The neuroimaging correlates of depression established across six large-scale datasets

The neuroimaging correlates of depression established across six large-scale datasets | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The neuroimaging correlates of depression established across six large-scale datasets Kassandra Miyoko Hamilton , View ORCID Profile Xiaoke Luo , View ORCID Profile Ty Easley , View ORCID Profile Fyzeen Ahmad , Thomas Guo , View ORCID Profile Setthanan Jarukasemkit , View ORCID Profile Hailey Modi , View ORCID Profile Samuel Naranjo Rincon , View ORCID Profile Cabria Shelton , View ORCID Profile Lyn Stahl , Zijian Wang , Yuling Zhu , View ORCID Profile Petra Lenzini , Deanna M. Barch , View ORCID Profile Kayla Hannon , View ORCID Profile Janine Bijsterbosch doi: https://doi.org/10.1101/2025.07.02.660888 Kassandra Miyoko Hamilton 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaoke Luo 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xiaoke Luo Ty Easley 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ty Easley Fyzeen Ahmad 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fyzeen Ahmad Thomas Guo 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Setthanan Jarukasemkit 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Setthanan Jarukasemkit Hailey Modi 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hailey Modi Samuel Naranjo Rincon 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Samuel Naranjo Rincon Cabria Shelton 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cabria Shelton Lyn Stahl 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lyn Stahl Zijian Wang 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuling Zhu 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Petra Lenzini 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Petra Lenzini Deanna M. Barch 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA 2 Department of Psychiatry, Washington University School of Medicine , Saint Louis, Missouri 63110, USA 3 Department of Psychological & Brain Sciences, Washington University , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kayla Hannon 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kayla Hannon Janine Bijsterbosch 1 Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Janine Bijsterbosch For correspondence: janine.bijsterbosch{at}wustl.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Summary Neuroimaging data offers noninvasive insights into the structural and functional organization of the brain and is therefore commonly used to study the neuroimaging correlates of depression. To date, a substantial body of literature has suggested reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks linked to depression. However, recent meta analyses have failed to identify significant converging correlates of depression across the literature such that a conclusive mapping of the neuroimaging correlates of depression remains elusive. Here we leveraged 23,417 participants across six datasets to comprehensively establish the neuroimaging correlates of depression. We found reductions in gray matter volume/ cortical surface area associated with depression in the frontal cortex, anterior cingulate, and insula, confirming prior studies showing the importance of prefrontal and default mode regions in depression. Our findings demonstrate multiple surprising results, including a lack of depression correlates in subcortical brain regions, significant depression correlates in somatomotor and visual regions, and limited functional connectivity findings. Overall, these results shed new light on key brain regions involved in the pathophysiology of depression, updating our understanding of the neuroimaging correlates of depression. We anticipate that these insights will inform further research into the role of sensorimotor and visual regions in depression and into the impact of heterogeneity on functional connectivity correlates of depression. Introduction Over 11,000 papers have been published to date to investigate the structural and functional neuroimaging correlates of depression 1 . However, recent meta analyses attempting to collate results across prior studies have reported null findings 1 – 4 , indicating a troubling lack of consistency in neuroimaging correlates of depression across the substantive existing literature. Sampling variability arising from underpowered study samples that comprise each meta-analysis may explain these null findings 5 , and such within-cohort sample size challenges are often present even in large-scale studies of the neural correlates of depression 6 , 7 . As such, a conclusive mapping of the neuroimaging correlates of depression remains unknown. In this study, we aimed to comprehensively identify the most reliable structural and resting state functional neuroimaging correlates of depression across six large-scale datasets. Beyond sampling variability in underpowered studies, there are several other challenges that may contribute to the observed inconsistencies in the literature on neuroimaging correlates of depression. For example, there are multiple self-reported phenotypic definitions of depression in common use, which are broadly based on either symptom severity or trait-level predisposition indexed using the neuroticism personality trait 8 , 9 . Furthermore, the literature on neuroimaging correlates of depression has largely focused on higher-order cortical systems 10 and subcortical regions 11 , which may have systematically resulted in under-reporting in other regions of the brain. To address these challenges, we determined the unbiased whole-brain spatial distribution of the structural and functional neuroimaging correlates reliably associated with symptom severity versus predisposition phenotypes of depression. We used six state-of-the-art large-scale datasets to comprehensively map the parcellated structural and resting state functional neuroimaging correlates of depression. Each dataset was analyzed separately and estimates were subsequently meta-analytically combined to establish the most reliable neuroimaging correlates of depression robust to sampling variability, phenotypic definitions, and regional bias ( Fig. 1 ). We purposefully chose to treat the six datasets included in this study separately rather than harmonizing all data into one mega sample. This approach was selected because perfect harmonization is not feasible 12 , especially when factors of importance (such as age range) are fully co-linear with dataset separation. Furthermore, non-removal of study differences (e.g., in relation to pre-processing, sample definition, depression instruments, etc.) offers a testbed for the type of realistic study-to-study variation observed in the literature. Download figure Open in new tab Figure 1. Overview of main analysis steps. From each of the 6 datasets, we extract one available depression severity phenotype and one available depression predisposition phenotype, and 45,052 matched imaging derived phenotypes. To estimate the association of each IDP with each depression phenotype, univariate within-dataset analyses were performed using linear mixed effect regressions and the results were entered into univariate meta-analyses to combine across datasets, followed by multiple comparison correction using false discovery rate (FDR). See the Online Methods for more information. Our findings identified significant structural neuroimaging correlates of depression. Brain regions with the strongest and most robust reductions in gray matter volume/ cortical surface area associated with depression included the frontal cortex, anterior cingulate and insula. Importantly, our findings did not indicate any significant resting state functional neuroimaging correlates of depression severity, casting doubt on the mechanistic theory of depression as a dysconnectivity syndrome. With regards to the whole-brain spatial distribution, significant structural neuroimaging correlates of depression included visual and somatomotor regions (e.g., reduced volume/area in paracentral, postcentral, precentral, fusiform, and pericalcarine regions), but no subcortical regions, shedding new light on key brain regions involved in the pathophysiology of depression. Taken together, this study substantially updates our understanding of the neuroimaging correlates of depression. Robust structural neuroimaging correlates of depression To establish the structural and functional neuroimaging correlates of depression, we comprehensively analyzed six existing datasets including the Adolescent Brain Cognitive Development (ABCD) study 13 , The UK Biobank (UKB) study 14 , the Human Connectome Project (HCP) Young Adult study 15 , the HCP Development study 16 , the HCP Aging study 17 , and the HCP Dimensional Connectomics of Anxious Misery study 18 . Depression severity was quantified based on available metrics in each dataset, using either the Hamilton Depression Rating Scale 19 (ANXPE), NIH Toolbox Sadness 20 (HCP-YA, HCP-A), Child Behavior Checklist (CBCL) Depression subscale 21 (HCP-D, ABCD), or Recent Depressive Symptoms (RDS) scale 22 (UKB). Depression predisposition was similarly quantified using dataset-specific measures, namely the Eysenck Neuroticism 23 (UKB), NEO five-factors inventory questionnaire Neuroticism subscale (HCP-YA, HCP-A, ANXPE), or UPPS negative urgency 24 (HCP-D and ABCD). Parcellated structural MRI metrics (gray matter volume, cortical thickness, cortical surface area) were calculated for 62 brain regions in the Desikan-Killiany-Tourville (DKT) 25 atlas, and - for gray matter volume - 16 subcortical regions in the ASEG 26 atlas. Functional connectivity was defined as the partial correlation between pairs of parcel timeseries using the Schaefer 27 300-dimensional atlas. Mass univariate linear mixed-effects regression (LMER) models were fit to separately estimate each imaging derived phenotype based on one of the depression phenotypes (severity or predisposition) and confounds (age, sex, total intracranial volume, head motion, imaging site, and family group, as relevant for each dataset). LMER effect size and error estimates from each of the six datasets were subsequently entered into mass univariate meta analyses to calculate overall brain-depression effect sizes separately for each imaging derived phenotype and each depression phenotype. False discovery rate (FDR) correction was performed to control meta analysis results for multiple comparisons across all structural and functional imaging measures within each depression phenotype, such that no significance is reported at the study level. The results revealed robust structural neuroimaging correlates of depression. The most consistent neural correlates of depression were reduced gray matter volume/ cortical surface area in the superior frontal cortex, middle frontal cortex, orbitofrontal cortex, rostral anterior cingulate and insula ( Fig. 2 ; full set of results included in the supplementary information). Notably, among significant regional findings, there was substantial agreement both bilaterally and between gray matter volume and cortical surface area ( Table 1 ). However, no significant meta analytical results were observed for cortical thickness, which may be explained by recent findings revealing extensive heterogeneity in cortical thickness changes associated with depression 28 . Overall, these findings are consistent with prior voxel-based meta analytical studies 29 – 32 , and point to the importance of frontal regions in depression. View this table: View inline View popup Download powerpoint Table 1: Overview of significant structural meta analytical effect sizes. Results that pass FDR-corrected p<0.05 are highlighted in blue font. Counts of significant entries are summarized per row (# Sig) and per column. Download figure Open in new tab Figure 2. Neuroimaging correlates of depression collated across six large-scale datasets for (A-D) depression severity and (E-H) predisposition phenotypes. Volcano plots reveal meta analytical estimates, with significant imaging measures indicated by crosses. Results are shown on the parcellated cortical surface unthresholded (cold-warm colorbar) and after thresholding (yellow; where significant). For functional connectivity, cortical surface figures show the absolute average across a row of the full correlation matrix with the sign determined by the mode of the sign across the row (e.g., negative sign if more edges of a brain region are negative than positive). Importantly, the meta analysis did not indicate significant gray matter volume changes associated with depression severity or predisposition in any of the 16 subcortical regions. Notably, none of the subcortical results achieved significance prior to multiple comparison correction either, although uncorrected trends were observed in the bilateral amygdala, left hippocampus, and right accumbens (p uncorrected =0.07; see supplementary information). It is possible that this lack of subcortical associations may be driven by a nonlinear relationship between subcortical volume and depression severity/predisposition that is obscured with the linear statistics adopted here. Such a nonlinear association may be more likely when using population datasets compared to traditional patient-control studies of depression, and therefore warrant future investigation. No robust functional correlates of depression Surprisingly, our meta-analysis found no functional connectivity results that reached significance after correction for multiple comparisons for depression severity ( Fig. 2D ), and only two functional connectivity edges that reached significance for depression predisposition ( Fig. 2H ). Specifically, depression predisposition was associated with increased functional connectivity between the right anterior temporal cortex and the right medial prefrontal cortex and with increased functional connectivity between the left medial prefrontal cortex and the left ventral prefrontal cortex. These findings are consistent with prior work linking depression to hyperconnectivity within the default mode network and between the default mode network and frontoparietal network 10 . Prior work has furthermore linked increased activity within the default mode network with increased rumination 33 , 34 and self-referential thinking 35 . As such, our limited functional connectivity findings support the role of rumination in depression predisposition 34 . Overall, the lack of substantive functional connectivity findings was surprising given the extensive prior literature highlighting functional connectivity alterations as a key descriptor of depression etiology 10 , which has led to major depressive disorder being described as a dysconnectivity syndrome 36 . Notably, the multiple comparison burden was combined across all structural and functional imaging measures and therefore cannot explain this difference. It is possible that the functional correlates of depression may be more heterogeneous across patients than structural correlates 37 – 39 . These results call into question the description of depression in light of dysconnectivity mechanisms, and raise the importance of robust studies of depression heterogeneity focused on functional connectivity. To further enable quantification of direct comparisons between the effect sizes of structural and functional neuroimaging correlates of depression, we performed a two-way ANOVA with a main effect for imaging metric type (4 levels; gray matter volume, cortical surface area, cortical thickness, functional connectivity), a main effect for depression phenotype (2 levels; severity, predisposition), and the interaction effect (imaging metric type x depression phenotype) on the absolute values of the meta-analytical effect sizes as the inputs. Importantly, this analysis was performed on all effect sizes (regardless of significance after multiple comparison correction) and can therefore pick up broader trends beyond the univariate results described above. The results revealed a significant main effect of imaging metric type (F=1700.3, p=0), a significant main effect of depression phenotype (F=15.7, p=7.42*10 −5 ), and a significant interaction effect (F=12.6, p=3.02*10 −8 ). Post-hoc results confirmed significantly higher effect sizes for depression severity compared to predisposition (especially for gray matter volume and cortical surface area). Post-hoc results further indicated significant differences between each pair of imaging metric types, with especially larger effect sizes for gray matter volume and cortical surface area as compared to functional connectivity and cortical thickness ( Fig. 3 ). Download figure Open in new tab Figure 3. Meta analytical estimates (absolute) as a function of imaging metric type (x-axis) and depression phenotype. Results show larger effects for severity (blue) compared to predisposition (gray), especially for gray matter volume and surface area. Visual and somatomotor involvement in depression The literature on the structural and functional neuroimaging correlates of depression has largely focused on subcortical regions (e.g., amygdala, hippocampus) and higher order cortical networks (e.g., regions of the default mode, fronto-parietal, and executive control networks) 10 , 11 . We aimed to perform an unbiased search for the structural and functional neuroimaging correlates of depression across the whole brain. Qualitatively, the meta analytical results summarized in Fig. 2 reveal a diffuse whole-brain pattern of associations with depression 40 . To systematically summarize the spatial distribution of neuroimaging correlates of depression, we assigned each parcel (from DKT or Schaefer atlases) to one of 7 cortical networks as defined by Yeo et al 41 or to an eighth combined subcortical network (ASEG) based on maximal spatial overlap. We then performed ANOVAs with a main effect for Yeo network (7 or 8 levels; depending on the inclusion/exclusion of subcortical regions), a main effect for depression phenotype (2 levels; severity and predisposition), and the interaction effect (Yeo network x depression phenotype) on the absolute values of the meta-analytical effect sizes as the inputs. Analyses were performed separately for each of the two imaging metric types with significant meta analytical results (namely, gray matter volume and cortical surface area). The results revealed a significant main effect of Yeo network for gray matter volume (F=10.1, p=3.5*10 −10 ), which was driven by relatively lower effect sizes for subcortical regions ( Fig. 4A ). This finding is surprising given prior research indicating associations between depression and volume of several subcortical regions 6 including the hippocampus 42 . Nevertheless, our findings are consistent with recent work showing chance-level multivariate classification of major depressive disorder, where subcortical volumes became uninformative for classification after careful harmonization for site effects 43 . It is possible that the wide variation of age ranges across the datasets may contribute to this surprising observation, given that hippocampal volume loss has been linked to cumulative depressive burden across lifetime episodes 44 , 45 and may be absent in first episode patients 6 . Alternatively, future work may wish to assess nonlinear associations between subcortical (e.g., hippocampal) volume and depression in population data. Download figure Open in new tab Figure 4. Neuroimaging correlates of depression as a function of spatial network organization (x-axis categories) and depression phenotype (gray = predisposition, blue = severity), shown separately for (A) gray matter volume, (B) cortical surface area. The white dots in each violin plot represent the median and the thick gray bar in the center represents the interquartile range. On each side of the gray line is a kernel density estimation to show the distribution shape of the data. Notably, the ‘limbic’ network in figure 4 refers to the Yeo-7 cortical definition of limbic regions (i.e., medial and lateral orbitofrontal cortex, inferior temporal cortex, and entorhinal cortex) and does not include subcortical contributions. Interestingly, post-hoc comparisons of the ANOVA results for gray matter volume and cortical surface area did not indicate any significant differences between cortical networks (p>0.25). Although more brain regions in the default mode, frontoparietal, and limbic networks showed significant associations with depression ( Table 1 ), multiple robust significant structural associations with depression were also observed for regions in the somatomotor and visual networks. Specifically, depression severity was associated with reduced gray matter volume and/or cortical surface area in paracentral, postcentral, precentral, and fusiform regions, whereas depression predisposition was associated with reduced gray matter volume and/or cortical surface area in the pericalcarine region. Recent studies have started to acknowledge and discuss the role of the visual and somatomotor networks in depression 46 – 49 , but attention on these findings remains limited. Our results substantiate structural associations of the visual and somatomotor networks in depression, highlighting the need to improve unbiased reporting and future research into potential explanatory mechanisms. In relation to the role of somatomotor networks in depression, recent studies have revealed the transdiagnostic nature of somatomotor abnormalities across multiple psychiatric and cognitive domains 50 – 53 . Taken together, there is a need for increased research focus on the role of the visual and somatomotor regions in depression. Unique neuroimaging correlates of severity versus predisposition Variability in depression phenotypes is one potential reason for the observed inconsistencies across the literature on neuroimaging correlates of depression. We directly compared the neuroimaging correlates across two classes of depression phenotypes, namely self-reported instruments that measure depression severity versus self-reported instruments that measure the personality trait neuroticism as a proxy for depression predisposition. Notably, the exact instrument within each class of depression phenotype varied across the datasets (see Online Methods for details). To assess systematic differences in meta analytic effect sizes, the same ANOVAs for gray matter volume and cortical surface area described above - which included a main effect for depression phenotype and an interaction effect (depression phenotype by network) - were used. In the ANOVAs for gray matter volume, both the main effect of depression phenotype (F=5.16, p=2.46*10 −2 ) and the interaction effect (depression phenotype by network; F=2.19, p=3.89*10 −2 ) were significant. Post hoc comparisons indicated that the main effect was driven by larger effect sizes for severity compared to predisposition. This is consistent with the univariate thresholded results, which indicated 38 significant hits for severity as compared to 22 significant hits for predisposition ( Table 1 ). Interaction effects were driven by subcortical regions as discussed above. For cortical surface area neither the main effect of depression phenotype (F=1.83, p=0.17) nor the interaction effect (F=1.09, p=0.37) reached significance. We note that potentially higher effect sizes may be expected when using clinician-rated diagnostic tools such as the Hamilton Depression Rating scale (HAM-D) or the structured clinical interview for diagnosis (SCID) 54 – 57 , even compared to self-reported indices of symptom severity. Although we did leverage clinician-rated instruments where available (in HCP-ANXPE), there is currently a lack of sufficiently large-scale neuroimaging cohorts with extensive clinician-rated phenotyping. Beyond clinician-rated severity scores, phenotypic variability in symptom type forms another source of potential bias in studies of the brain basis of depression. Future work may wish to explore the meta analytical brain basis of specific symptom domains. Conclusion In this study, we comprehensively analyzed the structural and functional neuroimaging correlates of depression across six large-scale datasets and meta-analytically combined results to establish the most reliable brain regions involved in the psychophysiology of depression. Our findings revealed that depression was associated with significant reductions in gray matter volume and cortical surface area in superior frontal cortex, middle frontal cortex, orbitofrontal cortex, rostral anterior cingulate and insula. Surprisingly, we did not observe any evidence for depression correlates in subcortical brain areas, and very limited evidence for depression correlates in functional connectivity. Although the majority of findings involved default mode, frontoparietal, and limbic regions, multiple unexpected structural associations with depression were also observed in somatomotor (paracentral, postcentral, and precentral) and visual (fusiform and pericalcarine) regions. Taken together, this study is the first to robustly map the neuroimaging correlates of depression across well powered studies. Importantly, these findings substantially update our understanding of the neuroimaging correlates of depression by highlighting the importance of structural rather than functional associations and uncovering key somatomotor and visual contributions. Data sharing statement All datasets used in this manuscript have been shared online: UKB data are available following an access application process: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access . This research was performed under UK Biobank application number 47267. ABCD data was available from the NIMH Data Archive when data were obtained for this study. HCP-A, HCP-D, ABCD and ANXPE (referred to as Dimensional Connectomics of Anxious Misery [DCAM]) data are available from the NIMH Data Archive collectively under the header of ‘CCF Data from the Human Connectome Projects’. HCP-YA data are available from the connectomeDB: https://db.humanconnectome.org/ . Code sharing statement All code used in this paper are available on Github: Author contributions Conceptualization: KH, KMH, JB Data Curation: XL, KMH Formal Analysis: KMH, XL, TE, FA, TG, SJ, HM, SNR, CS, LS, PW, YZ, PL Funding Acquisition: KMH, JB Methodology: TE, PL, LS, XL Supervision: KH, JB Visualization: XL, KMH, SJ Writing – Original Draft: KH, JB, KMH, XL Writing – Review & Editing: KMH, XL, TE, FA, TG, SJ, HM, SNR, CS, LS, PW, YZ, PL, KH, JB Funding information Janine Bijsterbosch was supported by the NIH (NIMH R01 MH128286 & NIMH R01 MH132962), and Ty Easley was supported by the National Science Foundation under Grant No. DGE-2139839, and Samuel Naranjo Rincon was supported by the National Science Foundation under Grant No. 2139839, and Setthanan Jarukasemkit was supported by the Prince Mahidol Foundation, and Cabria Shelton was supported by the NIH NeuroPrep Program under Grant R25 NS130965-02, and Kassandra Hamilton was supported by the NIH (R01 MH128286-03S2). Computations were performed using the facilities of the Washington University Research Computing and Informatics Facility (RCIF), which has received funding from NIH S10 program grants: 1S10OD025200-01A1 and 1S10OD030477-01. Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88 , R01 MH128286 , R01 MH132962 , R25 NS130965-02 , R01 MH128286-03S2 , 1S10OD025200-01A1 , 1S10OD030477-01 National Science Foundation, https://ror.org/021nxhr62 , DGE-2139839 , 2139839 Footnotes ↵ * Joint first authors ↵ # Joint last authors 1 Based on a PubMed search for the keyword string ‘neuroimaging depression’. References 1. ↵ Saberi , A. , Mohammadi , E. , Zarei , M. , Eickhoff , S. B. & Tahmasian , M. Structural and functional neuroimaging of late-life depression: a coordinate-based meta-analysis . Brain Imaging Behav . ( 2021 ) doi: 10.1007/s11682-021-00494-9 . OpenUrl CrossRef 2. Winter , N. R. et al. 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