PLOS ONE RESEARCH ARTICLE Specific cortical and subcortical grey matter regions are associated with insomnia severity Neus Falgàs ID1,2,3,4, Ignacio Illán-Gala1,2,5, Isabel E. Allen ID2,6, Paige Mumford3, Youssef M. Essanaa ID3,7, Michael M. Le3,7, Michelle You3, Lea T. Grinberg1,2,8,9, Howard J. Rosen2,3, Thomas C. Neylan3,7,10, Joel H. Kramer2,3,7, Christine M. Walsh3* 1 Atlantic Fellow for Equity in Brain Health at the University of California San Francisco, Department of Neurology, University of California San Francisco, San Francisco, California, United States of America, 2 Global Brain Health Institute, University of California, San Francisco, California, United States of America, 3 Department of Neurology, Memory & Aging Center, Weill Institute for Neurosciences, University of a1111111111 California, San Francisco, California, United States of America, 4 Alzheimer’s Disease and other Cognitive a1111111111 Disorders Unit, Department of Neurology, Hospital Clı́nic de Barcelona, Universitat de Barcelona, Barcelona, a1111111111 Catalonia, Spain, 5 Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, a1111111111 Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain, 6 Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America, a1111111111 7 Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, California, United States of America, 8 Department of Pathology, University of Sao Paulo Medical School, Sao Paulo, Brazil, 9 Department of Pathology, University of California, San Francisco, California, United States of America, 10 Stress & Health Research Program, Department of Mental Health, San Francisco VA Medical Center, San Francisco, California, United States of America OPEN ACCESS * Christine.Walsh@ucsf.edu Citation: Falgàs N, Illán-Gala I, Allen IE, Mumford P, Essanaa YM, Le MM, et al. (2021) Specific cortical and subcortical grey matter regions are associated with insomnia severity. PLoS ONE Abstract 16(5): e0252076. https://doi.org/10.1371/journal. pone.0252076 Editor: Allan Siegel, University of Medicine & Background Dentistry of NJ - New Jersey Medical School, There is an increasing awareness that sleep disturbances are a risk factor for dementia. ISRAEL Prior case-control studies suggested that brain grey matter (GM) changes involving cortical Received: November 19, 2020 (i.e, prefrontal areas) and subcortical structures (i.e, putamen, thalamus) could be associ- Accepted: May 10, 2021 ated with insomnia status. However, it remains unclear whether there is a gradient associa- Published: May 26, 2021 tion between these regions and the severity of insomnia in older adults who could be at risk for dementia. Since depressive symptoms and sleep apnea can both feature insomnia- Peer Review History: PLOS recognizes the benefits of transparency in the peer review related factors, can impact brain health and are frequently present in older populations, it is process; therefore, we enable the publication of important to include them when studying insomnia. Therefore, our goal was to investigate all of the content of peer review and author GM changes associated with insomnia severity in a cohort of healthy older adults, taking responses alongside final, published articles. The into account the potential effect of depression and sleep apnea as well. We hypothesized editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0252076 that insomnia severity is correlated with 1) cortical regions responsible for regulation of sleep and emotion, such as the orbitofrontal cortex and, 2) subcortical regions, such as the Copyright: © 2021 Falgàs et al. This is an open access article distributed under the terms of the putamen. Creative Commons Attribution License, which permits unrestricted use, distribution, and Methods reproduction in any medium, provided the original author and source are credited. 120 healthy subjects (age 74.8±5.7 years old, 55.7% female) were recruited from the Hill- blom Healthy Aging Network at the Memory and Aging Center, UCSF. All participants were Data Availability Statement: Data cannot be shared publicly because of participant approval determined to be cognitively healthy following a neurological evaluation, neuropsychological limited to sharing data with collaborators. Data are assessment and informant interview. Participants had a 3T brain MRI and completed the PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 1 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity available from the UCSF Institutional Data Access / Insomnia Severity Index (ISI), Geriatric Depression Scale (GDS) and Berlin Sleep Question- Ethics Committee (contact via ude.fscu@BRI) for naire (BA) to assess sleep apnea. Cortical thickness (CTh) and subcortical volumes were researchers who meet the criteria for access to confidential data. obtained by the CAT12 toolbox within SPM12. We studied the correlation of CTh and sub- cortical volumes with ISI using multiple regressions adjusted by age, sex, handedness and Funding: This work was supported by funding from UCSF Hillblom Aging Network grant, Larry L. MRI scan type. Additional models adjusting by GDS and BA were also performed. Hillblom Network (JHK), Biological predictors of brain aging trajectories (NIA R01 5R01AG032289- Results 10) (JHK), Clinical features and neuropathological basis of sleep-wake behavior in Alzheimer’s ISI and GDS were predominantly mild (4.9±4.2 and 2.5±2.9, respectively) and BA was Disease and PSP, (NIA R01 AG060477) (TCN, mostly low risk (80%). Higher ISI correlated with lower CTh of the right orbitofrontal, right LTG), Linking sleep dysfunction to tau-related superior and caudal middle frontal areas, right temporo-parietal junction and left anterior cin- degeneration across AD progression (NIA R01 AG064314) (LTG, TCN), Tau Consortium/ gulate cortex (p<0.001, uncorrected FWE). When adjusting by GDS, right ventral orbitofron- Rainwater Charity Foundation (CMW, TCN, LTG). tal and temporo-parietal junction remained significant, and left insula became significant The funders had no role in study design, data (p<0.001, uncorrected FWE). Conversely, BA showed no effect. The results were no longer collection and analysis, decision to publish, or significant following FWE multiple comparisons. Regarding subcortical areas, higher puta- preparation of the manuscript. https://www.llhf. org/grant-seekers/grant-criteria/network-grants men volumes were associated with higher ISI (p<0.01). https://projectreporter.nih.gov/project_info_ description.cfm?aid=10052959&icde= 52636853&ddparam=&ddvalue=&ddsub=&cr= Conclusions 1&csb=default&cs=ASC&pball=, https:// Our findings highlight a relationship between insomnia severity and brain health, even with projectreporter.nih.gov/project_info_description. relatively mild insomnia, and independent of depression and likelihood of sleep apnea. The cfm?aid=9932876&icde=52636818&ddparam= &ddvalue=&ddsub=&cr=1&csb=default&cs= results extend the previous literature showing the association of specific GM areas (i.e, orbi- ASC&pball= https://projectreporter.nih.gov/ tofrontal, insular and temporo-parietal junction) not just with the presence of insomnia, but project_info_description.cfm?aid=9989752&icde= across the spectrum of severity itself. Moreover, our results suggest subcortical structures 52636827&ddparam=&ddvalue=&ddsub=&cr= (i.e., putamen) are involved as well. Longitudinal studies are needed to clarify how these 1&csb=default&cs=ASC&pball= https:// tauconsortium.org/. insomnia-related brain changes in healthy subjects align with an increased risk of dementia. Competing interests: The authors have declared that no competing interests exist. Introduction Insomnia is a frequent sleep disorder defined as recurrent poor sleep quality or quantity due to difficulties in initiating or maintaining sleep (DSM-5). The prevalence of insomnia in older adults varies through epidemiological studies, but it has been estimated to affect up to 20% of the healthy older adult population [1]. In recent years, growing evidence has suggested that poor sleep quality may provide an increased risk for dementia [2,3]. Along this line, insomnia has been associated with the impairment of declarative memory in older adults, also affecting their general functioning [4–6]. Sleep appears to be intertwined with proteinopathies, where poor sleep quality is associated with β-amyloid dysregulation and sleep regulating regions are affected by abnormal protein deposition (i.e, tau, synuclein) early in many neurodegenerative disorders [7–11]. Taken together, this suggests that sleep disorders such as insomnia may be detrimental to brain health, possibly contributing to the development of neurodegenerative diseases [12,13]. Defining structural brain changes associated with insomnia could be helpful to understand the underlying changes conferring a risk for dementia in healthy individuals. This has become a topic of interest in recent years, where studies have investigated differences in grey matter (GM) and subcortical regions between insomniacs and non-insomniac older adults [14–16]. Although there is certain variability on the areas related to the report of insomnia across PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 2 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity studies, findings in prefrontal areas, putamen or thalamus are more consistent, suggesting that they might participate in insomnia status [14,15,17,18]. Despite the participation of prefrontal or subcortical regions in insomnia, it remains unclear how these structural brain changes may align with measures of the actual severity of the reported insomnia. For instance, although insomnia is a well-defined sleeping disorder, insomnia symptoms can still happen in non- insomniac individuals. Questionnaires evaluating insomnia severity such as the Insomnia Severity Inventory (ISI) are sensitive to the full range of insomnia symptoms, even if they are below the threshold of insomnia diagnosis [19]. This tool provides the opportunity to correlate the whole severity spectrum of insomnia complaints with their potential underlying brain changes. Secondary symptoms of insomnia can be caused by sleep apnea and depression, which are common in older adults, and could have potential effects on brain health as well. Therefore, when studying insomnia/brain health correlates in older adults, it is ideal to try to evaluate fac- tors which could themselves alter brain health. Therefore, in the current study we utilize an existing dataset to assess the association of both cortical and subcortical GM regions to per- ceived insomnia severity in a cohort of healthy older adults, taking into account the potential effect of depression and sleep apnea. We hypothesize that insomnia severity is correlated with 1) cortical regions responsible for regulation of sleep and emotion, such as the orbitofrontal cortex, and 2) subcortical regions, such as the putamen. Materials and methods Participants One hundred and twenty healthy subjects over 60 years old were selected from the Hillblom Healthy Aging network at the Memory and Aging Center, UCSF from 2012 to 2020 if they had completed the insomnia severity index questionnaire and had undergone magnetic resonance imaging. The study was approved by the UCSF Institutional Review Board, and all participants gave their written, informed consent. Subjects were determined to be cognitively healthy fol- lowing a comprehensive neurological evaluation, neuropsychological assessment and infor- mant interview. All subjects scored 0 for Clinical Dementia Rating (CDR). Insomnia severity assessment Subjective insomnia severity was assessed by the Insomnia Severity Inventory (ISI) [19]. The ISI is a self-reported questionnaire evaluating seven insomnia-related component scores: severity of difficulties with sleep onset, sleep maintenance, early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, noticeability of sleep problems by others, and distress caused by the sleep difficulties. Following the instruc- tions, participants rated each component from 0 to 5, indicating ‘no problem’ to ‘very severe problem’, respectively. The responses were summed to obtain the total score, which could range from 0 to 28. Berlin Sleep Questionnaire for sleep apnea The presence of obstructive sleep apnea was evaluated by the Berlin Questionnaire for sleep apnea (BA) [20]. The questionnaire consists of three categories related to the risk of having sleep apnea, including snoring and breathing, sleepiness, blood pressure and body mass index. The final score classifies individuals into High Risk or Low Risk for sleep apnea. PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 3 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity Geriatric Depression Scale The presence of depressive symptoms was assessed by the Geriatric Depression Scale (GDS) [21]. The GDS is a 30-item questionnaire where participants respond to yes/no questions how they felt over the past week. Scores of 0–4 are considered normal, depending on age, educa- tion, and complaints; 5–8 indicate mild depression; 9–11 indicate moderate depression; and 12–15 indicate severe depression. Brain MRI imaging Acquisition. MRI scans were examined at the UCSF Neuroscience Imaging Center on a Siemens Trio 3.0 T scanner (n = 86) or 3.0 T Prisma scanner (n = 34). T1-weighted magnetiza- tion-prepared rapid gradient-echo (MP-RAGE) structural scans were acquired with sagittal orientation, slice thickness = 1.0 mm; slices per slab = 160; plane resolution = 1.0x1.0 mm; matrix = 240X256, repetition time = 2300 ms, echo time = 2.98 ms, inversion time = 900 ms, flip angle = 9˚. Neuroimage processing. MRIs were processed with the CAT12 toolbox (http://www. neuro.unijena.de/cat/, version 1450) within SPM12 (http://www.fil.ion.ucl.ac.uk/spm/ software/spm12/, version 7487, running in MATLAB r2019b) and we recorded the IQR score during standard preprocessing as a measure of overall quality of images [22]. CTh estimates obtained with CAT12 are accurate and robust and can be considered a fast and reliable alterna- tive to other approaches for the analysis of cortical thickness [23]. The CAT12 toolbox uses tis- sue segmentation to estimate the white matter distance, and it then projects the local maxima (which is equal to CTh) to other GM voxels by using a neighbor relationship described by the white matter distance. Topological correction, spherical mapping, and spherical registration were performed to obtain vertex wise CTh. We then calculated the mean CTh at each region in the Desikan atlas and the volumes of subcortical GM structures in the neuromorphometrics atlas, as implemented in CAT12. Statistical analyses of clinical outcomes. To analyze the association of insomnia symp- toms with depressive symptoms and sleep apnea, we performed pairwise correlations on ISI scores with GDS and BA scores. Statistical analyses of ISI and cortical thickness. To study the correlations between CTh and ISI we performed linear modeling of the CTh maps as implemented in CAT12. We included age, sex, handedness and scan type as covariates. We applied the correction for multi- ple comparisons using Family-wise error (FWE) with a threshold of p<0.05 for cluster signifi- cance. This model was repeated to include BA as a covariate, and again to include GDS as a covariate. Additionally, we further performed CTh t-test sub-analyses to identify CTh differ- ences between the insomnia (ISI�8) and non-insomnia groups (ISI �7), adjusting by age, sex, handedness, scan type and GDS. Statistical analyses of ISI and subcortical volumes. Because subcortical gray matter vol- umes (but not the CTh) depend on total intracranial volume (TIV), we divided subcortical gray matter volumes by TIV of each participant to obtain normalized subcortical volumes. Using Stata 16.1 (College Station, Texas), we performed a multivariable linear regression for each subcortical volume to identify significant predictors of ISI while adjusting for age, sex, handedness and scan type. This model was repeated to include BA as a covariate, and again to include GDS as a covariate. We applied the correction for multiple comparisons using False Discovery Rate (FDR). In addition, we performed t-test sub-analyses between insomnia (ISI�8) and non-insomniaa (ISI �7) groups. PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 4 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity Results Demographics Detailed demographics data are shown in Table 1. Participants ranged in age from 62 to 88 years old. 55.7% of the sample were female and all participants had at least 12 years of educa- tion. The ISI total scores ranged from 0 to 16 corresponding to ‘non-insomnia’, ‘sub-threshold insomnia’ and ‘moderate insomnia’ categories. Association between ISI scores and other clinical outcomes ISI and GDS scores showed a significant positive correlation (r = 0.34, p<0.01) (Fig 1) while BA did not. Associations between ISI scores and GM regions Cortical regions. Multivariate analyses adjusting for age, sex, handedness and scan type showed that higher ISI scores correlated with lower CTh in right orbitofrontal, right superior and caudal middle frontal areas, right temporoparietal junction and left anterior cingulate regions (p<0.001, uncorrected FWE) (Fig 2). Covarying for BA did not alter the findings. When covarying for GDS, the negative correlations in the right orbitofrontal and temporo- parietal junction remained unchanged. The correlations in the right superior and caudal mid- dle frontal and left anterior cingulate regions, however, were lost. Further, we found that, when covarying for GDS, ISI was negatively correlated with CTh in the left insula (p<0.001, uncorrected FWE) (Fig 3). None of these clusters remained significant after the FWE multiple comparison correction. The CTh t-test comparisons between insomnia and non-insomnia groups showed lower CTh in certain orbitofrontal, prefrontal and temporo-parietal regions (p<0.001, uncorrected). However, differences in the insula were not replicated. Detailed results of the t-test sub-analyses are shown on S1 Fig. Subcortical regions. The detailed results from the linear regressions assessing subcortical volumes and ISI scores adjusted by age, sex, handedness and scan type are shown in Table 2. Right and left putamen showed a significant correlation with ISI, where greater putamen vol- umes were associated with higher ISI scores (p<0.01 uncorrected, Fig 4). Only the correlation Table 1. Cohort descriptors. Healthy subjects (n = 120) Age 74.8±5.7 (62–88) Gender (% women) 55.7% Years of education 17.4±2.1 (12–20) MMSE 29±1.1 (26–30) ISI total score 4.9±4.2 (0–16) ISI score categories (%) No clinically significant insomnia (0–7) 71 Subthreshold insomnia (8–14) 26 Clinical insomnia (moderate severity) (15–21) 3 Clinical insomnia (severe) (22–28) 0 GDS score 2.5±2.9 (0–15) BA score (% low risk) 80.2 Data are presented as means ± standard deviation (range). MMSE, Mini Mental State Examination; ISI, Insomnia Severity Index; GDS, Geriatric Depression Scale; BA, Berlin Apnea index. https://doi.org/10.1371/journal.pone.0252076.t001 PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 5 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity Fig 1. Correlation between Insomnia Severity Index and Geriatric Depression Scale. The figure shows a moderate positive correlation between ISI scores and Geriatric Depression Scale (r = 0.34, P-value < .001) in all participants (n = 119). https://doi.org/10.1371/journal.pone.0252076.g001 Fig 2. Correlation between Insomnia Severity Index scores and cortical thickness. Correlation between ISI scores and cortical thickness in all participants (n = 120). Only regions with P-value < .001 (uncorrected FWE) are shown. All multivariate linear regression models were adjusted for age, sex, handiness and scan type. Correlation coefficients are expressed as a color scale, indicating an increasing strength of the correlation from red to yellow. https://doi.org/10.1371/journal.pone.0252076.g002 PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 6 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity Fig 3. Correlation between Insomnia Severity Index scores and cortical thickness adjusting by Geriatric Depression Scale. Correlation between ISI scores and cortical thickness in all participants (n = 119). Only regions with P-value < .001 (uncorrected FWE) are shown. All multivariate linear regression models were adjusted for age, sex, handiness and scan type and Geriatric Depression Scale. Correlation coefficients are expressed as a color scale, indicating an increasing strength of the correlation from red to yellow. https://doi.org/10.1371/journal.pone.0252076.g003 Table 2. Effect of subcortical regional volumes to ISI scores. Region of interest β p Right Caudate 0.08 .405 Right Putamen 0.22 .016a Right Thalamus 0.07 .525 Right Pallidum 0.48 .608 Left Caudate 0.10 .303 Left Putamen 0.26 .005a Left Thalamus 0.07 .531 Left Pallidum 0.09 .328 Data are presented standardized beta (β). This table shows the effect of subcortical gray matter brain regions to ISI scores using linear model effects adjusting by age, sex, handiness, MRI scanner and total intracranial volume (FDR uncorrected). a p significant values (p<0.05). https://doi.org/10.1371/journal.pone.0252076.t002 Fig 4. Correlation between Putamen volumes and Insomnia Severity Index. Scatter plot showing the correlation of the normalized volumes of right and left putamen with ISI scores (n = 120). https://doi.org/10.1371/journal.pone.0252076.g004 PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 7 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity with left putamen volumes survived FDR multiple comparisons (p<0.05). Covarying for GDS and BA did not alter the findings. Additionally, box plot graphs showing the mean normalized volumes of the right and left putamen in insomnia (ISI�8) and non-insomnia (ISI �7) sub- jects are shown in S2 Fig. Discussion We performed a cross-sectional study analyzing the relationship between cortical and subcor- tical grey matter regions and the degree of perceived insomnia severity in a cohort of healthy older adults. We found that lower volumes in certain cortical areas (i.e., right ventral orbito- frontal, right temporo-parietal junction and left insula) and greater volumes in subcortical structures (i.e., putamen) were related to higher perceived insomnia severity independent of the presence of depression or likelihood of sleep apnea. Hence, identifying a detrimental rela- tionship between a insomnia symptoms and CTh in otherwise healthy aging populations. Compared to prior studies reporting insomnia prevalence around 30–35% in healthy aging populations, our cohort presents relatively low ISI scores that fall into the “no clinically signifi- cant insomnia” to “subthreshold insomnia” categories [1,14]. These differences might be related to methodological approaches and sample characteristics. Methodological differences involving tools or diagnostic criteria used to identify insomniacs can modify prevalence esti- mates, through differences in sensitivity and specificity [14,19]. Most of the studies assessing sleep quality in healthy aging cohorts are focused on younger populations over 50 years old, while our cohort is over 62 years old and could account for some of the difference. It is also possible that socio-economic statuses or prior education could influence the incidence of reporting insomnia, e.g. our cohort has a relatively high mean education of 17.4 years, with many participants having masters or doctorate degrees. Education, a proxy for cognitive reserve, might potentially shape the odds for insomnia development later in life as well. Nevertheless, despite the low insomnia severity found in our cohort, we still found an asso- ciation between ISI and cortical thickness. This fact is particularly remarkable because it rein- forces the observation that mild-moderate insomnia symptoms could have effects on brain structure in healthy aging populations, even with individuals below the threshold of insomnia diagnosis. Overall, this emphasizes the importance of the whole spectrum of insomnia severity with brain health in aging. Our findings highlighted the orbitofrontal cortex as a main cortical region associated with the severity of insomnia even when adjusting by depressive symptoms and sleep apnea. Specifically, we found smaller cortical volumes in the ventral orbitofrontal were associated with worse levels of insomnia. This is congruent with prior publications that have detected lower volumes of orbitofrontal cortex in insomnia subjects, as well as in healthy individuals with sleep fragmentation and early-morning awakenings [14,24–27]. Similarly, a report on a small sample of older adults (mean age 60 ± 6, age range 52–74) found that smaller orbitofrontal volumes were associated with higher scores in the insomnia subscale of the Sleep Disorders Questionnaire [15]. Our findings support the role of orbitofrontal regions in insom- nia but furthermore, extend this, confirming that the gradient of insomnia severity is associ- ated with volumetric values in these areas. As part of the highly interconnected limbic system, the orbitofrontal cortex participates in decision-making processes and emotion signaling, sug- gesting its dysfunction could lead to increased insomnia mediated by mood alterations. How- ever, the orbitofrontal cortex also has a role in evaluating thermal comfort [26]. Insomnia patients have difficulties judging thermal comfort, and interestingly, small changes from com- fortable temperatures have a detrimental effect on sleep quality [28]. Therefore, insomnia could also result from orbitofrontal dysfunction leading to the inability to recognize the opti- mal temperature for sleep [26,28]. PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 8 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity In addition, we also found that smaller areas of the dorsolateral prefrontal cortex were asso- ciated with worse levels of insomnia. The dorsolateral prefrontal cortex has long been impli- cated in high level cognitive functions such as attention, working memory, decision-making and reasoning. Impaired prefrontal functioning such as problem-solving abilities and emo- tional processing are associated with mood disorders (e.g. depression) that frequently are related or can lead to insufficient sleep [29]. Our findings support the role of the dorsolateral prefrontal cortex on insomnia symptoms especially when depressive symptoms may be con- tributing. In alignment with the hypothesis of emotional processing as a key character for insomnia development, a recent report highlighted right prefrontal areas (i.e, orbitofrontal) as a neuropathological core mechanism for the intersection of insomnia with mood symptoms (i. e, depression) [30]. Interestingly, our findings show this right-lateralized association between perception of insomnia severity not only in orbitofrontal but also in dorsolateral areas, rein- forcing the influence of emotional processing and mood to the perception of mild insomnia symptoms. However, the relationship between insomnia and brain atrophy might work in the opposite direction, meaning not only that grey matter volume may contribute to insomnia, but also insomnia could have an effect on grey matter structure. In this line, prior studies have reported morphological brain changes after sleep deprivation [31,32]. This suggests that insufficient sleep could modulate certain neurobiological processes that could potentially affect cortical gray matter structure (e.g. metabolite clearance, synaptic homeostasis, gene expression, macro- molecule biosynthesis, neuroinflammation, oxidative stress) [33–35]. Interestingly, the pre- frontal cortex is especially susceptible to oxidative stress and furthermore, sleep deprivation especially affects neuropsychological performance on tasks related to the prefrontal cortex (executive domains) [36]. Thus, it is possible that grey matter health in dorsal-orbitofrontal areas is vulnerable to insufficient sleep [37,38]. In addition, other cortical areas such as anterior cingulate have been associated with insom- nia severity in the present study although its effect disappeared when controlling for depres- sion. The cingulate cortex is considered part of the limbic system and participates in many high functions such as learning, memory processing, and emotion. It is plausible that the cin- gulate cortex/insomnia relationship is driven by the effect of mood disorders (i.e; depression). Although its role in sleep regulation is still poorly defined, it has been suggested to also partici- pate in specific processes such as the mediation of slow-waves during slow-wave sleep [39]. Most of the prior studies described lower volumes of the anterior or posterior cingulate in insomniacs [14,40,41]. Our findings are congruent with these prior reports, extending the lit- erature to show that these cingulate areas could be associated with insomnia severity. However, one previous study did report that larger rostral anterior cingulate volumes were associated with worse sleep in chronic insomnia patients [42]. Since chronic insomnia increases the risk for depression, these findings were interpreted as a compensatory response to repetitive sleep disturbance and a possible marker of resilience to developing mood disorders. The Winkel- mann study did not study older adults, but was restricted to young-middle-aged patients (mean age 39.3 ±8.7), which accounts for the conflicting results, and could suggest that there is an effect of aging on diminished resilience. Alternatively, the Winkelmann compensatory find- ing could be restricted to those with chronic insomnia, as the insomnia severity in our popula- tion was mild-moderate. On the other hand, the inferior parietal lobe together with the overlapping temporoparietal junction participate in a broad range of behaviors and functions such as attention, language processing, social cognition and self-awareness. As these are areas highly connected to diverse functional networks, they are thought to act more as a hub for multimodal integration, with probable participation in many cognitive processes [43]. Although the direct participation of PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 9 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity this area in sleep might be controversial, we believe that its involvement in insomnia is con- ceivable, specifically in the context of self-perception of sleep disturbance. In line with this hypothesis, a longitudinal study demonstrated that older adults with poor sleep quality devel- oped a widespread pattern of atrophy including these temporal-parietal areas as well [44]. Lastly, our cortical findings showing lower insular volumes are associated with insomnia severity, but only when withdrawing the effect of the depressive symptoms, is surprising. The insula is highly related to emotional processing and mood disorders, so we would expect it to be more related to the depressive symptoms than directly to insomnia. Indeed, the supplemen- tary analyses comparing insomnia vs non-insomnia groups within our cohort did not replicate this difference in the insula, supporting this hypothesis. However, prior literature has demon- strated changes in functional connectivity in the anterior insula after sleep deprivation [45,46]. Moreover, lesions on the anterior insula in rats elicits decreased wakefulness and increased rapid eye movement (REM) sleep and non-REM (NREM) sleep, suggesting that the insula could participate more than we thought in sleep-wake regulation processes [47]. The cortico-striato-thalamo-cortical loop has multiple neurocognitive functions including regulating arousals along with cognitive and affective functions. The striatum, formed by puta- men and caudate, is specially involved in sleep regulation [17]. In physiological terms, puta- men regulates arousal by inhibitory GABAergic projections to pallidum and thalamus, promoting cortical activity and wakefulness [48]. In the same line, bilateral lesions of the puta- men have demonstrated to reduce the time spent in wakefulness [49]. Furthermore, putamen is involved in motor regulation via connections with the primary motor cortex and premotor areas regulating restlessness, a manifestation of physiological arousal [50]. Morphometric studies that evaluated subcortical structures in insomnia patients, suggested altered volumes of putamen or thalamus in this population [14,18,51]. However, results are not consistent between studies showing either positive or negative correlations with sleep parameters. Our results support the effect of greater putamen volumes on insomnia severity, in line with its sug- gested wake-promoting role. The putamen, as a main component of the striatum, is highly interconnected with frontal cortical areas such as the orbitofrontal and prefrontal regions. The connectivity between these cortico-subcortical regions shapes the frontostriatal circuit, which has been related to sleep-wake regulation. Although the mechanism by which subcortical areas, such as the putamen, regulate arousal is still poorly understood, the dysfunction of the frontostriatal circuit seems to have a role in insomnia. In this line, prior functional MRI studies have reported altered patterns of connectivity between subcortical (putamen) and cortical (frontal) regions in insomnia patients [52–54]. We found no relationship, however, with tha- lamic volumes. Although it is known that subcortical structures participate in sleep regulation, further studies specifically evaluating which subcortical changes directly relate to insomnia are needed [17]. Aging is associated with neuronal dysfunction in terms of metabolic, proteostasis impairment and oxidative stress that can trigger amyloid-β, tau, and α -synuclein accumula- tion. The poor sleep quality and sleep deprivation associated with insomnia complaints, could accelerate these age-related changes potentiating disease-specific neuronal vulnerabilities caus- ing neurodegenerative disorders [55]. For instance, results from a recent study suggest that that sleep disturbances could predict accumulation of beta-amyloid across subsequent years [11]. Furthermore, relevant changes within frontal regions, from cortical atrophy to protein accumulation, have been identified in many early neurodegenerative disorders [27,56,57]. This evidence aligns with our findings reinforcing the idea of these areas related to insomnia severity, as especially vulnerable to neurodegenerative disease processes. Other sleep disorders have also been associated with subsequent onset of cognitive or neurodegenerative disorders. For example, periodic limb movements (PLMS), which precede dysexecutive impairment and PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 10 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity REM sleep behavior disorder (RBD), has been shown to predict the development of Parkin- son’s Disease [58,59]. Further investigations are required to understand the relationship between neurodegenerative processes and various sleep disorders, including PLMS, RBD, and now insomnia. Even though our results show an existing correlation between insomnia severity and certain gray matter changes, they do not confirm its causality. The relationship observed between both parameters could indicate that these specific gray matter changes are driving the severity of insomnia or, that insomnia severity has a detrimental effect on these areas. Ultimately, these brain changes could be happening in parallel with insomnia severity without being directly caused by it. The main strengths of this study are the well-characterized participants as cognitively healthy, older individuals. It is possible that cognitively healthy older adults with insomnia could be at greater risk for developing a neurodegenerative disease. However, we did not limit our cohort to those with high levels of insomnia. Evaluating not only individuals meeting crite- ria for insomnia disorder but also patients with slight insomnia symptoms, provides awareness of the effect the entire clinical spectrum of insomnia has on brain health. An important limita- tion of the present study is that the findings are restricted to subjective measures of insomnia as opposed to objective measures. Since BA identifies individuals at high risk for sleep apnea but does not confirm its actual presence, the contribution of sleep apnea to brain changes might be underestimated. Therefore, further studies assessing the presence of sleep apnea with objective (at-home or in-lab) diagnostic methods are needed. Additionally, individuals in our sample are cognitively intact but we do not have measures of neuropathological burden to detect silent underlying neurodegenerative changes. In this line, age-related changes such as vascular damage or the dysfunction of other subcortical nuclei within the brainstem, not evalu- ated in our study, could potentially modulate the observed relationship between cortical thick- ness and insomnia. A further limitation of the study is that the CTh correlations did not survive the correction for multiple comparisons. Although that was expected due to the small effect size of insomnia severity on CTh in an otherwise healthy population it could hamper the interpretation of the data. Further studies evaluating the relationship between insomnia and structural brain changes are warranted. Conclusions In conclusion, certain cortical areas (e.g, orbitofrontal, insula, temporo-parietal junction) and subcortical areas (i.e, putamen) are associated to the perception of higher insomnia severity, even in those individuals with mild insomnia symptoms and considering the added effect of insomnia-related comorbidities as depressive symptoms and sleep apnea. Further longitudinal studies are needed to clarify how these insomnia-related brain changes in healthy subjects align with an increased risk of dementia. Supporting information S1 Fig. Cortical thickness differences between insomnia and non-insomnia groups, adjusted by Geriatric Depression Scale. Cortical thickness t-test comparison between insom- nia (ISI�8) and non-insomnia (ISI �7) groups (n = 119) adjusting by age, sex, handedness, scan type and Geriatric Depression Scale. Only regions with P-value < .001 (uncorrected FWE) are shown. T-values are expressed as a color scale. (TIF) PLOS ONE | https://doi.org/10.1371/journal.pone.0252076 May 26, 2021 11 / 15 PLOS ONE Specific grey matter regions are associated with insomnia severity S2 Fig. Putamen volumes between insomnia and non-insomnia groups. Box plot showing the mean of the normalized volumes of right and left putamen in insomnia (ISI�8) and non- insomnia (ISI �7) subjects (n = 120). � Significance p<0.05. (TIF) Acknowledgments The authors thank the patients and their families for their invaluable contribution to brain aging research. Author Contributions Conceptualization: Neus Falgàs, Christine M. Walsh. Data curation: Youssef M. Essanaa, Michael M. Le, Michelle You. Formal analysis: Neus Falgàs, Ignacio Illán-Gala, Isabel E. Allen. Funding acquisition: Lea T. Grinberg, Thomas C. Neylan, Joel H. Kramer, Christine M. Walsh. Methodology: Neus Falgàs, Isabel E. Allen, Paige Mumford, Howard J. Rosen. Software: Ignacio Illán-Gala. Supervision: Lea T. Grinberg, Thomas C. Neylan, Christine M. Walsh. 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