Abstract Background Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

Combining multi-site MRI datasets increases statistical power and model generalisability but may be hindered by variability between sites. Harmonisation methods aim to remove potentially confounding variance while preserving biologically meaningful signals. However, this can be challenging, as each T1-weighted image reflects both scanner properties (e.g., field strength, sequence parameters) and individual biological characteristics (e.g., age, sex, ethno-cultural background, and pathology). Two image-based (HACA3, IGUANe) and two feature-based (neuroHarmonize, neuroCombat) harmonisation methods were assessed using T1-weighted brain imaging data from the Psy-ShareD database; 564 participants (295 schizophrenia, 269 controls) from seven studies acquired across 5 sites from the Psy-ShareD database. We trained several models to classify sites, schizophrenia diagnosis, age, and symptom levels. Site-classification accuracy was high for unharmonised data (90.1%) and for HACA3 (92.2%), slightly reduced with IGUANe (86.6%), and near chance for feature-based methods (4.2% neuroHarmonize; 1.8% neuroCombat), indicating effective bias removal. We fitted several models predicting biological signals including diagnosis, age, and symptom levels across different harmonisation methods. In most cases, classification with harmonised data performed at least as well as with unharmonised data. Generally, feature-based methods best remove site-related variance, but image-based approaches remain a promising avenue for preserving individual biological differences. This work provides practical guidance for selecting harmonisation strategies in multi-site psychiatric neuroimaging, depending on whether the priority is bias reduction or preservation of subject-level variability. ### Competing Interest Statement PA has been funded by FrieslandCampina and Nedra, GKM consults for Ieso Digital Health. RU reports consultancy from Vitaris and Springer Healthcare unrelated to the current work. ### Funding Statement This work was funded by the United Kingdom Medical Research Council grant number MR/X010651/1 and delivered through the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC). This work was supported in part by the Japan Agency for Medical Research and Development (AMED) Grant Number JP24wm0625302. All research at the Department of Psychiatry in the University of Cambridge is supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the NIHR Applied Research Collaboration East of England. RU is supported by the NIHR Oxford Health Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. A list of funders and acknowledgements for Psy-ShareD datasets can be found at https://psyshared.com/Team.html ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: London - Dulwich Research Ethics Committee Title of the Research Database: Psychosis MRI Shared Data Resource (Psy- ShareD), V1 REC reference: 25/LO/0184 IRAS project ID: 352347 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data used in the study is available via the Psychsis MRI ShareD Data Resource (psyshared.com)

Background:Major depressive disorder (MDD) is associated with altered brain structure and evidence of accelerated brain aging. However, previous studies have been limited by clinical samples with mixed medication status and multiple mood states, modest sample sizes, small percentage of MDD individuals older than 65 years of age, and/or reliance on summary-level data. Methods:Harmonized T1-weighted MRI from MDD (n = 645), all medication-free and in a current depressive episode, and matched healthy controls (n = 645), segmented into 145 regional volumes, from 11 sites in COORDINATE-MDD consortium. Brain age gap (BAG) was estimated using gradient boosting regression with nested cross-validation. Group differences in BAG (and age-corrected BAG [cBAG]) were examined across age strata. Regional contributions were evaluated using Shapley Additive exPlanations. Results:MDD was associated with significantly elevated cBAG compared with healthy controls (mean difference + 2.01 years). Age-stratified analyses showed no differences before mid-30s, with progressively larger gaps thereafter, reaching +6.85 years in MDD aged 55 and older. cBAG differed across neuroanatomical phenotypes associated with differential antidepressant response, cognitive impairment, increased adverse life events, increased self-harm and suicide attempts, and a pro-atherogenic metabolic profile. Key contributing regions included lateral and medial prefrontal regions, middle temporal gyrus, putamen, supplementary motor cortex, central operculum, and cerebellum. Conclusions:Accelerated structural brain aging in MDD is age-dependent and is most pronounced in a neuroanatomical phenotype associated with worse key clinical outcomes. The findings support neuroprogression models of MDD while demonstrating that cBAG is not a uniform feature of MDD and seem to be more strongly expressed in a specifically clinically vulnerable disease phenotype.

Importance:Major depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. Objective:To use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design setting and participants:A normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measures:Z-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. Results:Z-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevance:Our study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points: Question:Can z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? Finding:We confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. Meaning:Our findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7012 participants from 31 sites (N = 2772 MDD and N = 4240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from other MRI modalities such as fMRI and DWI will lead to a higher performance in this diagnostic task.

In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (β range = −0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address question (2), we bring together existing - and derive new - cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We report that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 66.1% of the variance) and these dimensions share spatial patterning with the g-morphometry profiles (p_spin < 0.05 |r| range = 0.22 to 0.55). We provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.

Major Depression (MD) is a prevalent, disabling and life-limiting condition. The neurobiological associations of genetic risk for MD remain under-explored in large samples, with no comprehensive mega-analysis conducted to date. Our study analysed data from 11 separate studies, encompassing 50,975 participants from the ENIGMA Major Depressive Disorder Working Group. We developed highly consistent genetic and neuroimaging protocols and applied these throughout all participating studies, together with rigorous genetic methods to remove overlap between the polygenic risk scores (PRS) training and testing samples. Elevated PRS for MD correlated with lower intracranial volume and lower global measure of cortical surface area (βICV = −0.017, pICV = 1.97 × 10−6; βSurf = −0.013, pSurf = 4.5 × 10−4; pFDR < 3.62 × 10−4). The most significant cortical association was observed in the surface area of the frontal lobe (β = −0.011, p = 2.85 × 10−6, pFDR = 1.42 × 10−5), particularly in the left medial orbito-frontal gyrus (β = −0.021, p = 9.48 × 10−8, pFDR = 1.25 × 10−5). In subcortical regions, lower volumes of the thalamus, hippocampus, and pallidum correlated with higher PRS of MD (β ranged from −0.011 to −0.015, p ranged from 0.002–1.73 × 10−5, pFDR < 0.006). In a subsample of young individuals only (<25 years old, N = 5570), although there were no FDR-significant findings, directions of effects were highly consistent between the analyses of cortical surface areas in youth and the full sample (71.2% in the same direction, exact binomial test p-value = 7.56 × 10−4). Subsequent Mendelian randomisation analysis revealed potentially causal effects of smaller left hippocampal volume on higher liability for MD (Inverse variance weighted analysis β = −0.064, p = 8.04 × 10−3, pFDR = 0.04). Our findings represent an example of how extensive international collaborations can significantly advance our neurogenetic understanding of MD and give insights to avenues for early interventions in those at high risk for developing MD.

Whether depression and poor sleep interact or have statistically independent associations with brain structure and its change over time is not known. Within a subset of UK Biobank participants with neuroimaging and subjective and/or objective sleep data (n = 28,351), we examined associations between lifetime depression and sleep disruption, and their interaction with structural neuroimaging measures, both cross-sectionally and longitudinally. Sleep variables were: self-reported insomnia and difficulty getting up; actigraphy-derived short sleep (<7 h); sustained inactivity bouts during daytime (SIBD); and sleep efficiency. Imaging measures were white matter microstructure, subcortical volumes, cortical thickness and surface area of 24 cortical regions of interest. Individuals with lifetime depression (self-reported, mental health questionnaire or health records) were contrasted with healthy controls. Interactions between depression and difficulty getting up for i) right nucleus accumbens volume and ii) mean diffusivity of forceps minor, reflected a larger negative association of poor sleep in the presence vs. absence of depression. Depression was associated with widespread reductions in white matter integrity. Depression, higher SIBD and difficulty getting up were individually associated with smaller cortical volumes and surface area, particularly in the frontal and parietal lobes. Many regions showed age-related decline, but this was not exacerbated by either depression or sleep disturbance. Overall, we identified widespread cross-sectional associations of both lifetime depression and sleep measures with brain structure. Findings were more consistent with additive rather than synergistic effects- although in some regions we observed greater magnitude of deleterious associations from poor sleep phenotypes in the presence of depression.

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone. Using the semi-supervised machine learning algorithm, Heterogeneity through Discriminative Analysis (HYDRA), we had identified two neuroanatomical dimensions in deeply phenotyped (i.e., comprehensively assessed across neuroimaging, clinical, and behavioural domains), medication-free participants with MDD from the COORDINATE-MDD consortium. In the present study, we apply this pre-trained HYDRA model to the UK Biobank (UKB) to validate these dimensions in a large general population and a subsample with current depressive symptoms. Dimension 2 (D2), compared to Dimension 1 (D1), is characterized by reduced grey and white matter volumes and limited treatment response to antidepressant and placebo medications. Out-of-sample validation in the UKB general population (n = 37,235) confirms these neuroanatomical features and reveals D2 associations with cognitive impairments, adverse life events, self-harm and suicide attempts, a pro-atherogenic lipid profile, and genetic links to neurodegenerative traits. Similar profiles are observed in the UKB subsample with current depressive symptoms (n = 1455). D1 and D2 represent distinct neurobiological mechanisms underlying MDD. The validation in a general population-based cohort and in a cohort sample with depressive symptoms delineates mechanisms underlying heterogeneity in MDD. Major depressive disorder is a common and disabling condition, but people differ greatly in their symptoms and responses to treatment. We used brain scans and machine learning to identify two patterns of brain structure linked to depression. One pattern showed relatively preserved brain volume and was associated with better treatment response. The other showed widespread reductions in brain volume and was related to poorer memory and thinking skills, greater exposure to adverse life events, increased risk of self-harm, and metabolic and genetic changes. These findings were confirmed in a large general population sample as well as in people with current depressive symptoms. The results suggest that depression includes distinct brain-based subtypes, which may help explain differences in treatment response and guide the development of more personalised approaches. Xiao, Woodham, Cui, et al. apply machine learning to brain MRI data from major depression and the UK Biobank. They identify two neuroanatomical dimensions, one linked to preserved brain structure and healthier outcomes, and the other to reduced volumes, impaired cognition, self-harm, and adverse metabolic and genetic profiles.

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), with the meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (|β| range = -0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address (2), we bring together existing - and derive new - cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We discover that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 65.9% of the variance) and denote aspects of neurobiological scaffolding that underpin the spatial patterning of MRI-cognitive associations we observe (significant |r| range = 0.21 to 0.56). Alongside the cortical maps from these analyses, which we make openly accessible, we provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.

STUDY OBJECTIVES:To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure. METHODS:Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions. RESULTS:Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen. CONCLUSIONS:Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples ( N = 1,384) of medication-free individuals with first-episode and recurrent MDD ( N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls ( N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals ( N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter ( N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter ( N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) ( β = –18.3, 95% CI (–34.3 to –2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene’s expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components : gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 41 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.15 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

Phenotyping of major depressive disorder (MDD) can vary from study to study, which, together with heterogeneity of the disorder, may contribute to the inconsistent associations with neuroimaging features and underlie previous problems with machine-learning methods for MDD diagnostic applications. In this study, we examined the classification accuracy of structural and functional connectomes across different depressive phenotypes, including separating MDD subgroups into those with and without self-reported exposure to childhood trauma (CT) (one of the largest risk factors for MDD associated with brain development). We applied logistic ridge regression to classify control and MDD participants defined by six different MDD definitions in a large community-based sample (N=14,507). We used brain connectomic data based on six structural and two functional network weightings and conducted a comprehensive analysis to (i) explore how well different connectome modalities predict different MDD phenotypes commonly used in research, (ii) whether stratification of MDD based on self-reported exposure to childhood trauma (measured with the childhood trauma questionnaire (CTQ)) may improve the accuracies, and (iii) identify important predictive features across different MDD phenotypes. We found that functional connectomes outperformed structural connectomes as features for MDD classification across phenotypes. The highest accuracy of 64.8% (chance level 50.0%) was achieved in the Currently Depressed (defined by the presence of more than five symptoms of depression in the past 2 weeks) sample with additional CTQ criterion using partial correlation functional connectomes. The predictive feature overlap, measured using Jaccard index, indicated that there were neurobiological differences between MDD patients with and without childhood adversity. Further analysis of predictive features for different MDD phenotypes with hypergeometric tests revealed sensorimotor and visual subnetworks as important predictors of MDD. Our results suggest that differences in sensorimotor and visual subnetworks may serve as potential biomarkers of MDD.

The current biologically uninformed psychiatric taxonomy complicates optimal diagnosis and treatment. Neuroimaging-based machine learning methods hold promise for tackling these issues, but large-scale, representative cohorts are required for building robust and generalizable models. The European College of Neuropsychopharmacology Neuroimaging Network Accessible Data Repository (ECNP-NNADR) addresses this need by collating multi-site, multi-modal, multi-diagnosis datasets that enable collaborative research. The newly established ECNP-NNADR includes 4,829 participants across 21 cohorts and 11 distinct psychiatric diagnoses, available via the Virtual Pooling and Analysis of Research data (ViPAR) software. The repository includes demographic and clinical information, including diagnosis and questionnaires evaluating psychiatric symptomatology, as well as multi-atlas grey matter volume regions of interest (ROI). To illustrate the opportunities offered by the repository, two proof-of-concept analyses were performed: (1) multivariate classification of 498 patients with schizophrenia (SZ) and 498 matched healthy control (HC) individuals, and (2) normative age prediction using 1170 HC individuals with subsequent application of this model to study abnormal brain maturational processes in patients with SZ. In the SZ classification task, we observed varying balanced accuracies, reaching a maximum of 71.13% across sites and atlases. The normative-age model demonstrated a mean absolute error (MAE) of 6.95 years [coefficient of determination (R2)=0.77, P<.001] across sites and atlases. The model demonstrated robust generalization on a separate HC left-out sample achieving a MAE of 7.16 years [R2=0.74,P<.001]. When applied to the SZ group, the model exhibited a MAE of 7.79 years [R2=0.79, P<.001], with patients displaying accelerated brain-aging with a brain age gap (BrainAGE) of 4.49 (8.90) years. Conclusively, this novel multi-site, multi-modal, transdiagnostic data repository offers unique opportunities for systematically tackling existing challenges around the generalizability and validity of imaging-based machine learning applications for psychiatry.

Background Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N < 50). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions. Methods Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD ( N = 664 in activation analyses, N = 474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N = 290 in activation analyses, N = 214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces. Results Compared to controls, LMDD was associated with increased activations in left amygdala ( P FWE = 0.031 , k E = 4) and left DLPFC ( P FWE = 0.002 , k E = 33), increased mean bilateral amygdala activation ( β = 0.0715 , P = 0.0314), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time. Limitations Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time. Conclusions LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of ‘bottom-up’ limbic-prefrontal effective connectivity in depression.

Background The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. Methods We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. Results In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. Conclusion Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Background: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. Methods: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level.International datasets are being shared from multi-ethnic community populations, first-episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. Results: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. Conclusion: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.