Nature Mental Health

Conduct disorder (CD) is among the most prevalent and burdensome disorders in early adolescence. Over the past decade, there has been growing interest in identifying reliable and localized neurobiological markers of conduct disorder (CD). However, recent meta-analyses have highlighted the weak reliability of these so-called markers, thereby limiting the ability to draw firm conclusions. Using normative network mapping (598 healthy subjects), we rather sought to investigate whether the heterogeneous findings across studies may map unto a common brain network. A meta-analysis of 38 fMRI studies involving adolescents with a CD (932 cases, 975 controls) was first conducted and showed only a very weak spatial convergence in brain activity alterations in the anterior temporal lobe (5 out of 38 studies). In turn, network mapping revealed that findings across studies show a consistent connectivity pattern across the whole brain, with regional overlap reaching up to 94.7% (36 out of 38 studies). This network was primarily driven by functional connectivity of brainstem nuclei, subcortical structures (i.e., thalamus, ventral striatum), cingulate cortex (i.e., anterior to posterior midcingulate), superior temporal sulcus, and visual cortices. We further describe the neurochemicals and genetic markers of this CD-Network with emphasis on midbrain serotoninergic, dopaminergic and cholinergic projections. Our findings suggest that our understanding of the neurobiological markers of CD could be enhanced by viewing the brain as a complex interconnected system rather than reducing its complexity to a limited number of brain structures. More importantly, this CD-Network may serve as evidence that the various theories of CD can be reconciled rather than seen as conflicting.

World Trade Center (WTC) responders exposed to traumatic and environmental stressors during rescue and recovery efforts have higher prevalence (23%) of persistent, clinically significant WTC-related post-traumatic stress disorder (WTC-PTSD). Here, we applied eigenvector centrality (EC) metrics and data driven methods on resting state functional magnetic resonance (fMRI) outcomes to investigate neural mechanisms underlying WTC-PTSD and to identify how EC shifts in brain areas relate to WTC-exposure and behavioral symptoms. Nine brain areas differed significantly and contributed the most to differentiate functional neuro-profiles between WTC-PTSD and non-PTSD responders. The association between WTC-exposure and EC values differed significantly between WTC-PTSD and non-PTSD in the right anterior parahippocampal gyrus and left amygdala (p= 0.010; p= 0.005, respectively, adjusted for multiple comparisons). Within WTC-PTSD, the index of PTSD symptoms was positively associated with EC values in the right anterior parahippocampal gyrus and brainstem. Our understanding of functional changes in neural mechanisms underlying WTC-related PTSD is key to advance intervention and treatment.

Mood and anxiety disorders emerge predominantly in adolescence, yet they are usually identified only once symptoms have consolidated, when intervention can only be reactive. A marker that registers the loss of healthy brain function before symptoms crystallise would allow earlier and more targeted treatment, much as caged canaries once warned miners of danger before it became apparent. Here we report such a marker using a single baseline resting-state functional MRI scan in 150 adolescents in the Human Connectome Project Boston Adolescent Neuroimaging of Depression and Anxiety (HCP BANDA) cohort, allowing us to prospectively predict depression and anxiety symptoms one year later in held-out participants at r = 0.60, substantially above the effect-size ceiling reported for functional connectivity in the same data. The marker is not computed from raw functional connectivity but read out from a whole-brain generative model fitted to each individual's dynamics, which gives access to interference structure that covariance-based features cannot represent. The regions driving the prediction, including precuneus, ventromedial prefrontal and anterior cingulate cortices, are among those previously implicated in internalising disorders, and the same signature tracks cognitive variation in healthy participants and is mechanistically linked to the efficiency of task-related computation. These findings establish a mechanistically interpretable and prospectively predictive marker of adolescent mental health and define a clear path towards external validation and clinical use.

An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.

Improving diagnostic accuracy of obsessive-compulsive disorder (OCD) using models of brain imaging data is a key goal of the field, but this objective is challenging due to the limited size and phenotypic depth of clinical datasets. Leveraging the phenotypic diversity in large non-clinical datasets such as the UK Biobank (UKBB), offers a potential solution to this problem. Nevertheless, it remains unclear whether classification models trained on non-clinical populations will generalise to individuals with clinical OCD. This question is also relevant for the conceptualisation of OCD; specifically, whether the symptomology of OCD exists on a continuum from normal to pathological. Here, we examined a recently published "meta-matching" model trained on functional connectivity data from five large normative datasets (N=45,507) to predict cognitive, health and demographic variables. Specifically, we tested whether this model could classify OCD status in three independent clinical datasets (N=345). We found that the model could identify out-of-sample OCD individuals. Notably, the most predictive functional connectivity features mapped onto known cortico-striatal abnormalities in OCD and correlated with genetic brain expression maps previously implicated in the disorder. Further, the meta-matching model relied upon estimates of cognitive functions, such as cognitive flexibility and inhibition, to successfully predict OCD. These findings suggest that variability in non-clinical brain and behavioural features can discriminate clinical OCD status. These results support a dimensional and transdiagnostic conceptualisation of the brain and behavioural basis of OCD, with implications for research approaches and treatment targets.

Multivariate machine learning techniques are a promising set of tools for identifying complex brain-behavior associations. However, failure to replicate results from these methods across samples has hampered their clinical relevance. This study aimed to delineate dimensions of brain functional connectivity that are associated with child psychiatric symptoms in two large and independent cohorts: the Adolescent Brain Cognitive Development (ABCD) Study and the Generation R Study (total n=8,605). Using sparse canonical correlations analysis, we identified three brain-behavior dimensions in ABCD: attention problems, aggression and rule-breaking behaviors, and withdrawn behaviors. Importantly, out-of-sample generalizability of these dimensions was consistently observed in ABCD, suggesting robust multivariate brain-behavior associations. Despite this, out-of-study generalizability in Generation R was limited. These results highlight that the degree of generalizability can vary depending on the external validation methods employed as well as the datasets used, emphasizing that biomarkers will remain elusive until models generalize better in true external settings.

Task-based functional magnetic resonance imaging (tb-fMRI) has advanced our understanding of brain-behavior relationships. Standard tb-fMRI analyses suffer from limited reliability and low effect sizes, and machine learning (ML) approaches often require thousands of subjects, restricting their ability to inform how brain function may arise from and contribute to individual differences. Using data from 9,024 early adolescents, we derived a classifier ( neural signature) distinguishing between high and low working memory loads in an emotional n-back fMRI task, which captures individual differences in the separability of activation to the two task conditions. Signature predictions were more reliable and had stronger associations with task performance, cognition, and psychopathology than standard estimates of regional brain activation. Further, the signature was more sensitive to psychopathology associations and required a smaller training sample (N=320) than standard ML approaches. Neural signatures hold tremendous promise for enhancing the informativeness of tb-fMRI individual differences research and revitalizing its use.

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2. Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Major depressive disorder has a point prevalence of 5% of the world population and is the main cause of disability, with up to a third of patients not responding to first-line treatments. Surgical neuromodulation is offered to only an anecdotal proportion of these patients, because while these methods are curative in some individuals, the proportion of responders rarely exceeds 50%. Recent efforts to establish reliable brain circuit-symptom relationships and thus predict response have involved mapping with multiple intracranial electrodes, but the impracticality of this approach currently prevents its application at scale. In the present study (ClinicalTrials.gov identifier NCT05697172; FDA Q220192) we begin to address this gap by leveraging low-intensity focused ultrasound (LIFU), a novel noninvasive technique, to modulate the anterior limb of the internal capsule, which is an established surgical deep white matter target for depression. We based our study on burgeoning in vitro evidence that LIFU attenuates axonal conduction by operating mechanosensitive channels in nodes of Ranvier. Compared with sham stimulus, active LIFU produced a functional disconnection of gray matter hubs reached by the sonicated axonal tracts, an increase in positive emotion, and top-down effects on the cardiovascular autonomic balance. Our results using LIFU of deep-brain white matter tracts in humans open three potential avenues to understand the mechanisms and improve the outcome of depression, namely attaining a personalized definition of brain circuit-symptom relationships, serving as a noninvasive probe for neuromodulation before irreversible procedures in a "try before you buy" approach, and ultimately emerging as a therapeutic intervention itself.

Substance use disorder (SUD) stands as a critical public health concern, contributing to substantial morbidity, mortality and societal costs. The effects of SUD on structural brain changes have been well documented. However, the neural mechanisms underlying SUD and the spatial-temporal volumetric changes associated with SUD remained underexplored. In this investigation, neuroimaging, behavioral and genomic data across four large population cohorts jointly covering the full lifespan were harmonized, and whole-brain volumetric trajectories between substance use disorders (SUDs) and healthy controls (HCs) were compared, revealing the potential neurobiological mechanisms and the genomic basis underlying SUD. Results highlighted three distinct life stages critical for the development of SUD: 1) adolescence to early adulthood (before 25y), where SUD is suspected to be the consequence of prefrontal-subcortical imbalance during neurodevelopment; 2) early-to-mid adulthood (25y - 45y), where SUD was strongly associated with compulsivity-related brain volumetric changes; 3) mid-to-late adulthood (after 45y), where SUD-related brain structural changes could be explained by neurotoxicity. Results were externally validated both via longitudinal analysis of these population cohorts and in independent cross-sectional samples. In summary, our study demonstrated the lifespan whole-brain volumetric changes associated with SUD, revealed potential neurobehavioral mechanisms for the development of SUD, and suggested critical time window for effective prevention and treatment of SUD.

Resilience to emotional disorders is critical for adolescent mental health, especially following childhood abuse. Yet, brain signatures of resilience remain undetermined due to the differential susceptibility of the brains emotion processing system to environmental stresses. Analyzing brains responses to angry faces in a longitudinally large-scale adolescent cohort (IMAGEN), we identified two functional networks related to the orbitofrontal and occipital regions as candidate brain signatures of resilience. In girls, but not boys, higher activation in the orbitofrontal-related network was associated with fewer emotional symptoms following childhood abuse, but only when the polygenic burden for depression was high. This finding defined a genetic-dependent brain (GDB) signature of resilience. Notably, this GDB signature predicted subsequent emotional disorders in late adolescence, extending into early adulthood and generalizable to another independent prospective cohort (ABCD). Our findings underscore the genetic modulation of resilience-brain connections, laying the foundation for enhancing adolescent mental health through resilience promotion.

Harmonized phenotyping and diverse population-specific studies are crucial for advancing gene discovery in psychiatric genetics. We conducted a genome-wide association (GWAS) mega-analysis of DSM-defined lifetime major depressive disorder (MDD) in 64 941 participants (25.7% cases) from the Dutch BIObanks Netherlands Internet Collaboration (BIONIC) consortium. SNP-based heritability was estimated at 13.4%, exceeding recent global meta-analyses, with a high genetic correlation (rG = 0.89) to the latest major depression GWAS by the Psychiatric Genetics Consortium (PGC-MD). We identified a novel genome-wide significant locus in PALMD (p = 3.26 x 10-), that was confirmed by GWAS-by-subtraction. Polygenic scores (PGSs) based on BIONIC predicted MDD in UK Biobank, and PGSs from PGC-MD predicted into BIONIC, with within-family analyses indicating minimal confounding. Genetic causal inference revealed associations with over 30 phenotypes. Twin concordance for MDD increased with polygenic burden, reinforcing its genetic architecture. This study emphasizes the power of harmonized phenotyping and regional biobanks in uncovering the genetic architecture of MDD, highlighting the value of population-specific studies for improving risk prediction and advancing psychiatric genetics.

Puberty demarks a period of profound brain dynamics that orchestrates changes to a multitude of neuroimaging-derived phenotypes. This poses a dimensionality problem when attempting to chart an individuals brain development on a single scale. Here, we illustrate shifts in subject similarity of imaging data that relate to pubertal maturation and altered mental health, suggesting that dimensional reference spaces of subject similarity render useful to chart brain dynamics in youths.

Rumination, or perseverative negative self-referential thinking, is a hallmark of depression. In adults, a dynamic resting-state fMRI model of trait rumination was recently identified through predictive modelling. In adolescents, a development period during which rumination and depression increase, the neurobiological correlates of ruminative thinking are less clear. In the current preregistered study, we examine dynamic connectivity correlates of self-reported rumination in the largest sample of adolescents to date (n = 443, containing clinical and non-clinical individuals). Notably, the adult model failed to generalize to our sample. In addition, linear models trained on default-mode network (DMN) connectivity, as well as whole-brain connectome models, failed to generalize to held-out data. In an exploratory random forest analysis, we found significant prediction performance of a model where increased variability between DMN-cerebellum, DMN-dorsal attention network, and DMN-DMN connections was nominally associated with higher rumination. However, the model did not generalize to an external sample with lower rumination scores and a distinct scanner protocol. Our findings illustrate the difficulty of characterizing the neurodevelopment of risk factors for depression.

ObjectiveAssociations between psychiatric problems and white matter (WM) microstructure have been reported in childhood and adolescence. Yet, a deeper understanding of this relation has been hampered by a dearth of well-powered longitudinal studies and a lack of explicit examination of the bidirectional associations between brain and behavior. We investigated the temporal directionality of WM microstructure and psychiatric symptom associations from late childhood to early adolescence. MethodsIn this observational study, we leveraged the worlds largest single- and multi-site cohorts of neurodevelopment: the Generation R and Adolescent Brain Cognitive Development Studies (total n scans = 11,400). We assessed psychiatric symptoms with the Child Behavioral Checklist as broad-band Internalizing and Externalizing scales, and as syndrome scales (e.g., Anxious/Depressed). We quantified WM with Diffusion Tensor Imaging, globally and at a tract level. We used cross-lagged panel models to test bidirectional associations of global and specific measures of psychopathology and WM microstructure, meta-analyzed results across cohorts, and used linear mixed-effects models for validation. ResultsWe did not identify any robust longitudinal associations of global WM microstructure with internalizing or externalizing problems across cohorts (confirmatory analyses). We observed similar findings for longitudinal associations between tract-based microstructure with internalizing and externalizing symptoms, and for global WM microstructure with specific syndromes (exploratory analyses). ConclusionUni- or bi-directionality of longitudinal associations between WM and psychiatric symptoms was not robustly identified. We propose several explanations for these findings, including interindividual differences, the use of longitudinal approaches, and smaller effects than expected.

Reproducible patterns of atypical functional connectivity of sensorimotor and higher-order networks have been previously identified in the autistic brain. However, the neurosignalling pathways underpinning these differences remain unclear. The {micro}-opioid system is involved in sensory processing as well as social and reward behaviours and has been implicated in autism, suggesting a potential role in shaping the autistic brain. Hence, we tested the hypothesis that there is atypical involvement of the {micro}-opioid system in these networks in autism. We used a placebo-controlled, double-blind, randomised, crossover study design to compare the effects of an acute dose of the {micro}-opioid receptor agonist tianeptine in autistic and non-autistic participants on functional connectivity (FC) of sensorimotor and frontoparietal networks. We found that tianeptine increased FC of a sensorimotor network previously characterised by atypically low FC in autism. The connectivity of the frontoparietal network was not significantly shifted. Our findings suggest that {micro}-opioid neurosignalling might contribute to functional brain differences in the sensorimotor network in autism. Given that sensorimotor system alterations are thought to be core to autism and contribute to other core autistic features, as well as adaptability and mental health, further research is warranted to explore the translational potential of {micro}-opioid modulation in autism.

Brain imaging-derived phenotypes (IDPs) serve as underlying functional intermediates in psychiatric disorders, both of which are highly polygenic and heritable. Observational studies have elucidated the correlation between IDPs and psychiatric disorders, yet no systematic screening of IDPs to confer the causal liability in such disorders. We conducted a bidirectional two-sample Mendelian randomization framework to explore the causality of 1,901 IDPs on 10 psychiatric disorders and established the BrainMR database (http://brainmr.online/idp2psy/Index.php). We identified 17 causalities in forward MR analyses and 14 causalities in reverse MR analyses, which all rigorously examined them through various sensitivity analyses. The top significant relationship in forward was a unit lower volume of right central lateral of the thalamic nuclei, which was causally associated with an increased anorexia nervosa risk with an estimated OR of 0.52 (95% CI 0.41-0.64, P = 3.32 x 10-9). In reverse, the top significant IDP to be affected was the area of superior segment of the circular sulcus of the insula in the right hemisphere, which was increased by the reason of SCZ risk with an estimated association effect size of 0.068 (95% CI 0.046-0.090, P = 1.12 x 10-6). Overall, our study provides unique insights into causal pathways of psychiatric disorders at the imaging levels.

IntroductionThe NIMH Research Domain Criteria (RDoC) posits similar cellular pathologies for particular symptom domains across diagnostic categories. Conversely, knowledge that these differ could advance treatment discovery, especially for affective and non-affective psychoses, as studies usually intermix them. MethodsWe tested this by comparing metabolite biomarker concentrations for cellular pathologies from whole hippocampal proton magnetic spectroscopic imaging ( 1H MRSI) with symptoms from the original and five factor PANSS, and the Hamilton Depression and Young Mania Scales. Participants were 26 healthy controls; 22 non-psychotic affective cases (NP-aff); and 33 with psychosis (including 20 schizophrenia (Scz) and 13 affective psychosis (aff-P) cases). ResultsPANSS activation factor was related to reductions in all cellular component biomarkers in Scz, including glia, membrane turnover, neural integrity, glutaminergic neurotransmission, and energy metabolism (ps<.05), but only to energy metabolism in NP-aff (p=.03). Biomarkers for mood symptoms also varied across categories, suggesting gliosis for mania and depression in HC (ps[&le;].025), but increased membrane turnover for mania in aff-P (p=.015), and decreased neural integrity and energy metabolism for depression in Scz (ps<.05). In contrast, negative symptoms and autistic preoccupation were related to reduced glia in both NP-aff and aff-P (ps<.05). Autistic preoccupation in Scz was related to both reduced glia and membrane turnover (ps<.05). Only Scz showed a significant finding for positive symptoms, specifically reduced membrane turnover (p=.018). DiscussionThese results suggest both distinct and similar cellular pathologies for symptoms across diagnoses, including affective and non-affective psychoses. The differences support categorizing disorders and stratifying different psychoses in research rather than transdiagnostic approaches.

Cognitive function, psychological processes, mental states, and behaviors are key dimensions of human subjective experience that separately relate to mental disorders across diagnostic categories. However, whether these dimensions are linked to common or distinct brain morphological patterns that convey risk or resilience for psychiatric disorders remains unclear. The current study is a longitudinal investigation on 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study aged 9-10 years at baseline. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of cortical morphology (4 metrics: surface area, cortical and subcortical volume, cortical thickness, and sulcal/gyral depth) with multidomain behavioral assessments including cognitive scores and psychological measures indexing motivation, impulse control, mental states, and behaviors across a normative continuum from healthy to pathological. Across morphological measures, we identified a robust latent brain structural variate that correlated positively with cognitive performance and negatively with psychological measures indexing greater psychology. Notably, higher scores on this brain variate reflected larger cortical surface area and cortical volume--especially in the temporal gyri--together with a posterior-anterior gradient in cortical thickness, showing relatively greater thickness in occipital, parietal, and temporal cortices and lower thickness in cingulate and frontal regions. This brain variate and the related cognitive-psychological-behavioral variate remained stable at the 2-year follow-up, demonstrating temporal consistency. Importantly, the brain variate showed a dose-dependent relationship with the cumulative number of psychiatric diagnoses assessed concurrently and at 2-year follow-up, with lower brain variate scores being associated with higher numbers of comorbid diagnoses. In addition, the brain scores were associated with longitudinal transitions between healthy and diagnosed states over the 2-year study period, in which lower scores at baseline were associated with persistent psychiatric diagnoses whereas higher scores at baseline were associated with persistent healthy states, suggesting that the brain scores capture a vulnerability- resilience continuum for psychopathology. By revealing shared brain structural substrates across conventional diagnostic boundaries, these findings advance the neurodevelopmental understanding of psychiatric disorders and highlight the potential utility of morphology-informed approaches for early screening and intervention in youth.

ObjectiveStudies reporting that highly intelligent individuals have more mental health disorders often have sampling bias, no or inadequate control group, or insufficient sample size. We addressed these caveats by examining the difference in the prevalence of mental health disorders between individuals with high and average general intelligence (g-factor) in the UK Biobank. MethodsParticipants with general intelligence (g-factor) scores standardized relative to the same-age UK population, were divided into 2 groups: a high g-factor group (g-factor 2 SD above the UK mean; N=16,137) and an average g-factor group (g-factor within 2 SD of the UK mean; N=236,273). Using self-report questionnaires and medical diagnoses, we examined group differences in prevalence across 32 phenotypes, including mental health disorders, trauma, allergies, and other traits. ResultsHigh and average g-factor groups differed across 15/32 phenotypes and did not depend on sex and/or age. Individuals with high g-factors had less general anxiety (OR=0.69) and PTSD (OR=0.67), were less neurotic ({beta}=-0.12), less socially isolated (OR=0.85), and were less likely to have experienced childhood stressors and abuse, adulthood stressors, or catastrophic trauma (OR=0.69-0.90). They did not differ in any other mental health disorder or trait. However, they generally had more allergies (e.g., eczema; OR=1.13-1.33). ConclusionsThe present study provides robust evidence that highly intelligent individuals have no more mental health disorders than the average population. High intelligence even appears as a protective factor for general anxiety and PTSD. Key PointsO_ST_ABSQuestionC_ST_ABSAre high IQ individuals at increased risk of mental health disorders? FindingsIn the UK Biobank (N [~=] 7,266 - 252,249), highly intelligent individuals (2SD above the population mean) were less likely to suffer from general anxiety and PTSD, and no more likely to have depression, social anxiety, a drug use disorder, eating disorders, obsessive-compulsive disorder, bipolar disorder, and schizophrenia. MeaningContrary to popular belief, high intelligence is not a risk factor for psychiatric disorders and even serves as a protective factor for general anxiety and PTSD.