BACKGROUND:Emotion regulation relies on the interplay between prefrontal and limbic brain regions, with prefrontal regions implicated in the top-down modulation of the amygdala. In social anxiety disorder, disruptions in these networks have been reported, but most studies used undirected functional connectivity. AIMS:Dynamic causal modelling (DCM) was used to assess effective (i.e. directed) connectivity differences during emotion processing and regulation in individuals with social anxiety disorder compared with healthy controls. METHOD:A total of 102 participants (61 with social anxiety disorder, 41 healthy controls) performed a functional magnetic resonance imaging emotion regulation task under two conditions: viewing neutral/negative faces, and downregulating emotions using a self-chosen strategy. DCM was applied to model effective connectivity among the amygdala and key prefrontal regions. Connectivity patterns were characterised in healthy controls, and group comparisons tested how social anxiety disorder differed from this baseline model using parametric empirical Bayes. Leave-one-out cross-validation (LOOCV) evaluated whether connectivity differences predicted diagnostic group, symptom severity and emotion regulation difficulties. RESULTS:In healthy controls, observation of negative faces was characterised by reciprocal influences between the amygdala and prefrontal cortex (PFC), including increased amygdala-to-ventromedial PFC (vmPFC) connectivity and inhibitory vmPFC-to-amygdala connectivity. During emotion regulation, healthy controls showed negative modulation from the amygdala to all prefrontal regions. Patients with social anxiety disorder did not differ from controls in amygdala-prefrontal connectivity; their alterations were confined to prefrontal circuits, with inhibitory connectivity from the pre-supplementary motor area (preSMA) to dorsolateral PFC during observation and bidirectional excitatory connectivity between the preSMA and vmPFC during regulation. LOOCV indicated that connectivity differences predicted diagnostic group. CONCLUSIONS:The results support the idea that emotion processing and regulation influence connectivity between prefrontal areas and the amygdala in a complex, feedback-driven manner. Our findings suggest that aberrant emotion regulation in social anxiety disorder appears to be more closely linked to differences in intra-prefrontal circuits than deficits in amygdala-prefrontal connectivity.

BACKGROUND:Childhood maltreatment (CM), encompassing abuse and neglect, is highly prevalent and associated with elevated risk for major depressive disorder (MDD), posttraumatic stress disorder (PTSD), and other related conditions. However, the extent to which neuroanatomical alterations in MDD and PTSD are attributable to CM is uncertain. METHODS:Here, we analyzed CM and whole-brain magnetic resonance imaging (MRI) data from 3711 participants in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) MDD and PTSD Working Groups (25 sites; mean age = 33.3 ± 13.0 years; 59.9% female). Normative modeling estimated deviation z scores for 14 subcortical volume, 68 cortical thickness (CT), and 68 surface area (SA) measures. To identify transdiagnostic effects, associations between CM and brain deviation scores were evaluated across all participants (patients and healthy control participants) stratified by sex and 3 age bins (pediatric, young adult, older adult). RESULTS:In young adults (ages 18-35), abuse was associated with larger volumes in the thalamus and pallidum, thinner isthmus cingulate and middle frontal regions, and thicker medial orbitofrontal cortex; there were no significant effects in pediatric (≤18 years) participants. The strongest effects were observed in young female adults (|β| = 0.07-0.22, q < .05): Greater abuse and neglect were correlated with smaller hippocampus and putamen volumes, thinner entorhinal cortex, and smaller SA in fusiform/inferior parietal regions and with larger SA in the orbitofrontal and occipital cortices. In males, abuse had widespread effects on CT and SA (|β| = 0.1-0.18, q < .05); effects for neglect were minimal. CONCLUSIONS:Our findings of age- and sex-specific instantiations of CM on brain morphometry highlight the importance of developmental context in understanding how adverse experiences shape neurobiological vulnerability to MDD and PTSD.

BACKGROUND:Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS:Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS:BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS:Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS:The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.

Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

Social anxiety disorder (SAD) is among the most prevalent anxiety disorders, and it has been associated with signs of advanced biological ageing. Despite this, brain age research on anxiety disorders remains limited. This mega-analysis investigated brain ageing in adults with SAD within the ENIGMA-Anxiety Working Group. Structural MRI scans from 576 participants with SAD and 1 355 non-affected healthy controls (HCs) across 26 international samples were included. Brain age was estimated from 77 cortical and subcortical regions using a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as the difference between brain age and chronological age. Group and subgroup differences (comorbidity, medication) were assessed using linear mixed-effect models. In the full sample, there was no group difference in brain-PAD ( β diagnosis (SE)=0.70 (0.37) years, p =0.061). In a subgroup of participants with SAD with comorbid anxiety disorders (n=184 SAD, n=1 355 HCs), a brain-PAD of +2.39 (0.93) years (Cohen's d =0.23, p FDR=0.003) was observed. This brain-PAD became smaller after exclusion of participants with comorbid agoraphobia and specific phobia, suggesting that these disorders may partly drive the advanced brain-PAD. In conclusion, this ENIGMA-Anxiety mega-analysis did not find evidence of advanced brain ageing in the full sample of adult participants with SAD relative to HCs. However, a sub-analysis suggested that SAD with co-occurring phobic disorders, or the phobic disorders themselves, are associated with neurostructural patterns typical of older brains. Future research could utilise transdiagnostic samples with information on age of onset and disorder duration to further clarify this relation.

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, limited brain age research has been conducted in anxiety disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group. 3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effects models examined group differences in brain-PAD and moderation by age. No significant group difference in brain-PAD manifested (βdiagnosis [SE] = 0.37 years [0.43], p = 0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (βdiagnosis-by-age = -0.08 [0.03], pFDR = 0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; βdiagnosis = 1.20 [0.60], p < 0.05, mixed-effects d [95% confidence interval] = 0.14 [0.00-0.28]), but not older participants (36-75 years; βdiagnosis = 0.07 [0.65], p = 0.91). Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post hoc analyses showed subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment, and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

MRI studies in bipolar disorder (BD) have yielded inconsistent findings, partly due to the varied use of psychotropic medications. This study utilised a mega-analysis approach, accounting for concurrent medication status (syndrome-based and Neuroscience-based Nomenclature (NbN) classifications), in order to assess the association of medication status with subcortical brain volumes in BD. Data from 2,664 BD patients and 4,065 controls (CN) were pooled from 34 research groups as part of the ENIGMA Bipolar Disorder Working Group. Standardized ENIGMA protocols were used to measure subcortical brain volumes. Linear-mixed-effects regression evaluated the association between psychotropic medications and subcortical volumes, and moderation analyses explored interactions. Medication-free patients (n = 410) showed mild ventricular enlargement (d = 0.07) and increased putamen volume (d = 0.06) compared to CN. Patients taking psychotropic medications exhibited smaller subcortical volumes (d = -0.06 to -0.11) and larger ventricles (d = 0.11 to 0.19). Use of antiepileptic and antipsychotic medications was associated with smaller hippocampal and thalamic volumes (d = -0.07 to -0.14), while NbN classification indicated that the categories of ‘valproate’ and ‘dopamine and other monoamine receptor antagonists’ are key variables when considering volume differences between BD and CN. Concurrent lithium use weakened the negative association between antiepileptic use and hippocampal volume (β = 0.19, q = 0.038) in patients. Medication status is associated with altered subcortical brain volumes in BD. The NbN classification provides a useful framework for future studies, emphasizing the need for comprehensive longitudinal research to further unravel complex clinical-pharmacological-neurobiological interactions in BD.

Recent research suggests that brain anatomy may help identify the most effective pharmacological treatment for each individual with bipolar disorder and reduce trial-and-error prescribing. We aimed to investigate whether brain anatomy predicts whether a medication is currently prescribed or has been discontinued, as a proxy for treatment effectiveness. The rationale is that medications that provide clinical benefit without unacceptable side effects are likely to be continued, whereas those with limited benefit or poor tolerability are typically discontinued. We used T1-weighted MRI from twelve ENIGMA-BD cohorts (n = 2462; 473 individuals with BD [61% female, age 18-73] and 1989 controls) to derive regional cortical thickness and surface area and subcortical volumes. Site differences were harmonized using ComBat models fitted on controls' data. Within cross-validation, models were trained to first adjust for cumulative dose and other covariates and then predict medication status. On test sets, current prescription (vs. discontinuation) of lithium was predicted by greater cortical thickness and reduced surface area, whereas current prescription (vs. discontinuation) of antidepressants and atypical antipsychotics was predicted by greater cortical thickness. Predictive regions for atypical antipsychotics were generally consistent across subgroups of age, gender, illness duration, and history of psychosis, and in the largest site, and differed from those associated with cumulative effects of medication on the cortex. Predictions were poor for subcortical volumes and for antiepileptic mood stabilizers and typical antipsychotics. These findings provide preliminary support that cortical anatomy may help inform future development of biomarkers for treatment selection, pending validation in longitudinal studies.

Current psychiatric neuroimaging supports the view that major depressive disorder (MDD) is a dysconnection syndrome, characterized by structural brain dysconnectivity. Recent studies investigating this question, however, did not evaluate the involvement of comorbid disorders, of which anxiety disorders (ANX) are particularly prevalent. Here, we investigated the structural connectivity alterations observed in MDD with and without comorbid ANX. To this end, we reconstructed structural brain networks of n = 781 individuals with a diagnosis of MDD who had at least one diagnosis of an ANX (n = 249) and those without any diagnosis of ANX (n = 532), as well as n = 906 healthy controls (HC) from structural and diffusion-weighted MRI. The network-based statistic (NBS) toolbox was employed to evaluate network-level differences in structural connectivity among the three groups. Transdiagnostic analyses were conducted to explore the dimensional relationship between anxiety and structural connectivity. NBS revealed decreased structural connectivity in MDD patients without comorbid ANX and increased structural connectivity in MDD patients with comorbid ANX relative to HC, with both effects found in spatially overlapping white matter connections. Transdiagnostic analyses suggested that increases in anxiety were associated with increased structural connectivity across all groups. Our finding that hyperconnectivity rather than hypoconnectivity characterizes the structural connectome of MDD patients with comorbid ANX challenges the applicability of the dysconnection syndrome hypothesis to MDD with comorbid ANX, warranting symptom-based investigations of brain changes in mental disorders.

The clinical and biological heterogeneity of major depressive disorder (MDD) may reflect the aggregation of different conditions with distinct pathologies under a single diagnostic label. Neuroanatomical heterogeneity in MDD was examined using a harmonized, age- and sex-matched sample from the ENIGMA MDD consortium (N = 5146; age range: 9-82 years; 64% female). Analyses of global neurostrucutral variability revealed greater cortical thickness heterogeneity in MDD compared with healthy controls (Cohen's d = -0.26). Regionally, increased variability in cortical thickness was most prominent in the cingulate (+6.1 to +6.6% more variation in MDD) and insular (+5.8%) cortices, as well as in the frontal (+5.7 to +6.8%) and temporal (+6.1 to +6.8%) lobes. Heterogeneity in cortical thickness was more pronounced among patients using antidepressant medication (Cohen's d = -0.39). Patient-specific analyses further showed that individuals with markedly increased cortical thickness variability (<5th percentile relative to the normative range) exhibited greater depressive symptom severity than those within the normative range (5th-95th percentile; Cohen's d = 0.19-0.36). Overall, the results indicate that neuroanatomical heterogeneity in MDD is primarily expressed in cortical thickness, offering refined insights into the neurobiological complexity of structural alterations associated with depression. These findings could guide future stratification efforts examining whether regionally confined changes in cortical thickness within areas of pronounced variability reflect clinically meaningful patient subgroups.

Resilience, the ability to adapt positively in the face of adversity, is shaped by combined influences of risk and protective factors. Previous neuroimaging studies on resilience have predominantly focused on single factors, often operationalizing resilience dichotomously as the absence of psychiatric disorders despite adversity. In this prospective magnetic resonance imaging study, we defined resilience as “better-than-expected” depressive symptom severity (Hamilton Depression Rating Scale) relative to cumulative risk across 22 risk and protective variables. Using ridge-regularized regression in N = 1804 participants (955 healthy, 849 depressed) from the Marburg-Münster Affective Disorders Cohort Study, we predicted symptom severity and derived residuals as measures of resilience. Residuals were then used to predict gray matter volume (GMV) and cortical thickness at baseline (T1) and two-year follow-up (T2; N = 808). This approach was complemented by extreme-group comparisons of resilient (better-than-expected outcome) and vulnerable (worse-than-expected outcome) individuals. Cumulative risk explained 49.6% of variance in depressive symptoms at T1 and 40.1% at T2. Residual scores showed moderate temporal stability ( r = 0.32, p < 0.001). Region-of-interest and whole-brain analyses revealed no morphometric associations with resilience at T1. In contrast, higher resilience at T1 predicted lower GMV in the left inferior orbitofrontal gyrus (IOFG) and temporal pole at T2 (ROI, p FWE(peak) < 0.001, r partial =0.18), with no changes in cortical thickness. Taken together, resilience to cumulative risk, defined as better-than-expected depressive symptom severity, was not associated with immediate brain structural differences. However, prospective analyses revealed smaller GMV in the IOFG and temporal pole over time, potentially reflecting greater neural efficiency or delayed biological costs.

Introduction: Different disorder courses contribute to the large heterogeneity within depressive syndromes. Chronic depression (CD) warrants further investigation given its high prevalence and poor treatment outcomes. This study aimed to identify a clinical profile and symptomatic phenotype associated with CD versus non-chronic depression (NCD) to provide a better characterization of CD. Methods: The preregistered analysis used cross-sectional data from a large German cohort (N = 994; 64.9% female) with retrospective information on depression trajectories. We assessed associations of nine psychological and social characteristics with CD in logistic regression models. We further examined symptom networks across disorder courses using Bayesian network analysis of Beck Depression Inventory (BDI-I) item-level data. Results: In our sample 18.5% of patients with depression met criteria for CD (nCD = 184), whereas 81.5% showed a non-chronic course (nNCD = 810). The characteristics childhood maltreatment, age of onset, neuroticism, extraversion, social networks, social support, psychosocial functioning, and psychiatric comorbidities were associated with CD in single regressions. In a combined model (R2 = 0.25), greater exposure to childhood maltreatment, lower extraversion, and lower psychosocial functioning were associated with CD, defining a clinical profile distinguishing CD from NCD. Symptom networks based on BDI-I items were highly similar across trajectories, differing only by the connection between past failure and self-contempt, which was unique to the CD group. Conclusion: Psychosocial factors, but not symptom profiles, distinguished CD from NCD. Thus, characteristics beyond classical depression symptoms seem more informative for differentiating depression trajectories. If confirmed longitudinally, these findings may improve prediction of depression trajectories and inform early intervention.

BACKGROUND:Cognitive deficits in Major Depressive Disorder (MDD) are clinically debilitating, persist after remission, and predict worse long-term outcomes, yet factors determining which patients are most at risk remain poorly understood. Cognitive reserve (CR) - the brain's capacity to buffer negative effects of structural alterations on cognitive functioning - is assessable through brief, clinically applicable proxy measures and may serve as a practical stratification factor in MDD. METHODS:CR of 539 MDD patients (65% female) and 549 healthy controls (65% female) from the Marburg-Münster Affective Disorders Cohort Study was estimated from educational years, occupational attainment, and verbal IQ. We examined the association of CR with cognitive impairment, tested whether CR moderates the relationship between MRI-based connectome alterations and cognitive performance, and predicted two-year disease course characteristics from baseline CR, benchmarked against polygenic risk and childhood maltreatment. RESULTS:Low CR was associated with increased cognitive impairment, especially in MDD patients (OR=6.24, p=0.003). CR moderated the association between cognitive performance and white matter network connectivity (t=2.780, pFWE=0.006). Low baseline CR predicted increased episode duration and symptom severity (psFDR<0.001) over two years, with effect sizes statistically indistinguishable from those of polygenic risk and childhood maltreatment. CONCLUSIONS:CR identifies MDD patients at risk for cognitive impairment and worse disease progression, with prognostic utility comparable to polygenic risk and childhood maltreatment. The moderating effect of CR on the brain network-cognition association provides a neurobiological rationale for this vulnerability. These findings support incorporating CR into routine clinical assessment to guide risk stratification and personalized treatment planning.

Cognitive-behavioral therapy (CBT) is a primary treatment for depression. Although previous research has underscored the significant roles of white matter (WM) alterations and maladaptive parenting in depression risk, their associations with CBT response remain largely unknown. This longitudinal study investigated the interplay of WM integrity changes over time, treatment response, and parenting style in patients with depression. Diffusion-tensor-imaging and clinical data were assessed in n = 65 (55% female) patients with depression before and after 20 CBT sessions and n = 65 (68% female) healthy controls (HC) in a naturalistic design. Linear-mixed-effect models compared changes in fractional anisotropy (FA) between groups and tested associations between FA changes and symptom changes. It was investigated whether parenting style predicts depressive symptoms at follow-up and whether FA changes mediate this association. Patients showed differential FA changes over time in the corpus callosum and corona radiata compared to HC (ptfce-FWE = 0.008). Increases in FA in the corpus callosum, corona radiata and superior longitudinal fasciculus were linked to symptom improvement after CBT in patients (ptfce-FWE = 0.023). High parental care (pFDR = 0.010) and low maternal overprotection (pFDR = 0.001) predicted fewer depressive symptoms at follow-up. The association between maternal overprotection and depressive symptoms at follow-up was mediated by FA changes (pFDR = 0.044). Robustness checks—controlling for outliers, non-linear age effects, clinical characteristics, and patient subgroups—supported these results. Overall, patients with depression show changes in WM integrity following CBT, which are linked to treatment response. The results highlight the significance of early life adversities and related microstructural changes in the effectiveness of CBT for treating depression.

Introduction:Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, brain age research in psychiatry has primarily examined mood and psychotic disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group. Methods:3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1,134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effect models examined group differences in brain-PAD and moderation by age. Results:No significant group difference in brain-PAD manifested (β diagnosis (SE)=0.37 years (0.43), p=0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (β diagnosis-by-age=-0.08 (0.03), pFDR=0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post-hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; β diagnosis=1.20 (0.60), p<0.05, mixed-effects d (95% confidence interval)=0.14 (0.00-0.28)), but not older participants (36-75 years; β diagnosis=0.07 (0.65), p=0.91). Conclusions:Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post-hoc analyses showed a subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

Technological advances in magnetic resonance spectroscopy (MRS), particularly at ultra-high field strengths (e.g., 7T), now allow for the reliable quantification of an increasing number of brain metabolites across multiple brain regions. However, most studies remain constrained to analyses of isolated metabolite levels, overlooking the complex interdependencies that govern neurometabolic regulation. Here, we present a multivariate framework that applies network analysis and systematic evaluation of pairwise metabolite ratios to a reference dataset of 53 individuals, revealing coordinated patterns of metabolite covariation as well as a hierarchical inter-metabolite architecture. We validate the generalizability and robustness of these findings across four independent MRS datasets, encompassing both longitudinal and cross-sectional samples from 78 humans and 7 rats across five brain regions and three field strengths. Our results show that inter-metabolite relationships are broadly conserved across methodological contexts, while selective axes exhibit graded, network-informed modulation in response to physiological challenges such as caloric restriction or transcranial magnetic stimulation. By moving beyond univariate measures, our multivariate framework accommodates the innate complexity in human brain metabolism and carries translational relevance for biomarker discovery and the development of metabolically targeted therapeutic strategies. ### Competing Interest Statement JR received speakers honoraria from Janssen, Hexal, Neuraxpharm and Novartis. MG received remuneration from Janssen for consultancy services. SET received honoraria for scientific advice from Janssen. ### Funding Statement The study was performed in parts by using the research infrastructure of the Werner-Kaiser-Research Center of the Jena University Hospital, Friedrich-Schiller-University Jena. JR was supported by the LOEWE program of the Hessian Ministry of Science and Arts (Grant Number: LOEWE1/16/519/03/09.001(0009)/98). SET was funded by the Leistungszentrum Innovative Therapeutics (TheraNova), funded by the Fraunhofer Society and the Hessian Ministry of Science and Art; the Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR; Federal Ministry of Research, Technology and Space) -- 01EO2102 INITIALISE Advanced Clinician Scientist Program; and the REISS foundation. The financial support by the Austrian Federal Ministry for Digital and Economic Affairs; and the National Foundation for Research, Technology, and Development; and the Christian Doppler Research Association, is gratefully acknowledged. We would like to acknowledge E.J. Auerbach and M. Marjanska (Center for Magnetic Resonance Research and the Department of Radiology, University of Minnesota, USA) for the development of the STEAM and FAST(EST)MAP sequences for the Siemens platform, which was provided by the University of Minnesota under a C2P agreement. ### 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: The ethics committees of the Medical Faculty of Aachen RWTH, Charite - Universitaetsmedizin Berlin, Jena University Hospital and the medical faculty at the University of Muenster in Germany as well as the committee on Animal Experimentation for the Canton de Vaud, Switzerland gave ethical approval for the study data used in this work. 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 All data produced in the present work are contained in the manuscript.

Background Resilience, the ability to adapt positively in the face of adversity, is shaped by the interplay of risk and protective factors. Previous magnetic resonance imaging (MRI) studies on resilience have predominantly focused on single factors, often operationalizing resilience dichotomously as the absence of psychiatric disorders despite adversity. Methods In this prospective MRI study, we defined resilience as better-than-expected depressive symptom severity (Hamilton Depression Rating Scale) relative to cumulative risk. Using ridge-regularized regression in N=1,804 participants (955 healthy, 849 depressed) from the Marburg-Muenster Affective Disorders Cohort Study, we predicted symptom severity and derived residuals as measures of resilience. Residuals were then used to predict gray matter volume (GMV) and cortical thickness at baseline (T1) and two-year follow-up (T2; N=808), using voxel- and surface-based analyses. This approach was complemented by extreme-group comparisons of resilient (better-than-expected outcome) and vulnerable (worse-than-expected outcome) individuals. Results Cumulative risk explained 51.4% of variance in depressive symptoms at T1 and 44.2% at T2. Residual scores showed moderate stability over time (r=0.31, p<0.001). Region-of-interest and whole-brain analyses revealed no morphometric associations with resilience at T1. In contrast, higher resilience at T1 predicted lower GMV in the left inferior orbitofrontal gyrus (IOFG) and temporal pole at T2 (ROI, pFWE(peak)=0.002, partial r=0.18), with no changes in cortical thickness. Conclusion Resilience to cumulative risk, defined as better-than-expected depressive symptom severity, was not associated with immediate brain structural differences. However, prospective analyses revealed smaller GMV in the IOFG and temporal pole over time, potentially reflecting greater neural efficiency or delayed biological costs. ### Competing Interest Statement Tilo Kircher received unrestricted educational grants from Servier, Janssen, Recordati, Aristo, Otsuka, neuraxpharm. This funding is not associated with the current work. The remaining authors have no conflicts of interest. ### Funding Statement This work is part of the German multicenter consortium Neurobiology of Affective Disorders. A translational perspective on brain structure and function, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG; Forschungsgruppe/Research Unit FOR2107). The principal investigators are Tilo Kircher (speaker FOR2107; DFG grant numbers KI 588/14-1, KI 588/14-2, KI 588/15-1, KI 588/17-1), Udo Dannlowski (co-speaker FOR2107; DA 1151/5-1, DA 1151/5-2, DA 1151/6-1, DA1151/9-1, DA1151/10-1, DA1151/11-1), Igor Nenadic (NE 2254/1-2, NE2254/2-1, NE2254/3-1, NE2254/4-1), Markus M. Noethen (NO 246/10-1, NO 246/10-2), Tim Hahn (HA 7070/2-2), Andreas Jansen (JA 1890/7-1, JA 1890/7-2), Benjamin Straube (STR 1146/18-1). This work was further funded in part by the Germanys Excellence Strategy (EXC 3066/1 The Adaptive Mind, Project No. 533717223). Nina Alexander, Tilo Kircher, Benjamin Straube, and Igor Nenadic were further supported by the Hessian Ministry of Higher Education, Science, Research and Art (LOEWE project DYNAMIC Grant Number: LOEWE1/12/519/03/09.001(0009)/98). Udo Dannlowski was additionally funded by the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Muenster (grant Dan3/022/22 to UD). This work was supported in part by the SFB/TRR 393 consortium from the German Research Foundation (DFG, project grant no 521379614). The principal investigators are Tilo Kircher (speaker SFB/TRR 393), Nina Alexander, Udo Dannlowski, Tim Hahn, Hamidreza Jamalabadi, Andreas Jansen, Elisabeth J. Leehr, Igor Nenadic, Susanne Meinert, Frederike Stein, and Benjamin Straube. ### 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: The study involved human participants and was conducted in accordance with the Declaration of Helsinki. Study protocols received ethical approval from the Ethics Committees of the Medical Faculties of the University of Marburg (AZ: 07/14) and the University of Münster (AZ: 2014-422-b-S), Germany. All participants provided written informed consent prior to participation and received financial compensation. Data were collected within the German multicenter research consortium (DFG) FOR2107 ("Neurobiology of Affective Disorders"). 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 All data produced in the present study are available upon reasonable request to the authors / cohort spokepersons

Background:Large-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. Methods:Using an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). Results:Higher brain age gaps were significantly associated with BMI (β=0.01, PFDR=0.02) and smoking (β=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education (β=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap (β=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS (βs=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors-BMI, smoking, and education-remained uniquely associated with brain age gaps. Conclusions:Genetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

Previous studies have suggested that alterations in white matter (WM) microstructure are implicated in suicidal thoughts and behaviours (STBs). However, findings of diffusion tensor imaging (DTI) studies have been inconsistent. In this large-scale mega-analysis conducted by the ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium, we examined WM alterations associated with STBs. Data processing was standardised across sites, and resulting WM microstructure measures (fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity and radial diffusivity) for 24 WM tracts and one global measure were pooled across 40 cohorts. We compared these measures among individuals with a psychiatric diagnosis and lifetime history of suicide attempt (n = 652; mean age = 35.4 ± 14.7; female = 71.8%), individuals with a psychiatric diagnosis but no STB (i.e., clinical controls; n = 1871; mean age = 34 ± 14.8; female = 59.8%), and individuals with no psychiatric diagnosis and no STB (i.e., healthy controls; n = 642; mean age = 29.6 ± 13.1; female = 62.9%). We also compared these measures among individuals with recent suicidal ideation (n = 714; mean age = 36.3 ± 15.3; female = 66.1%), clinical controls (n = 1184; mean age = 36.8 ± 15.6; female = 63.1%), and healthy controls (n = 1240; mean age = 31.6 ± 15.5; female = 61.0%). We found subtle but statistically significant effects, such as lower FA associated with a history of suicide attempt, over and above the effect of psychiatric diagnoses. These effects were strongest in the corona radiata, thalamic radiation, fornix/stria terminalis, corpus callosum and superior longitudinal fasciculus. Recent suicidal ideation was associated with higher AD in the cingulum. Effect sizes were small (partial eta-squared < 0.015). This large-scale coordinated mega-analysis revealed subtle regional and global alterations in WM microstructure in individuals with a history of suicide attempt and recent suicidal ideation. Longitudinal studies are needed to confirm whether these alterations are a risk factor for suicidal behaviour or ideation.

BACKGROUND:Studies investigating social anxiety disorder (SAD) have reported inconsistent alterations in white matter (WM) microstructure. The ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Anxiety Working Group investigated differences in the microstructure of 25 WM tracts between individuals with SAD and healthy control (HC) participants in a mega-analysis. METHODS:We analyzed data from 487 individuals with SAD and 1604 HC participants (ages 8-65 years) from 12 cohorts worldwide. Analyses and quality control were performed using standardized ENIGMA diffusion tensor imaging protocols. We primarily examined fractional anisotropy (FA) as the main parameter of WM microstructure. Linear mixed-effects analyses were conducted to compare individuals with SAD with HC participants in the total sample. Next, adult (age >21) and adolescent (age ≤21) samples were analyzed separately. In sensitivity analyses, additional effects of sex, medication, symptom severity, and comorbid psychiatric disorders were investigated. RESULTS:In the total sample, individuals with SAD showed lower FA in several tracts, including the corpus callosum and fornix, compared with HC participants. Widespread sex × diagnosis interactions were observed, mostly driven by lower FA in females with SAD. Adults with SAD showed lower FA in multiple tracts, while age × diagnosis interactions were observed in adolescents. CONCLUSIONS:Using a mega-analytic approach, several differences in WM microstructure were found between individuals with SAD and HC participants, both in the full sample and in age group-specific sensitivity analyses. Some neurobiological changes in WM tracts in individuals with SAD may vary with age and sex, whereas others may relate to broader transdiagnostic neurobiological features underlying psychopathology. Further research should investigate these issues in more detail.