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

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.

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