Nature Mental Health

Approximately 30% of U.S. adolescents have consumed alcohol, and earlier onset is associated with faster escalation and greater risk of adult alcohol use disorder. Predicting not only who will initiate drinking but also when could enable more timely and targeted prevention. Existing machine-learning approaches have largely classified drinking status or predefined trajectory classes, requiring fully observed outcomes and often excluding adolescents who remain abstinent at last follow-up. Many also rely on neuroimaging or small, hand-selected feature sets, limiting scalability. We developed a stacked-encoder survival framework for predicting drinking-onset timing from baseline clinical data alone. The framework compresses 1,582 clinical features using an unsupervised denoising autoencoder and re-embeds them with FocalTab, a TabPFN encoder fine-tuned with focal loss; the encoders are then frozen for survival modeling using a neural Cox or random survival forest head. Among 661 NCANDA adolescents who were non-drinkers at baseline, 495 initiated drinking within six years. The stacked representation outperformed single-encoder and raw-feature models, achieving an Uno C-index of 0.7654 and time-dependent AUC of 0.8274. Focal-loss encoding outperformed cross-entropy encoding, while SHAP identified alcohol expectancies, substance access, and community prevention engagement among the most influential predictors. Unsupervised clustering further identified five biotypes with a monotonic gradient in mean time to onset (4.30 to 2.86 years) and differential limbic and thalamic neuroimaging phenotypes. These findings support the feasibility of scalable, clinically based prediction of drinking-onset timing and data-driven adolescent risk stratification.

Robust sex differences in brain morphology and function exist from birth onwards. While traditionally conceptualized as categorical, they were recently shown to be best described along a female-to-male continuum. Here, we probed the under-explored implications of this proposal for understanding early life vulnerability to psychiatric conditions with sex-biased prevalence. We analyzed environmental, clinical and resting state functional neuroimaging data from 411 participants in Developing Human Connectome Project (dHCP) and 214 children from the Oregon ADHD 1000 study. In the dHCP, we identified complementary patterns of neonatal alignment with sex-typical young adult architecture, which overlapped areas rich in nicotinic cholinergic receptors and predicted the same psychiatric symptoms across sexes in toddlerhood. Specifically, for association (compared to sensorimotor) areas, greater alignment with female-typical functional connectivity (FC) patterns foreshadowed higher anxiety, whereas greater alignment with male-typical FC patterns forecasted autism-related problems. The latter effect was weaker among toddlers with an older epigenetic age at birth and exposure to fewer maternal health problems, but greater environmental enrichment. In the independent child sample, patterns of alignment with sex-typical FC, overlapping those observed in neonates, distinguished a history of depression and generalized anxiety (greater alignment with female-typical FC) from a history of separation anxiety, behavioral problems and current ADHD (greater alignment with male-typical FC). Our study speaks to the psychiatric relevance of sex-related functional brain architecture from birth onwards, its value in identifying vulnerable youths and its promise in monitoring the success of environmental interventions.

Worry is an important contributor to anxiety and depressive disorders, particularly in adolescence. Underlying changes in brain networks are thought to drive development of pathological worry, yet the degree to which neural correlates of worry vary by age is unknown. Here, we use multivariate pattern analysis (MVPA) to investigate individual variance in brain activity during worry in a cross-sectional sample of adolescents and adults varying in worry symptoms. Participants (n=69; ages 12-31) completed a personalized worry-induction fMRI task. Brain activity was averaged across k=268 brain regions from the Shen atlas. Participants also completed self-reports assessing trait worry (PSWQ), anxiety (SCARED), and depression (MFQ) severity. A random-forest model was trained on estimated parameters from worry vs. neutral blocks using leave-one-subject-out cross-validation. Age and symptomatology measures were correlated with model performance metrics. The overall cross-validated model had accuracy=66.71%, balanced accuracy=62.61%, sensitivity=80.43%, and specificity=44.78%. Trial-level accuracy increased with in-scanner worry ratings regardless of condition (b =0.62, p<.001). Accuracy (r=0.30, p=.01) and balanced accuracy (r=0.31, p=.01) significantly correlated with age but did not relate to symptom severity. Brain patterns associated with worry become more differentiated across adolescence, suggesting that neural processes of worry consolidate across this period. Thus, early adolescence may be an opportune time for interventions to alter worry, but interventions adapted from adult populations may require tailoring for adolescent patients.

Understanding the mechanisms that underpin the pervasive sex bias in risk for specific psychiatric and neurodegenerative conditions could substantially advance efforts promoting personalized medicine. As a first step towards addressing this issue, we applied imaging transcriptomics and multivariate analysis techniques to data from an adolescent sample enriched in anxiety/depression cases, a child sample enriched in attention deficit hyperactivity disorder (ADHD) cases, and adult patients with treatment resistant major depressive disorder (MDD). We tested whether, across sexes, alignment with female-typical, rather than male-typical, patterns of neural functional connectivity (FC) would relate to a diagnosis (adolescent sample) or severity of symptoms (adult sample) associated with internalizing disorders, which tend to be more often diagnosed in females. To provide preliminary evidence on its functional specificity, we compared the sex-aligned FC profile that distinguished neurotypical adolescents from those diagnosed with depression/anxiety against the sex-aligned FC patterns that distinguished neurotypical children from those diagnosed with ADHD. All the analyses controlled for age within each sample and sex. Finally, we examined the spatial overlap between the sex-aligned FC profile linked to an internalizing diagnosis in adolescence and the transcriptomic maps of genes that show sex-dependent dysregulation in neurodegenerative conditions. Across sexes, increasing alignment with female-typical FC patterns, from sensory-perceptual to default mode regions, discriminated between typically developing and anxious/depressed teens and tracked longitudinal fluctuations in depressive symptoms within adult MDD patients. The identified internalizing-related gradient showed a negative correlation with risk for ADHD in the child sample. The brain regions most strongly aligned with female-, rather than male-, typical FC in depression/anxiety overlapped the spatial expression profiles of genes linked to gonadal hormone synthesis, as well as genes dysregulated in female autism spectrum disorder (ASD) and male neurodegenerative conditions, specifically, Alzheimer's Disease (AD), Huntington's Disease (HD) and Multiple Sclerosis (MS). The brain areas most strongly aligned with male-typical, rather than female-typical, FC patterns overlapped the transcriptomic maps of genes related to androgen and estrogen receptors, as well as of genes dysregulated in male ASD and female neurodegenerative conditions (i.e., AD, HD, MS). Our results raise the possibility that the link between mood pathology and neurodegeneration could reflect a partial, regional reversal of sex-normative functional brain profiles.

Deep brain stimulation (DBS) can be an effective treatment for severe obsessive-compulsive disorder (OCD), but response is variable, and objective, reliable predictors of who will respond positively are absent. In three individuals with OCD, we implanted intracranial electrodes across the cortico-striato-limbic network before DBS. Prefrontal gamma activity, most prominently in the orbitofrontal cortex (OFC), distinguished high- from low-symptom OCD states reproducibly across days. Acute stimulation of the ventral capsule/ventral striatum relieved OCD symptoms and, in one patient, significantly shifted this OFC gamma signal. Effective connectivity from therapeutic stimulation sites to these prefrontal regions defined three patient-specific OCD subnetworks; the baseline strength of the nucleus accumbens/ventral pallidum-OFC subnetwork predicted acute and long-term DBS response. Imaging-based modeling of current propagation in stimulation-activated ventral capsule to the OFC tracked acute response, suggesting that this prediction could be approximated from imaging alone. These findings outline a predictive, subnetwork-level framework for personalized DBS in OCD.

Cerebral cortical maturation supports emotional, social, and cognitive development during adolescence1. Neurodevelopmental theories of psychiatric illnesses, which peak in onset around age 182, implicate subtle alterations in adolescent cortical development3. However, mechanistic links between cortical maturation and emergent psychopathology have remained elusive. This gap likely reflects a need for more nuanced understanding of cortical development4, larger, longitudinal samples powered to detect small effects,5 and more uniform image quality control.6 Using 29,482 structural MRI scans from 11,720 adolescents in the Adolescent Brain Cognitive Development (ABCD) study, we demonstrate reproducible differences in the contour and tempo of maturation across the cortical surface. Regional trajectories are robust to image quality variance and align with increasing functional specialization over time. Thicker cortex in early-maturing sensory regions predicts psychiatric symptoms as early as four years prior to their emergence, a pattern that subsequently spreads to association cortex. Overlap with spatially and temporally organized gene co-expression networks implicates prenatal programming of this sequence of maturation and its links to psychopathology. These findings present a novel, versatile neurodevelopmental framework for cortical maturation during adolescence that sequentially implicates sensory and association cortex in risk for emergent psychiatric symptoms.

Introduction: Major depressive disorder (MDD) is a highly prevalent and disabling psychiatric disorder. Human neuroimaging studies increasingly frame its neurobiological substrate in terms of alterations of large-scale brain network organization. Resting-state fMRI findings broadly align with this view, yet remaining highly heterogeneous, reflecting both clinical and analytic variability. Predominant approaches largely characterize functional organization in terms of pairwise relationships between regions, which may limit the ability to capture more global features of brain organization associated with depression and its course. Here we use novel methods from Topological Data Analysis to provide new perspectives on these brain-behavior associations Objectives: The main objective of this study was to determine whether whole-brain topological descriptors of functional connectivity capture alterations associated with MDD. In particular, we examined whether these features vary along clinically relevant dimensions of heterogeneity, focusing on illness course (single-episode vs. recurrent MDD) and current antidepressant medication status. To this end, we analyzed the area under the curve (AUC) of the zeroth and first Betti numbers (B and B), which index global network integration and higher-order cycle structure across scales, respectively. Methods: Neuroimaging (resting state fMRI) and phenotypic data were obtained from 1,490 participants (776 individuals with MDD and 714 never-depressed controls) included in the REST-meta-MDD project of the DIRECT consortium. Functional connectivity matrices were computed using Pearson correlations between regions defined by the Power-264 atlas and harmonized across sites using CovBat. Topological Data Analysis was applied to characterize whole-brain network organization across connectivity thresholds using B and B curves and their areas under the curve (AUC). B associated cycle counts and their coarse anatomical configurations were further examined. Group differences and effects of illness course and medication status were assessed using multiple linear and generalized linear regression models. Results: Compared with controls, individuals with MDD showed significantly higher B AUC, indicating altered global integration profiles across connectivity thresholds. B AUC did not differ by illness course, illness duration, or symptom severity. In contrast, B AUC showed a graded increase across illness courses, with the highest values observed in recurrent depression. This effect was driven by an increased number of one-dimensional cycles rather than greater cycle persistence. Medication status further modulated B alterations, with elevated values observed in unmedicated single-episode and medicated recurrent MDD. Anatomical decomposition revealed that higher-order alterations were primarily driven by inter-network configurations spanning multiple large-scale functional systems. Discussion:These findings indicate that MDD is associated with altered multiscale functional organization, combining less compact global integration with an increased prevalence of cross-network hole-defining cycles, with these features varying systematically with illness course and current medication, suggesting that topology-informed measures capture clinically relevant variability in brain organization in depression.

Circadian disruption is a transdiagnostic feature of psychiatric illness, particularly in bipolar disorder (BD), where evening chronotype is linked to greater symptom severity. The suprachiasmatic nucleus (SCN) times pineal gland (PG) melatonin synthesis through a well-described feedforward pathway. Moreover, melatonin can also phase-shift the SCN-pacemaker, consistent with a functional feedback limb. Directed effective connectivity between the SCN-containing hypothalamus and the PG has not been estimated in human neuroimaging, and it is unknown whether that directed coupling varies by psychiatric diagnosis or chronotype. We applied cross-spectral dynamic causal modeling (DCM) to resting-state fMRI data in 431 UK Biobank (healthy controls [HC, N=136], BD [N=78], major depressive disorder [MDD, N=154] and psychotic disorders [PY, N=63]). Random-effects Bayesian model selection compared a fully connected model with reduced archiature and, separately, feedforward-only (SCN[-&gt;]PG) vs feedback-only (PG[-&gt;]SCN) models. A per-subject log Bayes factor difference (dF) indexed directionality preference and was tested against diagnosis, chronotype and their interaction using ANCOVA. The fully connected model was strongly preferred (posterior probability=97.8%, protected exceedance probability [PXP]=1.00). Feedforward architecture dominated overall (70% vs 30%, PXP=1.00). Diagnosis did not predict dF (p=.403), but chronotype did (p=.036), and there was a significant diagnosis-by-chronotype interaction (p<.001). Feedforward dominance was strongest in evening chronotypes (p<.001). Model evidence favored a fully connected architecture with overall feedforward dominance. That preference varied by chronotype and, qualified by the interaction, was strongest in BD evening chronotypes. The pattern supports BD-evening chronotype as a high-risk subgroup and argues for routine chronotype assessment in BD care.

The hippocampus has long been implicated in depression, yet the underlying mechanisms remain incompletely understood. Leveraging precision functional mapping in densely sampled individuals and large cross-sectional cohorts, and validating key results in direct intracranial EEG recordings from the human brain, we show that hippocampal connectivity alternates between two discrete and opposing network states on the order of minutes: one dominated by default mode network (DMN) coupling and another by sensorimotor system (SM) coupling. This dynamic switching is a fundamental property of hippocampal function in both healthy brains and in major depressive disorder (MDD). In MDD, this equilibrium is disrupted, such that the DMN-dominant state is overrepresented. In densely sampled individuals, hippocampus-DMN coupling tracks symptom fluctuations over time and normalizes after electroconvulsive therapy (ECT), which drives switches in hippocampal connectivity away from the DMN-dominant state and toward the SM-dominant state, an effect that was closely linked to a positive treatment response. Direct intracranial recordings in patients with MDD identified analogous fluctuations in hippocampal-DMN theta synchrony, which tracks symptom fluctuations over hours and days. Together, these findings identify a dynamic cortico-hippocampal mechanism underlying depression and indicate that successful neuromodulation treatments such as ECT act in part by restoring a dynamic equilibrium in hippocampal network connectivity.

Language alterations are recognized as early markers of psychopathological vulnerability, yet genetic and environmental factors underlying language-related brain regions remain unknown. We applied univariate twin analyses and risk-discordant comparisons to multimodal dataset from 31 healthy twin pairs (9-31 years) and 17 pairs discordant for psychopathological risk (10-27 years). Gray matter volume (GMV), cortical thickness (CT), and functional interactions, specifically effective connectivity (EC), were assessed in eight bilateral language-related regions, alongside linguistic, emotional-behavioural, temperamental, and personality-related phenotypes. In healthy twin pairs (27-31 pairs, depending on data completeness), genetic and environmental determinants were estimated for all phenotypes. In 17 pairs discordant for psychopathological risk, intra-pair neuroimaging differences were tested for association with psychopathological discordance and zygosity, and individual brain measures were compared across high-risk twins, their low-risk co-twins, and healthy controls. Environmental factors predominated in structural and functional brain variability, while genetic contributions were limited, region-specific. Narrative fluency and psychological traits showed stronger heritability. Findings converged on the right middle frontal gyrus, where GMV and CT alterations reflected non-shared environment and risk expression. Despite the small sample, preliminary results suggest neurobiological vulnerability in language networks arises from environmental rather than genetic factors, informing environmental susceptibility to adolescent mental illness.

Chronic heavy alcohol use shifts brain metabolism from glucose toward acetate utilization. Sudden decreases in acetate following alcohol cessation in individuals with alcohol use disorder (AUD) may create an energetic imbalance that contributes to craving and withdrawal. A recent randomized study found that, compared with a standard American (SA) diet, a ketogenic diet (KD) during alcohol detoxification reduced withdrawal severity and craving and increased ketones and glutamate in the dorsal anterior cingulate cortex. These findings suggest that ketones may support metabolic adaptation during alcohol withdrawal, but the effects of altered brain metabolism on neural dynamics and alcohol cue reactivity remain unknown. Here, we used a control theoretic approach to test whether shifts in cerebral energy substrate utilization influence large-scale network dynamics through altered information flow. We analyzed fMRI cue-reactivity scans collected after one, two, and three weeks of inpatient treatment during presentation of alcohol, food, and neutral cues. Whole-brain effective connectivity was estimated using Shannon transfer entropy applied to whole brain atlas-aligned fMRI time series. Assuming neural activity flow along effective connections, we identified distinct shifts in the state-transition landscape in participants receiving the KD compared with the SA diet. Increases in serum {beta}-hydroxybutyrate (BHB) were associated with changes in state-transition landscape organization, which were in turn associated with reduced cue-induced alcohol craving. These findings link metabolic adaptation to large-scale neural dynamics and clinically relevant behavior and suggest that cerebral energy substrate availability may shape behavior by altering the organization of large-scale neural dynamics.

Genetic studies of serious mental illness (SMI, including schizophrenia and bipolar disorder) have implicated two classes of risk variant: common variants1-3, each of small effect but collectively accounting for most population-level risk, and ultra-rare coding variants4,5 which confer large risk in a small number of individuals. Most such studies cannot directly assess the impact of specific variants on SMI, transdiagnostically, as the phenotypic information that they collect is focused on a single diagnosis. Here we report results of association analysis of predicted deleterious variants from exome sequencing of 21,958 cases (SMI and severe major depressive disorder) and 19,826 controls from the Mision Origen biobank in the Paisa genetic isolate of Colombia. We identified transdiagnostic associations to seven genes, three at an exome-wide and four at a false discovery rate significance threshold. Five of these genes have not previously been implicated in SMI (ADAP1, WIPI1, NFE2L1, FBXO10, and HLA-DPB1 ); for the first four of these the association is driven by variants enriched in the Paisa due to a founder effect, filling the gap between ultra-rare and common variants. The strongest association that we observe is to a frameshift variant (7:904169:T:TC) in ADAP1 that is genome-wide significant for SMI, schizophrenia, and bipolar disorder and increases risk for both diagnoses by more than threefold; it truncates ADAP1, a brain-enriched Arf6 GTPase-activating/scaffolding protein that has been mechanistically linked to post-mitotic neuronal morphogenesis and regulation of dendritic differentiation6. Variant carriers differ significantly from other SMI cases in Mision Origen for psychotic, manic, and cognitive item-level phenotypes assessed through analyses of electronic health records7-9 and displayed impaired executive function on neurocognitive testing. Variant carriers differ significantly from other SMI cases in Mision Origen for multiple item-level phenotypes (psychotic, manic, and cognitive) assigned through natural language processing of longitudinal electronic health records. Although the 7:904169:T:TC variant is not significantly associated with major depressive disorder, carriers compared to others with this diagnosis display a significant increase in symptoms typical of bipolar disorder. The variant's high allele frequency in Mision Origen (~0.4%) will enable population-level studies to stratify SMI based on causation and identify additional factors influencing risk and resilience.

Brain network development differs between neurodivergent (ND) and neurotypical (NT) youth, but its developmental trajectory, and relationship to clinical outcomes, remains unclear. We examined age-related changes in functional network connectivity and clinical symptoms in 861 participants (5-21 years) from the Healthy Brain Network, including NT youth and those with Neurodevelopmental Disorders (NDDs) and generalized anxiety disorder (GAD). Functional connectivity during movie-watching was modeled using generalized additive models across eight large-scale networks to characterize nonlinear developmental trajectories. Divergence in connectivity profiles between ND and NT groups first emerged during late childhood and were largest during adolescence. Importantly, divergence in connectivity patterns followed a similar trajectory path, but preceded, divergence in clinical outcomes. These findings identify adolescence as a sensitive period when differences in patterns of brain activity and clinical differences become increasingly pronounced, highlighting a potential window for targeted intervention.

Major depressive disorder (MDD) is biologically heterogeneous, but how this variability is expressed in the temporal organization of brain activity remains unclear. Using resting-state functional MRI from 2,225 participants, we applied hidden Markov modeling to identify recurrent brain states and quantify their temporal organization. Five states formed a shared repertoire across individuals with MDD and healthy controls, while MDD-related alterations were distributed across states, functional systems, and temporal properties. Within MDD, unsupervised clustering of state occupancy, persistence, and switching delineated two prototype neurodynamic architectures characterized by preferential deployment of ACS/VSS or MAS/DMS states within a preserved state repertoire and broadly similar transition backbone. The architectures showed different within-group brain-symptom associations and symptom-network patterns. Connectome transcriptome analyses organized architecture-state imaging patterns into three transcriptomic state families shared across architectures, and contrasting group-level deployment patterns were recovered in an independent MDD cohort. These findings suggest that MDD heterogeneity is expressed not only through spatially distributed neural alterations, but also through differences in how a common repertoire of recurrent brain states is organized over time.

Age of first alcohol use is associated with alterations in brain development and an increased risk of problematic substance use later in life. However, it remains unclear if neurobiological alterations associated with first-use age are present in young adulthood and how these alterations may be associated with different patterns of adult substance use. In this study, we employed SuperBigFLICA, a semi-supervised multimodal imaging data fusion approach, to identify joint structural morphometric and functional MRI (fMRI) brain profiles present in adulthood that are most predictive of age of alcohol first-use in the Human Connectome Project-Young Adult cohort (n=622). Our model achieved moderate performance (r=0.22 in external validation), identifying multimodal structure-function profiles predictive of first-use largely involving frontal regions. We then utilized Canonical Correlation Analysis to determine whether these multimodal brain profiles were differentially linked to patterns of adult substance use behaviors. We identified three significant brain modes (Mode 1: r = 0.2750, p = 0.014; Mode 2: r = 0.2249, p = 0.005; Mode 3: r = 0.1568, p = 0.029) associated with distinct profiles of alcohol and cannabis use. Together, we identified multimodal brain patterns in young adults that predict age of first use of alcohol and demonstrated that resulting multimodal structure-function brain profiles capture distinct multivariate patterns of adult substance use, providing a more integrated view of substance use neurobiology.

The adolescent social environment and dynamics therein have been linked to divergent brain organization and greater risk of psychopathology later in life, suggesting the neural underpinnings of mental health may depend on social context. To examine this, we leveraged longitudinal ABCD Study data to identify dynamic social envirotypes, or distinct trajectories of social environment change over four years. Dynamic social envirotypes differed in mental health outcomes, changes in mental health, and whole-brain functional connectivity, with each dynamic social envirotype exhibiting a characteristic pattern of brain network organization. Crucially, functional connectivity associations with internalizing and externalizing problems diverged across dynamic social envirotypes, with the highest and lowest social resource envirotypes showing largely opposing associations between connectivity and symptom severity. These findings suggest that dysconnectivity is contextual: there is no single brain connectivity pattern that is universally associated with better mental health. Rather, adolescent brain connectivity relates to mental health in context-specific ways, challenging normative narratives of the neural underpinnings of psychopathology and centering the role of context for understanding and treating mental health.

Background: Biological risk scores provide objective markers of bipolar disorder (BD) liability. Large-scale genome-wide association studies (GWASs) have led to polygenic risk scores (PRSs) that capture genetic liability to BD at the individual level. We lack a comparable large-scale effort to leverage a brain-wide association study (BWAS) to create an imaging risk score (IRS) that captures brain structural liability to BD at the individual-level. Here, we conduct the largest multivariate BWAS of BD and develop a BD IRS. We compare the BD IRS to the PRS and assess their combined predictive performance in multimodal risk score (MRS) models. Methods: Our multivariate BD BWAS used 153 structural neuroimaging features from 3902 cases and 6855 controls (mean age (sd)=35.47 years (13.42), 56.70% female) in a cross-validated machine learning approach. Using the resulting model coefficients, we constructed an IRS. We then evaluated the out-of-sample performance of the IRS, PRS derived from the largest GWAS to date, and combined MRS model using logistic regression in a subset of individuals with imaging and genetic data (nBD=486, ncontrols=1701, age (sd)=33.10 (12.02), 56.47% female). We also performed stratified analyses for demographic and BD clinical groups. Prediction performance was quantified using the area under the receiver operating characteristic curve (AUC). Subsequent analyses examined associations between each risk score and BD relevant symptoms in developing youth. Outcomes: In predicting case status, the BD BWAS model achieved a mean AUCBD of 0.62 (sdBD=0.08) and in stratified analyses, had higher prediction than subgroup-specific BWAS models. The BD type II BWAS was the only exception (AUCBDII=0.60, sdBDII=0.07 vs AUCBD=0.58, sdBD=0.09). The BD PRS had an AUCBD of 0.65 (CI95%=0.63-0.68) and a higher AUC than subtype-specific PRS in stratified analyses. The PRS typically attained a higher AUC than the IRS. Notably, the IRS and PRS were uncorrelated (r=0.03, p=0.11). MRS models outperformed the prediction of individual PRS and IRS with the strongest prediction for BD type I (AUCBD=0.74, CI95%=0.70-0.78). The IRS had the strongest associations with BD-related symptoms in developing youth including increased energy (OR=1.21, pfdr=0.03) and distress from psychotic-like experiences (OR=1.23, pfdr=0.04). Interpretation: Our BD IRS is a novel biological indicator of BD risk. The lack of correlation between the IRS and PRS along with improved MRS prediction suggests the two risk scores capture some orthogonal liability to BD. We show that integrating imaging and genetics is useful for biological characterization of BD and should be considered for the development of clinically relevant prediction models.

Importance: Anhedonia, social withdrawal and executive deficits represent transdiagnostic symptoms in depression, schizophrenia and addition, yet transdiagnostic brain volumetric alterations and their contribution to these symptom domains have not been systematically characterized. Objective: To determine common and disorder-specific gray matter deficits in patients with schizophrenia (SZ), major depression disorder (MDD) and alcohol use disorder (AUD) compared to heathy controls. Data Sources: A comprehensive literature search using three databases including PubMed, Web of Science and PsycInfo (between January 2000 to March 2024) as well as searches on review articles. Study Selection: Studies had to be published in English, reporting whole-brain results, comparing patients and healthy controls. Data Extraction and Synthesis: This meta-analysis adhered to the PRISMA guidelines and was pre-registered (https://osf.io/s57uq) prior to data analysis. Data was independently extracted and verified by 3 contributors, and statistical brain maps were generated using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) random-effects model. 147 VBM studies encompassing 90 Schizophrenia, 44 MDD and 13 AUD studies were finally included. Main Outcomes and Measures: Statistical maps of common and disorder-specific gray matter deficits in patients versus healthy controls are presented. Statistical inference utilized permutation tests, with threshold at p < .0025 and cluster extent of 10 voxels. Heterogeneity was evaluated using the I2 statistic and publication bias using Eggers regression test. Results: Capitalizing on a coordinate-based meta-analysis involving n=13,041 participants, we found shared GMV alterations in the ventromedial prefrontal cortex, ventral anterior cingulate cortex, insula, superior temporal gyrus. On the network level, we found robust functional interactions of the identified shared regions with the cortical midline structures as well as social and theory of mind-related behavioral functions. Subsequent characterization analyses highlighted the involvement of dopaminergic and serotonergic neurotransmitters as well as associated genetic annotations. Conclusions and Relevance: Overall, the present meta-analysis found common as well as separate GMV abnormalities in schizophrenia, major depressive disorder and alcohol use disorder suggesting a common neurostructural substrate and treatment target for social withdrawal and anhedonia across these disorders. Keywords: alcohol-use disorder, depression, meta-analysis, schizophrenia, voxel-based morphometry

Socio-cognitive alterations are widely observed across different psychiatric conditions, yet it remains unclear whether these deficits map onto distinct diagnostic categories or represent a shared transdiagnostic phenotype, driven by dimensional psychopathological symptom severity.This study evaluates Theory of Mind (ToM) network activation during a naturalistic functional magnetic resonance imaging (fMRI) movie paradigm in 313 participants, including 91 healthy controls and 222 psychiatric inpatients across five distinct diagnostic clusters (substance use, schizophrenia spectrum, affective, anxiety, and personality disorders). Frequentist analyses revealed decreased ToM-selective activity in psychiatric patients, compared to controls across all ToM regions of interest (ROIs). No significant difference emerged between diagnostic groups, but ROI activation correlated negatively with Adult Self-Report (ASR) scales (Total Problems, Internalising, Externalising). Subsequent Bayesian analysis of covariance (ANCOVAs) consistently favoured models that excluded traditional categorical diagnoses, suggesting that dimensional measures, especially Total Problems and Internalising, provided substantially greater predictive utility for ToM-related regional brain activation. Our findings challenge traditional categorical classifications in neurobiological research as they suggest that atypical mentalising might reflect a shared transdiagnostic vulnerability.

Brain-imaging models predicting psychiatric outcomes are rarely tested where they would be used. Clinical high risk for psychosis is a demanding test case because services differ in whom they recruit and how they record transition. We re-estimated a published structural magnetic resonance imaging (MRI) risk model and applied it unchanged to four independent cohorts (685 participants, 106 transitions). Discrimination was weak (area under the curve, 0.56) and near chance in the two cohorts not previously used to evaluate the model (0.53 and 0.56). Predicted risks did not transfer (calibration slope, 0.17). Exploratory performance landscapes, which map local discrimination across participant characteristics, showed that discrimination varied with negative-symptom severity, also in external cohorts, whereas apparent age- and brain-age-related variation depended on single cohorts; no global test survived multiple-testing correction. Reporting where a model works, not only its average accuracy, can reveal when one diagnostic label hides several prediction problems.