychological parameters in narcoleptics [34, 35]. For further investigation, we compared NT1 and NT2. Fifty-two percent of narcoleptics in our study had NT1. We found that the type of narcolepsy did not have a signicant impact on QoL. It has been reported that narcolepsy symp- tomatology (narcolepsy with cataplexy-like symptoms, hyp- nagogic or hypnopompic hallucination, and sleep paralysis) is associated with poor QoL, symptoms of depression, and anxiety [34], yet our ndings indicated that cataplexy-like symptoms have no signicant eect on QoL. Previous studies have also associated QoL of narcoleptics with the degree of EDS and psychological variables such as depressive mood [7 , 26, 36]. We found that both anxiety and depression have a negative impact on QoL in narcolep- tics, but the total QoL were more aected by anxiety than depressive mood. The EDS did not have a signicant impact on QoL, contrary to other studies reporting a signicant association between EDS and psychiatric symptoms such as depression and anxiety [34, 37]. Although EDS did not impact QoL, it showed signicant correlation with anxiety. Also, nocturnal sleep disturbance was not a factor impacting QoL, nocturnal sleep disturbance showed signicant correla- tion with anxiety, depression, and EDS. It is possible that QoL is indirectly aected by both EDS and nocturnal sleep disturbance through psychiatric factors as a downstream eect. Overall, we found anxiety the most impactful fac- tor in QoL of narcoleptics. A previous study reported more that anxiety than depressive mood in narcolepsy patients. Additionally, many patients reported noticeable impairment in daily functioning due to anxiety and mood problems. We suggest that physicians consider this notion along with the sleep symptoms [38]. There were some dierences between narcolepsy patients and those with OSA-som, as well as between narcolepsy and insomnia patients. In both OSA-som and insomnia groups, the QoL was aected by severity of insomnia and depres- sive mood, however EDS did not correlate with severity of insomnia or any psychiatric factors. Some studies have reported coexisting with OSA as a contributing factor to EDS with negative inuence on QoL [39]. However, our ndings did not support these reports. In our results, the OSA-som was dened by K-ESS scale, so it should inuence the correlation between EDS and QoL. On the other hand, other study has reported a strong correlation between depres- sive mood and low QoL, although EDS might have a small eect on QoL [22]. That ndings may partially support our results. In OSA-som patients, the severity of insomnia did not show a signicant correlation with QoL, however in fac- tor analysis, controlling the eect of depression, severity of insomnia was included as one of the eect factors, and depressive mood seemed to aect QoL the most. Previous studies have reported that men with comorbid OSA and insomnia have greater prevalence and severity of depression, and lower QoL [40, 41] which is in line with our results. In addition, other studies reported that anxiety had strong cor – relation with depression, and was more common in female patients [42]. Our study only included six females, so we were unable to conrm these particular correlations. Further research is needed to determine the signicance of anxiety as an inuence on QoL. In current study the RDI did not show any correlation with QoL. This was supported by pre- vious studies which found no correlation between AHI and QoL and concluded that the eect of OSA on QoL cannot 1 3 453 Sleep and Biological Rhythms (2019) 17:447–454 be explored with PSG or by utilizing commonly used QoL questionnaires [10, 43]. In insomniacs, there were signicant correlations among severity of insomnia, depressive mood, anxiety, and QoL. The factors impacting QoL were the severity of insomnia and depressive mood, with the severity of insomnia having the higher impact (β = − 0.39, vs β = − 0.30). In patients with insomnia, the severity of insomnia has been associated with psychiatric symptoms [13, 44], and QoL [45]. Our ndings could be supported by these data. In insomniacs, EDS did not show any correlation with severity of insomnia, psychi- atric factors, and QoL. In contrast to our results, some of previous studies have reported that EDS could be considered secondary to chronic insomnia and depression [46]. Also, EDS would correlate with lower QoL and depressive mood [ 47, 48]. The signicance of this study was to compare the factors inuencing the QoL of the narcolepsy group against the two groups each consisting of OSA-som and insomnia, in addi- tion to comparing NT1 and NT2 with each other. Thus, we investigated the dierence in the degrees of inuence fac- tors, such as EDS, sleep disturbance, cataplexy symptoms, and mood disturbance (depression and anxiety) on QoL. There were several limitations of our study. The rst is its retrospective design in addition to the data having been obtained from a pool of patients referred to a tertiary sleep center. We could not control nor include other variables that may have an eect on QoL. In addition, the diagno- ses of NT1 were merely based on patient reports with no CSF hypocretin (orexin) levels checked. Third, the OSA patients were rst diagnosed with OSA using PSG. A patient interview and sleep questionnaire were also given, which eliminated the need for further testing for comorbidities such as NT2, idiopathic hypersomnia, or insu cient sleep syndrome. Also, although this is a single center study, the results are not signicantly dierent from those reported by multicenter studies. In conclusion, the QoL of the total narcolepsy group was comparable to the OSA-som and insomnia groups. There are dierent inuential factors involved in QoL of narcolep- tics, OSA-som patients, and insomniacs. The EDS did not directly aect QoL, in fact, the most inuential factor in QoL in narcoleptics was anxiety while EDS showed a signicant correlation with anxiety. In OSA-som patients, the most sig- nicant factor impacting QoL was depressive mood, while for the insomniacs, it was severity of insomnia. Although, there were some dierences in the degree of eect, ulti- mately the QoL was aected most by psychiatric variables, directly or indirectly. Therefore, to improve the QoL in these patients, pharmacological or non-pharmacological psychiatric treatments should be considered. In particular, patients with narcolepsy, should prioritize anxiety manage- ment for the purpose of improving QoL. It should be noted that anxiety in narcolepsy patients may also be reduced as a result of narcolepsy treatment. Further research is needed to evaluate the eect of narcolepsy treatment on anxiety levels. Funding No funding was received in this study. Compliance with ethical standards Conflict of interest None of the authors have potential conicts of in- terest to be disclosed. Ethical standards This study was approved by the institutional review board of a regional university hospital and patient consent was exempt due to the retrospective nature of the study (#2017-02-009). 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European Archives of Psychiatry and Clinical Neuroscience https://doi.org/10.1007/s00406-022-01485-7 ORIGINAL PAPER Distinct functional brain abnormalities in insomnia disorder and obstructive sleep apnea Weiwei Duan 1 · Xia Liu 2 · Liangliang Ping 3 · Shushu Jin 4 · Hao Yu 1 · Man Dong 1 · Fangfang Xu 1 · Na Li 1 · Ying Li 1 · Yinghong Xu 1 · Zhe Ji 1 · Yuqi Cheng 5 · Xiufeng Xu 5 · Cong Zhou 1,4 Received: 24 April 2022 / Accepted: 29 August 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany 2022 Abstract Insomnia disorder (ID) and obstructive sleep apnea (OSA) are the two most prevalent sleep disorders worldwide, but the pathological mechanism has not been fully understood. Functional neuroimaging ndings indicated regional abnormal neural activities existed in both diseases, but the results were inconsistent. This meta-analysis aimed to explore concordant regional functional brain changes in ID and OSA, respectively. We conducted a coordinate-based meta-analysis (CBMA) of resting-state functional magnetic resonance imaging (rs-fMRI) studies using the anisotropic eect-size seed‐ based d mapping (AES-SDM) approach. Studies that applied regional homogeneity (ReHo), amplitude of low-frequency uctuations (ALFF) or fractional ALFF (fALFF) to analyze regional spontaneous brain activities in ID or OSA were included. Meta-regressions were then applied to investigate potential associations between demographic variables and regional neural activity alterations. Signicantly increased brain activities in the left superior temporal gyrus (STG.L) and right superior longitudinal fasciculus (SLF.R), as well as decreased brain activities in several right cerebral hemisphere areas were identied in ID patients. As for OSA patients, more distinct and complicated functional activation alterations were identied. Several neuroimaging alterations were functionally correlated with mean age, duration or illness severity in two patients groups revealed by meta- regressions. These functionally altered areas could be served as potential targets for non-invasive brain stimulation methods. This present meta-analysis distinguished distinct brain function changes in ID and OSA, improving our knowledge of the neuropathological mechanism of these two most common sleep disturbances, and also provided potential orientations for future clinical applications. Registration number: CRD42022301938. Keywords Insomnia disorder · Obstructive sleep apnea · Resting-state fMRI · Neuroimaging · Meta-analysis Introduction Insomnia disorder (ID) and obstructive sleep apnea (OSA) are the two most prevalent sleep disorders worldwide [1 ]. The former implicates a perceived diculty in falling or staying asleep and obtaining refreshing sleep, as well as early morning awakening [2 , 3], while the latter is a com- mon chronic sleep-related breathing disorder, characterized by repeated complete or partial collapse and obstructions of the upper airway, leading to recurrent intermittent hypoxia, hypercapnia, and sleep frequent awakening [4 , 5 ]. The preva- lence of ID in the worldwide population ranges from 4 to 22% [6 , 7]. The disease strongly aects patients’ regular statues, which may reduce the eciency of daily work and increase the risk of road and motor vehicle accidents [8 ]. On the other hand, the prevalence of OSA is noticeable in * Cong Zhou [email protected] 1 School of Mental Health, Jining Medical University, Jining, China 2 Department of Sleep Medicine, Shandong Daizhuang Hospital, Jining, China 3 Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China 4 Department of Psychology, Aliated Hospital of Jining Medical University, Jining, China 5 Department of Psychiatry, The First Aliated Hospital of Kunming Medical University, Kunming, China Vol.:(0123456789) 1 3 European Archives of Psychiatry and Clinical Neuroscience general population and around 50% in patients with cardio- vascular or metabolic disorders [4 , 9]. Though the clinical manifestations of these two diseases are distinct, they both interfere with the quality of life of the patients. With condi- tions continuing, patients with ID and OSA present high comorbidity with aective disorders and emotional dysregu- lation [10– 13]. Specially, dierent types of sleep disorders and non-sleep circadian disorders were proven to be risk fac- tors of subsequent depression [14]. In addition, sleep disor – ders are closely related to airway diseases. Airway diseases such as obstructive sleep apnea syndrome (OSA) can disturb sleep structure, reduce sleep quality, and induce refractory insomnia. OSA also contributes to cognitive decline, and there is increasingly evidence showing OSA to be one of the rare modiable risk factors for neurodegenerative dementia [ 4 ]. Even brief disturbances in sleep can have a lasting eect on the internal activity and reactivity during waking [1 ]. Long-time sleep disturbances will further aect brain func- tions of patients with either ID or OSA. Advances in neuro- imaging techniques allow researchers to visualize and inves- tigate brain activities with non-invasive means, among them is the resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI approach measures blood oxygen- level-dependent (BOLD) signals to reect the spontane- ous uctuations during neural activity in resting state [15], and has been widely applied in neuropsychiatric disorders to enhance a better understanding of the pathophysiology and potential mechanisms of the diseases [16]. Although the design of rs-fMRI research is similar in essence, the analysis methods for processing rs-fMRI data are diverse, mainly consist of seed-based functional connectivity (FC), independent component analysis (ICA), graph theory and regional spontaneous brain activity analysis [17]. In terms of the last one, regional homogeneity (ReHo), amplitude of low-frequency uctuations (ALFF), fractional ALFF (fALFF) are three widely used methods for characterizing local spontaneous activity of rs-fMRI data. ReHo measures the local synchronization of the time series of neighboring voxels, whereas ALFF/fALFF measures the amplitude of time series uctuations at each voxel [18]. Commonalities and dierences exist in these metrics, which provide sup – plementary information to improve  the understanding of regional spontaneous brain activities [19]. Previous electro- encephalogram (EEG) studies have revealed functional brain dynamics vary in ID and OSA [20– 22], fMRI could provide more insights into the neurological function characteristic in these two sleep disturbances. For now, a number of rs-fMRI studies explored brain function characteristics in both ID and OSA, but the results are complex and inconsistent. These two sleep disorders pos- sess their own clinical characteristics, and also have distinct neurophysiological and social bases. However, nowadays, research has found similar or disparate neuroimaging changes involved with sleep and arousal in ID and OSA. Most of the reported brain areas involved in sleep-wakeful- ness or even cognitive processing [23– 25]. The variability of the ndings might attribute to relatively small sample sizes, heterogeneous patient groups that diered in demo- graphic characteristics, and use of diverse methodologi- cal techniques across studies. Meta-analysis is a powerful method to synthesize neuroimaging ndings from dierent studies in a comprehensive way, which helps to overcome the discrepancies of regional alterations among various neu- roimaging studies [26]. This method can also distinguish false results from replicable ndings, and summarize and integrate a large amount of data across studies [27]. Besides, progresses in neuroimaging meta-analytic methodology have made it possible to correlate imaging results with clinical characteristics [28]. The anisotropic eect-size seed‐ based d mapping (AES-SDM) is an advanced statistical technique for coordinate‐based meta‐analysis (CBMA) to attain a syn- optic view of distributed neuroimaging ndings and dierent neuroimaging methods (e.g., structural and functional) in an objective and quantitative fashion [29]. The strengths of AES‐ SDM has been summarize elsewhere [29– 33]. To date, a few research performed meta-analysis on fMRI studies of ID and OSA. One activation likelihood estimation (ALE) meta-analysis [34] found no signicant convergent evidence for functional disturbances in ID across previ- ous studies. This study took rs-fMRI, task-fMRI, as well as positron emission tomography (PET) studies together in the meta-analysis. The methodological heterogeneity might lead to the lack of consistent brain alterations in ID. By comparison, another AES-SDM meta-analysis [35] con- centrated on rs-fMRI (including FC, ALFF, ReHo and ICA) and contained articles written in English and other language (Chinese). This study found that patients with persistent ID exhibited over activations in right parahippocampal gyrus (PHG.R) and left median cingulate/paracingulate gyri, together with weakened activities in right cerebellum and left superior frontal gyrus/medial orbital. The lately ALE meta-analysis [8 ] explored both structural and functional brain changes in ID, but distinguished ALFF and ReHo stud- ies, and analyzed these two measures separately without any pooled meta-analysis. One ALE meta-analysis on OSA [4 ] investigated structural and functional neural adaptations. Convergent evidence for structural atrophy and functional disturbances in the right basolateral amygdala/hippocampus and the right central insula were identied in this study. This meta-analysis was a relatively comprehensive research, but was conducted in about 6 years ago, which is surely in need of updating. In the field of exploring the consistent alteration of regional spontaneous brain activities caused by diseases, CBMA containing ALFF, fALFF and ReHo has been 1 3 European Archives of Psychiatry and Clinical Neuroscience applied in major depression disorder (MDD) [36], Parkin- son’s disease [37], type 2 diabetes mellitus (T2DM) [38] and anxiety disorders [17]. In our present study, we aimed to perform a CBMA of rs-fMRI studies which utilized ALFF, fALFF or ReHo in ID and OSA, so as to detect the common and distinct neurophysiological mechanisms of these two diseases for a comparative view. Moreover, we intended to explore the potential eects of demographics and clinical characteristics including mean age, duration of disease, and severity of illness on brain functions using meta-regression approach, which we hope would bring some inspirations for future clinical diagnoses and treatments of ID and OSA. Methods Literature search strategy We performed this meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [39– 41]. The protocol of this CBMA was registered at PROSPERO (http:// www. crd. york. ac. uk/ PROSP ERO) (registration number: CRD42022301938). Relevant literatures were acquired using systematic and comprehensive searches from the PubMed, ScienceDirect and Web of Science databases published (or “in press”) up to December 31, 2021. The search keywords were (“insomnia” OR “insomnia disorder”) and ((“functional magnetic reso- nance imaging” or “fMRI”) or (“amplitude of low frequency uctuation” or “fractional amplitude of low frequency uc- tuation” or “ALFF” or “fALFF”) or (“regional homogeneity” or “ReHo” or “local connectivity” or “coherence”)) for ID, and (“obstructive sleep apnea” or “OSA” or “obstructive sleep apnea syndrome” or “OSAS” or “obstructive sleep apnea–hypopnea syndrome” or “OSAHS”) and ((“functional magnetic resonance imaging” or “fMRI”) or (“amplitude of low frequency uctuation” or “fractional amplitude of low frequency uctuation” or “ALFF” or “fALFF”) or (“regional homogeneity” or “ReHo” or “local connectivity” or “coher – ence”)) for OSA, respectively. Additionally, the reference lists of identied studies and relevant reviews were manually checked to avoid omitting. Study selection The common inclusion criteria were: (1) studies compared ReHo, ALFF or fALFF value dierences between patients and HC in whole-brain analyses; (2) reported results in Talairach or Montreal Neurological Institute (MNI) coor – dinates; (3) used a threshold for signicance; (4) articles written in the English language and published in peer- reviewed journals. Exclusion criteria were: (1) meta-analy – sis, reviews, case reports or tractography-based only study; (2) studies with no direct between-group comparison; (3) studies from which peak coordinates or parametric maps were unavailable. Quality assessment and data extraction Two authors (D.W. and L.X.) independently searched the literatures, assessed the quality of the retrieved articles, extracted, and cross-checked the data from eligible arti- cles. The quality of the nal studies was also independently checked by both authors following guidelines for neuroimag- ing meta-analyses promoted by Müller and colleagues [27]. For both diseases, the following data were recorded: rst author, cohort size, demographics (age and gender), illness duration, imaging parameters, data processing method, as well as statistical threshold in each study. For ID studies, Pittsburgh Sleep Quality Index (PSQI) and Insomnia Sever – ity Index (ISI) scores were extracted, and for OSA studies, we specially recorded Apnea–Hypopnea Index (AHI) scores, Epworth Sleepiness Scale (ESS) scores and BMI. Meta‑analyses Regional brain activity dierences between patients and HC were performed using the SDM software v5.15 (http:// www. sdmpr oject. com) [30, 42] in a voxel-based meta-anal- ysis approach. We conducted the analysis according to the SDM tutorial and previous meta-analytic studies. The AES- SDM technique is a powerful statistical technique that uses peak coordinates for meta-analysis to assess dierences in brain activity [29]. The AES-SDM procedures have been described in detail elsewhere [17, 26, 35, 43– 45], and were briey summarized as below: (1) the software recreated the eect-size maps of dierences in regional activities between patients and HC for each study based on the peak coordi- nates of the eects and statistics the level of t -statistics (Z- or P -values for signicant clusters which were then converted to t-statistics using the SDM online converter); (2) the peak coordinates for each study were recreated using a standard MNI map of the eect size of the group dierences in neu- roimaging by means of an anisotropic Gaussian kernel [31]. Both positive and negative coordinates were reconstructed in the same map [30]; (3) the standard meta-analysis was conducted to create a mean map via voxel-wise calculation of the random-eects mean of the study maps. According to the inventors of the AES-SDM algorithm, the default AES-SDM threshold uncorrected P = 0.005 is approximately equivalent to a corrected P = 0.025 [29]. Here, a more stringent thresholds were applied for both ID and OSA analyses with an uncorrected P value = 0.0025, representing the multiple comparison correction for two dis- eases (P = 0.005/2 = 0.0025), which is consistent with pre- vious studies [43, 45]. Other parameters included the peak 1 3 European Archives of Psychiatry and Clinical Neuroscience height threshold Z = 1.00 and cluster size threshold = 100 voxels. Sensitivity analyses To assess the replicability of the results, we performed a sys- tematic whole-brain voxel-based jackknife sensitivity analy – sis. This procedure involved repeating the main statistical analysis for each result N times (N represents the number of datasets in each meta-analysis), discarding a dierent study each time. If a brain region remains signicant after run – ning jackknife sensitivity in all or most of the combinations of studies, the nding is considered highly replicable [30]. Subgroup meta‐analyses In the present investigation, we initially conducted pooled meta-analysis of all the included studies for each disease. Subsequently, we performed subgroup meta‐ analyses, which only included methodologically homogenous studies so as to minimize the inuence of any potential methodological dif- ferences among individual studies. Specically, we planned to conduct subgroup meta‐ analyses of ReHo studies as well as ALFF/fALFF studies in each disease separately. Meta‑regression analyses Considering the potential associations between demo – graphic variables and neuroimaging changes, meta-regres – sion analyses were performed in each patient group. A more conservative threshold (P < 0.0005) was adopted in consistent with previous meta-analyses and the recom- mendations of the AES-SDM authors [30], and only brain regions identied in the main eect were considered. Results Included studies and sample characteristics Figure  1 presents the ow diagram of the identication and the attributes of the studies in ID and OSA. The demo – graphics and neuroimaging approaches of the samples for each disease are summarized in Table 1 and Table 2 , respectively. For the meta-analysis of ID, the search strategy identied 155 studies, 9 of which met the inclu- sion criteria [23, 46–53]. The study by Wang et al. [51] contained two randomly selected datasets of insomnia patients. The nal sample of ID comprised 316 insomnia patients and 291 HC, along with 49 coordinates extracted from 10 datasets. For the meta-analysis of OSA, a total of 241 studies were identied according to the search strat- egy, and 10 of them met our inclusion criteria [24, 25, 54– 61]. Among them, three studies [24, 57, 58] contained both ALFF and ReHo analyses, and one study [60] con- tained ALFF, fAFLL and ReHo analyses. We treated these analyses as independent datasets. The nal sample of OSA comprised 376 OSA patients and 397 HC, along with 81 coordinates extracted from 15 datasets. Fig. 1 Flow diagram for the identication and exclusion of studies 1 3 European Archives of Psychiatry and Clinical Neuroscience Results of the pooled meta‑analyses fMRI in ID The pooled meta-analysis revealed that compared with HC, ID patients exhibited signicant increased brain activities in two clusters including the left superior temporal gyrus (STG.L, BA 48), right superior longitudinal fasciculus (SLF.R) II, as well as three clusters with decreased brain activities including the right hemispheric lobule IX, right median cingulate/paracingulate gyri and right inferior fron- tal gyrus (IFG.R, opercular part, BA 48). The results are illustrated in Fig.  2A and Table  3. fMRI in OSA Increased functional activations were found in four clusters in OSA patients relative to controls, locating in the right median cingulate/paracingulate gyri, right lenticular nucleus (putamen, BA 48), left parahippocampal gyrus (PHG.L, BA 36) and corpus callosum (CC), together with decreased acti- vations in three clusters including the left calcarine ssure/ surrounding cortex (BA 17), right superior frontal gyrus (SFG.R, dorsolateral, BA 10) and left middle frontal gyrus (MFG.L, BA 46). See Fig.  2B and Table  3. Sensitivity analysis The whole-brain jackknife sensitivity analyses revealed that the results were highly replicable, as decreased brain activi- ties in right cerebellum in ID and increased brain activities in the right median cingulate/paracingulate gyri in OSA remained signicant throughout all but 1 combination of the datasets. The remaining resultant clusters remained sig – nicant in all but 2 or 3 combinations of datasets, except the result of decreased MFG.L functions in OSA remaining signicant in all but 4 combinations of datasets. The details are shown in Table  3. Subgroup analysis Detailed results of heterogeneous methodologies (ReHo or ALFF/fALFF studies) on each disease are presented in Table S1 in the Appendix A. Supplementary data. The results of dierent subgroups were highly consistent with the pooled meta-analysis ndings, but the signicant cluster numbers were a little bit less, which might be related with the statistical eects. Besides, distinct results were found between ReHo and ALFF/fALFF studies, which might be due to the methodological heterogeneity. Table 1 Demographic and clinical characteristics and the neuroimaging approaches of the participants in the 9 studies (10 datasets) included in the meta-analysis of ID ALFF low-frequency uctuation, BA Brodmann area, fALFF fractional amplitude of low-frequency uctuations, FDR false discovery rate, FWE family-wise error, HC healthy controls, ID insomnia disorder, ISI Insomnia Severity Index, N/A not available, PCC posterior cingulate cortex, PSQI Pittsburgh Sleep Quality Index, ReHo regional homogeneity Study Subjects, n (female, n) Age, years Duration, years PSQI scoreISI scoreScannerType of analysis Statistical threshold Number of coordinates ID HCIDHC (Dai et al., 2014) 24 (17)24 (12) 54.852.56.0 15.619.33.0 TReHo P < 0.01, AlphaSim corrected 3 (Wang et al., 2016) 59 (38)47 (33) 39.340.0N/A 12.4N/A1.5 TReHo P < 0.05, AlphaSim corrected 7 (Dai et al., 2016) 42 (27)42 (24) 49.249.15.44 15.218.43.0 TALFF P < 0.01, AlphaSim corrected 4 (Li et al., 2016) 55 (31)44 (33) 39.239.93.8 12.519.71.5 TALFF P < 0.01, AlphaSim corrected 6 (Ran et al., 2017) 21 (16)20 (14) 40.638.7N/A 13.3N/AN/AALFF N/A 5 (Wang et al., 2020)a 15 (10)15 (8) 48.445.5N/A N/AN/A3.0 TfALFF P < 0.001, FWE corrected 7 (Wang et al., 2020)b 15 (9)15 (8) 49.745.5N/A N/AN/A3.0 TfALFF P < 0.001, FWE corrected 9 (Zhao et al., 2020) 22(13)20(12) 42.636.2N/A 12.4N/A3.0 TALFF P < 0.01, AlphaSim corrected 1 (Zhang et al., 2021) 32(20)34(21) 37.535.8N/A 12.0N/A3.0 TReHo P < 0.05, AlphaSim corrected 4 (Feng et al., 2021) 31 (8)30 (10) 44.842.3N/A 13.919.43.0 TReHo P < 0.05, FDR corrected 3 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 2 Demographic and clinical characteristics and the neuroimaging approaches of the participants in the 10 studies (15 datasets) included in the meta-analysis of OSA ALFF low-frequency uctuation, AHI apnea–hypopnea index, BMI body mass index, ESS Epworth Sleepiness Scale, fALFF fractional amplitude of low-frequency uctuations, FDR false dis- covery rate, GRF Gaussian random eld, HC healthy controls, N/A not available, OSA obstructive sleep apnea, ReHo regional homogeneity Study Subjects, n (female, n) Age, years Duration of disease, years AHI, per hour ESSBMI kg/m 2 Scanner Type of analysis Statistical threshold Number of coordinates OSA HCOSAHC (Santarnecchi et al., 2013) 19 (3)19 (5) 43.2416.5 36.3 14.430.3 1.5 T ReHo P < 0.05, FDR corrected 16 (Peng et al., 2014) 25 (0)25(0) 39.439.5 – 60 15.227.8 3 T ReHo P < 0.001, FDR orrected 8 (Li et al., 2015) 25 (0)25 (0) 39.439.5 – 60 15.227.8 3 T ALFF P < 0.05, FDR orrected 2 (Kang et al., 2020) 14 (0)16 (0) 48.744.8 – 28.9 –27.4 3 T ALFF P < 0.05, AlphaSim corrected 5 (Kang et al., 2020) 14 (0)16 (0) 48.744.8 – 28.9 –27.4 3 T ReHo P < 0.05, AlphaSim corrected 7 (Qin et al., 2020) 36 (0)38 (0) 48.546.1 – 58.8 –29.0 3 T ALFF P < 0.001, AlphaSim corrected 10 (Qin et al., 2020) 36 (0)38 (0) 48.546.1 – 58.8 –29.0 3 T ReHo P < 0.001, AlphaSim corrected 8 (Zhou et al., 2020) 33 (3)22 (4) 43.639.7 – 57.9 14.429.1 3 T ReHo P < 0.05, GRF corrected 4 (Ji et al., 2021) 20 (8)29 (17) 7.27.7 – 16.5 –19.2 3 T ALFF P < 0.05, AlphaSim corrected 2 (Ji et al., 2021) 20 (8)29 (17) 7.27.7 – 16.5 –19.2 3 T ReHo P < 0.05, AlphaSim corrected 3 (Bai et al., 2021) 31 (12)33 (16) 5.76.0 1.8 12.9 –18.4 3 T ALFF P < 0.001, GRF corrected 1 (Bai et al., 2021) 31 (12)33 (16) 5.76.0 1.8 12.9 –18.4 3 T fALFF P < 0.001, GRF corrected 2 (Bai et al., 2021) 31 (12)33 (16) 5.76.0 1.8 12.9 –18.4 3 T ReHo, P < 0.001, GRF corrected 2 (Santarnecchi et al., 2021) 20(3)20(4) 42.9416.9 38.3 13.829.5 N/A fALFF P < 0.05, Monte Carlo corrected 7 (Li et al., 2021) 21(1)21(1) 40.140.1 – 48.4 10.827.3 3.T ReHo P < 0.01, GRF corrected 4 1 3 European Archives of Psychiatry and Clinical Neuroscience Meta‑regression analysis In ID group, the meta-regression analysis found a positive correlation between brain function alterations in SLF.R II and the mean age as well as the PSQI of the patients, along with a negative correlation between brain function altera- tions in the right cerebellum (hemispheric lobule IX) and the illness duration. In OSA patients, the mean age of the patients was sig- nificantly and positively correlated with brain function alterations in the right median cingulate/paracingulate gyri, PHG.L, and CC. The AHI was positively correlated with brain function alterations in PHG.L and CC, and negatively correlated with brain function alterations in the SFG.R. The BMI impacted brain activities the most, with a positive correlation with brain function alterations in right median cingulate/paracingulate gyri, PHG.L and CC, as well as a negative correlation with SFG.R. The details are shown in Table  4. Discussion To our knowledge, this study is the rst CBMA of rs-fMRI studies investigating regional spontaneous neural activity abnormalities in ID and OSA simultaneously. Unlike some previous meta-analyses, this whole-brain meta-analysis excluded the inuence of treatment and external tasks to purely reect intrinsic brain activity, and might provide more reliable information on the neural patterns and their potential roles in the pathophysiology of ID and OSA. Our pooled meta-analysis results showed increased brain activi- ties in the STG.L, SLF.R, and decreased brain activities in the right cerebellum, right median cingulate/paracingulate gyri and IFG.R when comparing ID patients with HC. When conducting comparisons between OSA patients and HC, increased functional activations in the right median cingu- late/paracingulate gyri, right lenticular nucleus, PHG.L and CC, and decreased activations in the left calcarine ssure/ surrounding cortex, SFG.R and MFG.L were identied. Our current ndings indicated complexed resting-state dysfunc- tions in these two sleep disorders, and were mostly consist- ent with previous meta-analyses [ 4, 8 , 35 ], but distinct neural activity alterations existed between ID and OSA. ID patients demonstrated increased brain activities in the STG.L and SLF.R. Hyperactive fMRI signals might be coin- cided with the hyperarousal model of insomnia [62], reect- ing a sleep–wake dysregulation. The STG is a vital compo- nent of the default mode network (DMN), which is believed to be related with interplaying between attention orientation and default mode processing, and are associated with dis- rupted switching between resting and task-context process- ing [63]. Evidence has shown that sleep deprivation, which might occur in insomnia, leads to aberrant stability and func- tion of the DMN [1 ]. The ndings of another study sug- gested that sleep disturbances were associated with greater Fig. 2 Meta-analysis of regional abnormal resting-state brain activi- ties in (A) ID and (B) OSA. Signicant clusters are overlaid on MRI- cron template for Windows for display purposes only. CC corpus callosum, ID insomnia disorder, IFG.R right inferior frontal gyrus, MFG.L left middle frontal gyrus, OSA obstructive sleep apnea, PHG.L left parahippocampal gyrus, SFG.R right superior frontal gyrus, SLF.R II right superior longitudinal fasciculus II, STG.L left superior temporal gyrus 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 Regional functional brain abnormalities in ID patients and OSA patients compared to HC in the pooled meta-analysis Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ ID vs HC ID > HC Left superior temporal gyrus, BA 48 − 38 − 6− 12 1.621 0.000159979 211Left insula, BA 48 (98) Left superior temporal gyrus, BA 48 (80) Left lenticular nucleus, putamen, BA 48 (13) Left inferior network, inferior fronto-occipital fasciculus (10) Left inferior network, uncinate fasciculus (5) BA 20 (4) Left striatum (1) 8/10 Right superior longitudinal fasciculus II 32 − 1654 1.628 0.000144482 172Right precentral gyrus, BA 6 (50) Right frontal superior longitudi- nal (41) Right superior longitudinal fas- ciculus II (30) Right superior frontal gyrus, dorsolateral, BA 6 (25) Right precentral gyrus, BA 4 (16) Corpus callosum (10) 8/10 ID < HC Right cerebellum, hemispheric lobule IX 10 − 58− 42 − 2.036 0.000206411 672Right cerebellum, hemispheric lobule VIII (335) hemispheric lobule IX (145) Right cerebellum, undened (142) Cerebellum, vermic lobule VIII (26) Right cerebellum, hemispheric lobule VIIB (13) Cerebellum, vermic lobule IX (11) 9/10 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ Right median cingulate/paracin- gulate gyri 4 − 3644 − 1.756 0.001326323 191Right median cingulate/paracin- gulate gyri, BA 23 (94) Left median cingulate/paracingu- late gyri, BA 23 (36) Right median cingulate/paracin- gulate gyri (34) Left median cingulate/paracingu- late gyri (16) Right median network, cingulum (11) 7/10 Right inferior frontal gyrus, opercular part, BA 48 54 108 − 1.774 0.001171529 163Right inferior frontal gyrus, oper – cular part, BA 48 (49) Right rolandic operculum, BA 48 (48) Right inferior frontal gyrus, oper – cular part, BA 44 (28) Right insula, BA 48 (15) Right inferior frontal gyrus, opercular part, BA 6 (8) Right frontal aslant tract (7) Right rolandic operculum, BA 6 (3) Right inferior frontal gyrus, opercular part (3) Right insula (1) Right fronto-insular tract 4 (1) 7/10 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ OSA vs HC OSA > HC Right median cingulate/paracin- gulate gyri 4 − 232 2.545 ~ 0 1534Left median cingulate/paracingu- late gyri, BA 24 (245) Right median cingulate/paracin- gulate gyri, BA 24 (227) Left median cingulate/paracingu- late gyri (210) Right median cingulate/paracin- gulate gyri, BA 23 (140) Left median cingulate/paracingu- late gyri, BA 23 (133) Left median network, cingulum (103) Right median cingulate/paracin- gulate gyri (90) Right median network, cingulum (83) Corpus callosum (77) Right median cingulate/paracin- gulate gyri, BA 32 (63) Right anterior cingulate/paracin- gulate gyri, BA 24 (59) Right anterior cingulate/paracin- gulate gyri (24) Left supplementary motor area (21) Left median cingulate/paracingu- late gyri, BA 32 (14) Right supplementary motor area (9) Left superior frontal gyrus, medial, BA 32 (9) Right supplementary motor area, BA 24 (4) Left superior frontal gyrus, medial (5) Left supplementary motor area, BA 23 (3) (undened) (15) 14/15 Right lenticular nucleus, puta- men, BA 48 32 14− 2 1.643 0.000340641 434Right lenticular nucleus, puta- men, BA 48 (222) Right insula, BA 47 (91) Right insula, BA 48 (75) Right striatum (26) Right lenticular nucleus, putamen (14) Right insula (3) Right lenticular nucleus, puta- men, BA 47 (2) Right inferior network, inferior fronto-occipital fasciculus (1) 13/15 1 3 European Archives of Psychiatry and Clinical Neuroscience Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ Left parahippocampal gyrus, BA 36 − 18 − 14− 30 1.965 0.000020623 379Left parahippocampal gyrus, BA 35 (96) Left median network, cingulum (71) Left parahippocampal gyrus, BA 36 (57) Left parahippocampal gyrus, BA 30 (51) Left pons (30) Left hippocampus, BA 35 (9) Left hippocampus (7) Left fusiform gyrus, BA 36 (6) Left fusiform gyrus, BA 30 (5) Left cerebellum, hemispheric lobule III (2) Left cerebellum, hemispheric lobule IV/V (1) Left hippocampus, BA 30 (1) Left fusiform gyrus (1) (undened) (42) 13/15 Corpus callosum − 102248 2.003 0.000020623 239Corpus callosum (102) Left superior frontal gyrus, dor – solateral, BA 8 (58) Left superior frontal gyrus, medial, BA 8 (40) Left superior frontal gyrus, dor – solateral, BA 9 (16) Left supplementary motor area, BA 8 (13) Left superior frontal gyrus, medial, BA 9 (3) Left superior frontal gyrus, medial, BA 32 (2) Left frontal aslant tract (2) Left supplementary motor area, BA 32 (2) Left supplementary motor area, BA 6 (1) 12/15 1 3 European Archives of Psychiatry and Clinical Neuroscience waking resting-state connectivity between the retrosplenial cortex/hippocampus and various nodes of the DMN [64 ]. The SLF is a large bundle of association tracts in the white matter of each cerebral hemisphere connecting the parietal, occipital and temporal lobes with ipsilateral frontal cortices [ 65], and SLF.R has been proved to play roles in the forma- tion of distress both within and between components of the DMN, salience network, and executive-control network [66]. Disruptions of SLF has also been reported in diusion tensor imaging (DTI) studies of primary insomnia [65, 67]. Taken Table 3 (continued) Regions Maximum Cluster Jackknife sensitivity analysis MNI coordinates SDM ValueP Number of voxels *Breakdown (number of voxels) X YZ OSA < HC Left calcarine ssure/surrounding cortex, BA 17 0 − 86− 10 − 1.933 0.000082552 290Left calcarine ssure/surrounding cortex, BA 17 (77) Right lingual gyrus, BA 17 (54) Left cerebellum, hemispheric lobule VI, BA 17 (38) Right lingual gyrus, BA 18 (38) Left lingual gyrus, BA 17 (28) Left calcarine ssure/surrounding cortex, BA 18 (7) Left calcarine ssure/surrounding cortex (5) Right inferior network, inferior longitudinal fasciculus (4) Cerebellum, vermic lobule VI, BA 17 (4) Right cerebellum, hemispheric lobule VI, BA 18 (4) Left lingual gyrus, BA 18 (1) Left cerebellum, hemispheric lobule VI (1) (undened) 29 12/15 Right superior frontal gyrus, dorsolateral, BA 10 16 6410 − 1.524 0.000722528 134Right superior frontal gyrus, dorsolateral, BA 10 (82) Right superior frontal gyrus, dorsolateral (20) Right superior frontal gyrus, medial, BA 10 (19) Corpus callosum (7) Right superior frontal gyrus, dorsolateral, BA 11 (6) 13/15 Left middle frontal gyrus, BA 46 − 44460 − 1.406 0.001594663 100Left middle frontal gyrus, BA 46 (42) Left inferior frontal gyrus, trian- gular part, BA 45 (30) Left middle frontal gyrus, orbital part, BA 47 (18) Left inferior frontal gyrus, trian- gular part, BA 46 (4) Left inferior frontal gyrus, orbital part, BA 46 (3) Left middle frontal gyrus, BA 45 (3) 11/15 * All voxels with P < 0.0025 uncorrected BA Brodmann area, HC healthy controls, ID insomnia disorder, MNI Montreal Neurological Institute, OSA obstructive sleep apnea, SDM seed‐ based d mapping 1 3 European Archives of Psychiatry and Clinical Neuroscience together, overactivated functions in the STG.L and SLF.R might lead to sleep-wakefulness disorders in ID patients. Besides, disrupted brain functions overlapping with above ndings have been constantly reported in MDD patients [28, 36]. Macroscopically speaking, ID is clinically described as a heterogeneous disorder, which includes dierent subtypes of pathophysiology in terms of cognitions, mood, traits, his- tory of life events and family history and not necessarily due to sleep complaints only [14, 34]. From a microscopic point of view, in consideration of the neuroimaging link between sleep disturbances and mental diseases, our fMRI results provided more objective insights that insomnia and circadian rhythm might participate in the pathophysiology of depres- sion and other neuropsychiatric disorders. Increasing evidence has demonstrated that in addition to well-known role in motor control, the cerebellum also plays roles in cognitive and emotional regulatory processes [ 68, 69], and also associates with sleep regulation [35]. The cerebellum is structurally and functionally connected to the limbic-cortical network [68, 70], which forms a feedback information ow that allows the cerebellum to involve in advanced neural activities. The IFG.R is thought to play roles in attentional control [71] and working memory [72]. Weakened regional brain functions in above regions might be related with cognitive decline and low spirit symptoms in ID patients. It is particularly noteworthy that the function of the right median cingulate/paracingulate gyri was altered in both ID and OSA, but presented converse patterns in these two dis- eases. The median cingulate/paracingulate gyri belong to the limbic system, which is responsible for regulating emotional disorders [35], and also involve in the subjective percep- tion of pain and one’s cognition and memory [73]. Altered activities of above brain areas reect complex changes of brain function in these two sleep disorders. Moreover, OSA patients showed distinct neural activity abnormalities in other brain regions compared with ID patients, including hyperactivities in right lenticular nucleus, PHG.L, CC and hypoactivities in left calcarine fissure/surrounding cor – tex, SFG.R and MFG.L. Therefore, though both served as most commonly seen sleep disturbances in clinic, ID and OSA possessed dierent neural mechanisms, or exhibited as various functional brain abnormalities. And most of the involved altered brain areas lied in the DMN, the central executive network (CEN) and the salience network (SN), all of which are essential in performing neural functions dur – ing rest, cognition, autonomic and emotional processes [1 ]. This provides important inspirations for our clinical work in the future, that is, these functionally altered areas could be served as potential targets for non-invasive brain closed loop stimulation, such as repetitive transcranial magnetic stimu- lation (rTMS), to rebalance the sleep homeostasis [35]. For example, high-frequency rTMS may increase reduced activ – ity in the right median cingulate/paracingulate gyri in ID patients, while low-frequency rTMS may be used to decrease increased activity in this area in OSA patients, which helps Table 4 Associations between demographic variables and brain function alterations in ID and OSA patients revealed by meta‐regression analy – ses AHI apnea–hypopnea index, BA Brodmann area, BMI body mass index, ID insomnia disorder, MNI Montreal Neurological Institute, OSA obstructive sleep apnea, SDM seed‐based d mapping, PSQI Pittsburgh Sleep Quality Index MNI coordinates Factor Anatomic label XYZSDM value P Number of voxels ID patients Age Right superior longitudinal fasciculus II 30− 1452 2.280 0.000139356 106 Duration Right cerebellum, hemispheric lobule IX 14− 58− 44 − 3.844 ~ 0 1446 PSQI Right superior longitudinal fasciculus II 28− 1460 2.144 0.000010312 68 OSA patients Age Right median cingulate/paracingulate gyri 2630 3.801 ~ 0 1038 Left parahippocampal gyrus, BA 36 − 20− 16− 30 2.861 0.000020623 273 Corpus callosum − 102448 3.039 0.000015497 199 AHI Left parahippocampal gyrus, BA 36 − 22− 16− 28 3.079 ~ 0 321 Corpus callosum 10820 2.848 0.000025809 18 Right superior frontal gyrus, dorsolateral, BA 10 16668 − 2.300 0.000206411 70 BMI Right median cingulate/paracingulate gyri 2430 3.600 ~ 0 1004 Left parahippocampal gyrus, BA 36 − 22− 16− 30 2.634 0.000020623 193 Corpus callosum − 102648 2.543 0.000020623 72 Right superior frontal gyrus, dorsolateral, BA 10 166410 − 1.941 0.000196099 20 1 3 European Archives of Psychiatry and Clinical Neuroscience to reverse abnormal brain function [74]. With timely inter- vention, the degrees of cognitive decits such as diculties with attention, memory, executive-functioning, and quality of life might be reversed. The sensitivity analysis and subgroup analysis revealed high reproducible, which conrmed the reliability of the study. However, the significant cluster numbers were a little bit less, this might be due to the statistical eects of less samples. Inconsistent ndings existed between ALFF/ fALFF and ReHo studies. This might be explained by the dierences in these two methods that ALFF/fALFF mainly measures the amplitude of uctuation of every single voxel, while ReHo reects the local synchronization of nearest neighboring voxels [37]. The meta-regression analysis indicated that the brain function alterations in the SLF.R II were positively corre- lated with the mean age and the PSQI of ID patients, and regional spontaneous activities in the right cerebellum were negatively correlated with the illness duration. Thus, the functional activities of SLF.R might be used to reect the severity of the disease. With the increase of the age and the progresses of duration, regional function alterations might continue exacerbating [2 ]. In OSA patients, neuro- imaging changes related with demographic variables were rather consistent. The mean age of the patients has posi- tive correlations with regional functional abnormalities in the right median cingulate/paracingulate gyri, PHG.L and CC. Aging is still one of the most important factors lead- ing to the chaos of brain function. The AHI was positively correlated with brain activity alterations in PHG.L and CC, and negatively correlated with brain function alterations in the SFG.R. Neural activities in these three regions were most closely associated with the severity of symptoms, and might be treated as important targets for non-invasive brain stimulation. The BMI had the maximum impacts on regional spontaneous brain activities, with a positive correlation with brain function alterations in right median cingulate/paracin- gulate gyri, PHG.L and CC, as well as a negative correlation with SFG.R. Obesity and higher BMI are considered to be vital risk factors of both adolescent and adult OSA patients [ 75– 77]. Our ndings provided a neurobiological theoreti- cal basis for the therapeutic strategies of weight control in OSA patients. Above meta-regression analysis brought inspirations to our future clinical work, that it is necessary to diagnose and treat both ID and OSA as early as possible, and to control the weight of OSA patients to alleviate their symptoms. Several limitations should be addressed in this current study. First, the data acquisition parameters and clinical vari- ables in the included studies were heterogeneous inescap- ably. It is hardly possible to eliminate these heterogeneities by statistical methods. Second, the present meta-analysis focused only on resting-state regional spontaneous brain activity changes in ID and OSA. Future studies need to include other approaches (i.e., FC, ICA, graph theory) as well as task-fMRI studies to provide a more comprehen- sive perspective of functional patterns of these two disor – ders. Third, it is meaningful to investigate the dynamicity and reversibility of neural activities, but the current meta- analysis and the literatures included in our research are all cross-sectional design. Longitudinal studies with respect to dynamicity of brain functions of ID and OSA are of great importance and should be explored in the future. Fourth, limited by the methodological shortcomings of nowadays analytical means, the study lacked a direct comparison between ID and OSA, which might be overcome by neuro- scientists and programmers in the future. Last but not least, the number of studies included in our meta-analysis was still insucient. The number of included subgroup studies was relatively small, so the interpretation of the subgroup nd- ings should be taken cautiously. Conclusions The AES-SDM approach served as a powerful meta-analysis method to synthesize neuroimaging ndings from dierent studies in a comprehensive way. In this present research, we performed a CBMA of rs-fMRI studies in ID and OSA to investigate the neurophysiological mechanisms of these two sleep disturbances simultaneously for a comparative perspective. We found distinct spontaneous brain activity alterations in these two diseases. These ndings improved our knowledge of the neuropathological mechanism of these two most prevalent sleep disorders, and also provided poten- tial guidance for future clinical application. The functionally altered brain regions might be served as biomarkers for more accurate and individualized diagnosis and treatment of ID or OSA in the future. Supplementary Information The online version contains supplemen- tary material available at https:// doi. org/ 10. 1007/ s00406- 022- 01485-7. Acknowledgements This study was supported by the Key Research and Development Plan of Jining City (2021YXNS024), the Medical and Health Science and Technology Development Plan of Shandong Province (202003061210), the Cultivation Plan of High-level Scientic Research Projects of Jining Medical University (JYGC2021KJ006), the National Natural Science Foundation of China (81901358), the Natural Science Foundation of Shandong Province (ZR2019BH001 and ZR2021YQ55), the Young Taishan Scholars of Shandong Province (tsqn201909146), and the Supporting Fund for Teachers’ Research of Jining Medical University (600903001). Funding Key Research and Development Plan of Jining City, 2021YXNS024, Cong Zhou, Medical and Health Science and Tech- nology Development Plan of Shandong Province, 202003061210, Cong Zhou, Cultivation Plan of High-level Scientic Research Projects of Jining Medical University, JYGC2021KJ006, Cong Zhou, National 1 3 European Archives of Psychiatry and Clinical Neuroscience Natural Science Foundation of China, 81901358, Hao Yu, Natural Science Foundation of Shandong Province, ZR2019BH001, Hao Yu,ZR2021YQ55, Hao Yu, Taishan Scholar Foundation of Shandong Province, tsqn201909146, Hao Yu, Supporting Fund for Teachers’ Research of Jining Medical University, 600903001, Cong Zhou Declarations Conflict of interest The authors declare that there is no conict of in- terest. Ethical approval This article is a meta-analysis with all analyses based on previously published studies; thus, no ethical approval and patient consent are required. References 1. 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