Nature Mental Health

General distress cuts across psychiatric symptom domains, yet its computational correlates remain poorly defined. We examined whether drift rate--a core parameter indexing the efficiency of evidence accumulation--is more strongly associated with general distress than with domain-specific symptoms. In a cross-sectional online sample of 441 adults from the general population, participants completed a perceptual and value-based decision-making task, symptom assessments, and cognitive testing. Drift rates were estimated using hierarchical drift-diffusion modeling. Individuals with severe symptom elevations showed robust reductions in drift rate, particularly for value-based decisions. Mixed-effects models demonstrated that general distress, indexed by the Positive Symptom Distress Index, was more strongly associated with value-based than perceptual drift rate, even after accounting for all symptom domains. Value-based drift rate also explained variance in general distress beyond that accounted for by elevated symptoms across domains and selectively attenuated associations with somatization and paranoid symptoms. These findings suggest that value-based evidence accumulation captures a transdiagnostic component of distress-related impairment that is not reducible to symptom burden alone.

Substance use disorders (SUD) are chronic conditions with devastating effects on brain health, functioning, and survival. In this study, we compared brain morphometry of 2,782 individuals with SUD to 1,951 controls and assessed the topographic overlap of these differences with brain connectivity and receptor architecture. Across SUD, we identified a morphometric signature involving frontal, parietal, temporal and limbic systems that overlapped with cortical hub regions and harbored cortical and subcortical disease epicenters. Findings were highly consistent across six substances and numerous robustness and generalizability analyses. Transdiagnostic comparisons showed high spatial overlap of SUD epicenters with those of schizophrenia and bipolar disorder, suggesting shared network-constrained cortical differences. Finally, multivariate mapping revealed that SUD brain differences aligned with two neurotransmitter axes contrasting cannabinoid-opioid and dopaminergic systems. These findings indicate that addiction-related brain differences are shaped by connectome and neurotransmitter architecture, positioning brain network and neurochemical organization as key principles of SUD-related brain alterations.

Childhood and adolescence represent a critical period in which neurodevelopmental psychiatric conditions emerge; traditional case-control approaches often underestimate the complexity and co-occurrence of psychiatric conditions, calling for a transdiagnostic approach as a complementary measure. Open-access data sharing initiatives provide an opportunity to decipher structural- and functional-based organisational constraints on the relationship between brain connectivity and psychopathology. Using a highly heterogenous and comorbid neurodevelopmental sample of children and adolescents aged between 6 and 17 years old at-risk of neurodevelopmental conditions (N = 174, 114 males, mean age 10.72 {+/-} 2.21 years), and age-matched neurotypical controls (N = 27, 12 males, mean age 10.65 {+/-} 2.07 years), we identified a multivariate association, or latent variable, between resting-state functional connectivity and psychopathology. We extensively benchmarked this latent construct to develop a more parsimonious account of childhood psychopathology though an analytical framework spanning biological maps, brain connectivity, and behaviour. Participant-level expression of this latent brain-behaviour association differed by diagnostic burden and symptomatology, and pre-empted longitudinal psychopathology. Whilst diagnostic status was useful for interpretation, the latent construct transcended traditional diagnostic borders, revealing a neurotypical-neurodivergent continuum. The relationship between functional connectivity and neurodevelopmental psychopathology was circumscribed by functional connectivity networks (visual, fronto-parietal, and dorsal attention) and cytoarchitectonic class (primary/secondary sensory and primary sensory cortices). The latent variable aligned with magnetoencephalography-defined electrophysiological alpha (), high-gamma ({gamma}), and theta ({theta}) frequency bands, and was enriched across cortical distributions of astrocytes and excitatory neurones. The connectivity signature was significantly aligned with the archetypal sensorimotor-to-association axis and validated in an independent sample of pre-adolescents (N = 3504), with the strongest alignment with the principal component of gene expression and myelination and were relatively less enriched in cortical regions related to language, indicating a protective effect, and more positively enriched in regions related to executive functioning, conferring greater risk of psychopathology. Together, our findings suggest that the predictive link between functional connectivity and common symptoms of neurodevelopmental psychopathology are circumscribed by underlying macroscale anatomical, functional, and cognitive-related hierarchies.

ObjectiveTo delineate the phenotype of juvenile myoclonic epilepsy (JME) with a focus on obsessive-compulsive personality disorder (OCPD) using multimodal psychiatric, neuropsychological, quantitative EEG (qEEG), and structural MRI markers within a predictive-processing/free-energy framework. MethodsWe prospectively studied 65 patients with JME and 68 matched healthy controls (HC). Participants completed DSM-IV SCID I/II interviews and a neuropsychological battery assessing working memory, psychomotor speed, mental flexibility, divided attention, inhibition, and phasic/tonic alertness; standard EEG and high-resolution structural MRI were acquired. Groups comprised HC and JME subgroups without psychiatric comorbidity, with non-OCPD Axis I/II diagnoses, and with OCPD. Welchs t-tests (FDR-corrected) and Hedges g quantified neuropsychological and alpha-band coherence differences. Surface-based analyses assessed cortical thickness/surface area. Exploratory regressions tested associations of OCPD, seizure freedom, and antiseizure medication (ASM) load with cognition; Kendalls tau tested coherence-cognition associations. ResultsCompared with HC, JME showed broad executive-attentional impairment, most pronounced in patients with psychiatric comorbidity. The OCPD subgroup exhibited particularly large slowing in psychomotor speed, inhibition (reaction time), and tonic alertness versus HC, while OCPD versus non-OCPD JME differences did not survive multiple-comparison correction. qEEG showed increased interhemispheric frontal and decreased temporal alpha coherence in JME, with temporal hypo-coherence strongest in those with psychiatric comorbidity; within JME, OCPD was linked to increased left fronto-temporal alpha coherence. In the MRI subsample, JME-OCPD demonstrated increased cortical thickness in left medial orbitofrontal and anterior cingulate regions (vs HC and vs JME without OCPD) and additional posterior occipito-temporal clusters versus HC. Regression and coherence-cognition associations were weak and non-significant after FDR correction. SignificanceJME features syndrome-level executive-attentional dysfunction and altered fronto-temporal network organization. Comorbid OCPD marks a subgroup with accentuated cognitive slowing and distinct medial prefrontal/cingulate structural and left fronto-temporal connectivity signatures, aligning with predictive-processing accounts of rigid, over-precise high-level priors. Key pointsJME is linked to broad executive-attentional impairment versus healthy controls. Psychiatric comorbidity amplifies cognitive deficits in JME. JME with OCPD shows particularly large slowing/inhibitory-control deficits versus controls, while OCPD vs non-OCPD differences within JME are modest. Alpha-band EEG coherence indicates altered network organization in JME and an OCPD-related increase in left fronto-temporal coherence within JME Surface-based MRI suggests an OCPD-related structural phenotype in JME, involving medial orbitofrontal/anterior cingulate cortical thickening

Understanding the neurobiological basis of mental health disorders and their symptoms is a central goal of research in psychiatry. Yet, identifying robust brain-psychopathology associations with neuroimaging remains difficult, in part due to substantial heterogeneity within and comorbidity between diagnostic categories. Transdiagnostic latent factor models aim to address this structure by separating shared and unique symptom variance. This can potentially yield more reliable and neurobiologically-relevant dimensions of psychopathology. However, the extent to which latent factor models improve brain-psychopathology associations remains largely unclear. Using two large developmental cohorts, we compared transdiagnostic bifactor models, correlated factor models, and typical summary scores derived from the Child Behaviour Checklist (CBCL) in their reliability and multivariate associations with whole-brain structure (MRI) and function (resting-state fMRI). We found no consistent evidence that latent factors (bifactor or correlated factor models) strengthened reliability or brain-psychopathology associations, relative to summary scores. Whole-brain predictive models revealed broadly distributed neural signatures that were highly similar between corresponding factor and summary score constructs, with general psychopathology factors and total problem summary scores approaching numerical equivalence. Bifactor scores did, however, display more distinct neural signatures between general, internalising, and externalising dimensions than did summary or correlated factor scores. These results suggest that phenotypic modelling of psychopathology alone does not systematically strengthen the predictive utility of psychiatric neuroimaging, possibly reflecting fundamental limits on the amount of explainable symptom variance by brain features. While latent factor models may aid in distinguishing neural correlates between constructs, improving phenotypic assessment may be necessary for improvements to brain-psychopathology association strength.

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.

Roughly one-third of patients with major depressive disorder (MDD) fail to respond to standard treatments and develop treatment-resistant MDD. For these patients, alternative therapies ofer additional options but yield inconsistent outcomes. Progress has been limited by the absence of objective, brain-based biomarkers to guide target selection or track therapeutic response in real time. Instead, clinicians rely on behavioral assessments that evolve slowly over weeks to months, obscuring the underlying neural dynamics of symptom changes. Here, we test whether the aperiodic exponent of intracranial EEG (iEEG) local field potentials can serve as a neurophysiological marker of depression symptom severity. We leveraged a large iEEG cohort (N = 20) undergoing invasive monitoring for refractory epilepsy, yielding over 1,800 contacts spanning cortical and subcortical zones. For each contact, we estimated the aperiodic exponent (thought to reflect aspects of cortical excitability) of the power spectrum across 10-100 Hz within local brain regions and across distributed cortical association networks. Depressive symptoms were assessed with the Beck Depression Inventory-II (BDI-II) immediately before intracranial resting state recordings. With respect to the BDI-II scale, participants were identified as experiencing minimal (BDI-II [≤] 13) or elevated depression symptoms (BDI-II [≥] 14). Associations between symptom severity (BDI-II total score and Somatic-Afective, Cognitive, and Anhedonia subscales) and region- or network-level exponents were modeled with ordinary least squares (OLS) regression. The whole-brain, mean aperiodic exponent for each participant discriminated symptom status (AUC = 0.82). At the regional level, the orbitofrontal cortex, anterior cingulate cortex, insula, and amygdala showed higher exponents in the elevated depression symptom group (d = 1.18-1.71; p = 0.032-0.004). A post-hoc classification analysis across these four regions misclassified one participant per group (AUC = 0.86; 95% CI 0.64-1.00). In continuous analyses, BDI-II scores correlated positively with exponents in these same four regions (pFDR = 0.019-0.027; partial r=0.61-0.70) and at the network level in the Salience network (pFDR = 0.024; partial r = 0.63) and Default (pFDR = 0.046; partial r = 0.55) network. The Salience network significantly tracked Anhedonia symptoms (p = 0.004; partial r = 0.62). Here we report that intracranial aperiodic exponents within fronto-limbic and insular circuits, overlapping with networks implicated in contemporary accounts of depression pathophysiology, diferentiate depressive symptom status and scale with severity. These findings support the aperiodic exponent as a candidate neurophysiological marker of current depression symptom burden, with potential relevance for individualized neuromodulation in MDD.

Mechanistic understanding and biomarkers of gonadotropin-releasing hormone antagonist treatment effect in paedophilic disorder are absent but may enhance outcomes and reduce sexual-offending risk. 52 help-seeking self-referred Swedish men with paedophilic disorder enrolled in a double-blinded, placebo-controlled, randomized clinical trial. Participants underwent task-based fMRI before, and two weeks after, subcutaneous injection of 120mg of degarelix or equal volume of placebo. fMRI blood-oxygen-level-dependent activation was compared between child and adult (child>adult) stimuli in task-derived regions of interest. Primary outcome was within region-of-interest child>adult activation change, whereas secondary outcomes correlated region-of-interest child>adult activation change to change in clinical measurements of risk, paedophilic interest, sexual preoccupation, hyper- and hyposexuality. 19 degarelix and 22 placebo participants had sufficient fMRI data quality. Reductions in paedophilic interest were strongly correlated with increased child>adult cerebellar (vermis) region-of-interest activation following degarelix (r=-0.740, p<0.001) but not placebo (r=0.183, p=0.41; between-group correlation coefficient z=3.347, p<0.001). Treatment did not significantly change child>adult region-of-interest activity. Post hoc analysis indicated that baseline autism symptoms correlated with degarelix-induced changes in paedophilic interest (r=0.717, p<0.001; between-group correlation coefficient z=2.958, p=0.003) and cerebellar activation (r=-0.581, p=0.01; between-group correlation coefficient z=-1.930, p=0.05). Increased child>adult cerebellar activation was associated with degarelix-induced reductions of paedophilic interest, suggesting cerebellar activity as mechanistically important to, and a prospective biomarker of, degarelix treatment effect. Additionally, autism symptoms may inform treatment prediction. Together, these findings have mechanistic and clinical implications for degarelix treatment of paedophilic disorder. EU clinical trials register identifier: 2014-000647-32 https://www.clinicaltrialsregister.eu/ctr-search/trial/2014-000647-32/SE, registered on 05/06/2014.

Identifying robust biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) remains challenging. The brain-age gap (BAG), which is the difference between predicted biological brain age and chronological age, has been proposed as a marker of atypical neurodevelopment, yet findings have been inconsistent across studies, limiting its clinical translation. We hypothesized that two major sources of heterogeneity, medication status and developmental stage, contribute to these discrepancies. To test this, we analyzed structural MRI data from a large multi-site cohort (N = 947; ages 7-26 years). Individuals with ADHD were stratified by medication history (medicated vs. unmedicated) and developmental stage (childhood vs. adolescence) and compared with matched typically developing controls. We estimated global- and network-level BAG using publicly available pretrained models. Our results revealed that BAG differences were contingent on medication status, with abnormalities predominantly observed in unmedicated individuals. Developmental stage further moderated the presence of this effect: BAG abnormalities were present significantly in children with ADHD but not in adolescents. Network-level analyses demonstrated spatial specificity, with significant effects in visual, ventral attention, and dorsal attention networks. Together, these findings provide compelling evidence that BAG in ADHD is both medication- and development-dependent, highlighting the need to account for treatment exposure and developmental stage when evaluating BAG as a biomarker and further underscoring the importance of early intervention during childhood.

As the global burden of addiction intensifies, the neurobiological commonalities and distinctions between substance use disorders (SUDs) and behavioral addictions (BAs) remain poorly characterized. This coordinate-based meta-analysis of 59 fMRI articles (n = 2,951) mapped the neural signatures of visual cue-reactivity across the addictive disorders. Our results revealed a universal core network shared by SUDs and BAs centered in the bilateral opercular inferior frontal gyrus, suggesting a shared disruption in inhibitory control. Distinctively, SUDs exhibited a stronger recruitment of a subcortical salience pathway, with greater involvement of the left thalamus ventral anterior nucleus than BAs, potentially reflecting pharmacologically amplified bottom-up salience attribution. Notably, recovery-related patterns diverged in the left medial superior frontal gyrus. Alcohol use disorder was associated with neural restoration, whereas heroin use disorder showed neural decompensation. These neural signatures establish a rigorous neurobiological basis for differentiating substance and behavioral phenotypes, supporting tailored circuit-based precision treatments.

Bullying is an adverse childhood experience affecting up to one-third of the global population and linked to psychosis-like experiences (PLEs), which increase the risk of psychotic disorders. This study aimed to investigate the association between the severity and persistence of bullying and PLEs and the neurobiological pathways from bullying to psychosis-like experiences by assessing multiscale brain functional network connectivity (msFNC). We used data from the ABCD Study, a large, ongoing, multisite, population-based prospective cohort study following U.S. adolescents. We included adolescents with complete bullying and PLEs assessments at the 2- and 3-year follow-ups (T1: n=10,939; T2: n=10,102). We examined the associations between bullying severity and temporal exposure and PLEs using linear mixed-effects models. In a 2-year rsfMRI subsample (n=5,280), we used a Neuromark framework to analyze whether msFNC mediated the pathway from bullying to PLEs. Higher PLEs were associated with the presence and severity of bullying (non-bullied vs. mild bullying: d=-0.19, CI: -0.39 to -0.19, p<0.0001; moderate vs. severe bullying: d=-0.49, CI: -0.69 to -0.56, p<0.0001). When bullying ceased, PLEs returned to non-bullied levels (d=-0.13, CI:-0.20 to -0.05, p=0.16), whereas persistence over two years led to greater elevations (d=-0.36, CI:-0.43 to -0.29, p<0.0001). We observed similar patterns for non-paranoid and hallucination-like experiences and their distress. msFNC in paralimbic, default mode, central executive, somatomotor, temporoparietal, insulotemporal, and frontal networks mediated the association. Bullying is time- and dose-dependently associated with psychosis-like outcomes. msFNC between functional brain networks is a novel neurobiological pathway that mediates the link from bullying to PLEs.

Psychotic disorders lack treatment-informative biomarkers, especially during the earliest illness stages when interventions are most effective. Integrating etiological theories on hippocampal pathology and whole-brain neural dysconnectivity, we studied connectivity changes within the hippocampus as a neuroimaging marker of emerging symptomatic and functional trajectories in the psychosis risk state. We analyzed multicenter longitudinal clinical and functional neuroimaging data across an eight-month period from 434 participants (356 individuals at clinical high risk for psychosis and 78 healthy controls) using latent variable regressions. Decreases of intra-hippocampal connectivity over time tracked worsening negative symptoms, depressive symptoms, and psychosocial functioning in at-risk subjects. This finding was not observed for attenuated positive symptoms and cognition, was specific to high-risk individuals relative to healthy controls, and was not obtained for connectivity within other brain areas. Unveiling the temporal sequence of these associations, we found that an early decrease in connectivity within the hippocampus preceded a subsequent worsening of negative symptoms, but not vice versa. These findings position intra-hippocampal connectivity changes as a neuroimaging marker of early affective-motivational and functional trajectories in the psychosis risk state. They further indicate that changes of connectivity within the hippocampus hold prognostic value specifically for emerging negative symptoms. This informs future risk stratification approaches and neurostimulation therapies in the psychosis risk state: Intra-hippocampal connectivity decline could be a valuable predictive marker to improve risk stratification. Ameliorating connectivity reduction within the hippocampus may represent a promising neurostimulation target to prevent unfavorable clinical trajectories. One Sentence SummaryDecreasing connectivity within the hippocampus is a neural prognostic marker of worsening negative symptoms in the psychosis risk state

Despite decades of clinical implementation of medications for opioid use disorder (OUD), overdose mortality rates remain high, underscoring a critical gap in treatments that target brain mechanisms driving addiction. Mindfulness-Oriented Recovery Enhancement (MORE) has demonstrated efficacy in reducing opioid use and craving, hypothetically by restructuring the salience of drug and natural rewards. Yet, to date, MOREs neurobiological mechanisms remain unclear. In this first functional magnetic resonance imaging (fMRI) randomized controlled trial (RCT) of MORE for OUD (NCT04112186), we tested whether compared with an active psychoeducational supportive therapy (PST) control group, MORE rebalanced neural responses to drug and natural reward cues in inpatients with OUD receiving standard of care including medications. Compared with PST, eight weeks of MORE significantly reduced drug-biased activity in the dorsolateral prefrontal cortex (dlPFC) and posterior regions of the default mode network including the precuneus during downregulation of responses to drug cues relative to upregulation of responses to natural reward cues (even when controlling for passive cue viewing). The shift from drug to natural reward responses in the lateral and ventromedial PFC was associated with lower cue-induced craving exclusively in the MORE group. MORE also reduced medial PFC synchronization to naturalistic drug-related movie scenes and significantly extended abstinence duration at follow-up ([~]4 months post-treatment) relative to PST. Together, this neuroimaging RCT demonstrates that MORE normalizes function in PFC nodes of the reward, salience, and control systems, positioning MORE as a biologically-grounded adjunct to pharmacotherapy for OUD.

Arousal and valence are fundamental dimensions of affective experience signifying levels of activation and pleasantness, respectively. These dimensions play a crucial role in shaping emotional responses and behaviors, with significant implications for psychopathology. Previous machine learning studies had some success decoding these states from brain activation patterns observed during task-based functional magnetic resonance imaging (fMRI), but the results have varied across studies. Moreover, prior studies have often been limited by small sample sizes, weak decoding performance, and non-whole-brain analyses, leaving the neural representations of arousal and valence largely unresolved. Here we successfully decoded arousal and valence from whole-brain task-fMRI data collected from 132 participants during exposure to 300 unique emotional stimuli, including 150 movie clips and 150 text scenarios that reliably induced a wide range of arousal and valence states. Mass univariate general linear models identified block-level activation (emotion stimuli > washout) from all gray matter voxels. Multivariate regression analysis predicted arousal and valence ratings based on these gray matter activations. Patterns in the fMRI data underlying arousal and valence were robust, as they were successfully decoded across both induction modalities using five different linear multivariate regression models. Although significant, decoding from scenarios was less successful than from movies, likely due to their more imaginative nature. In particular, decoding arousal from scenarios only showed low predictive utility. Representations of arousal and valence were widespread throughout the brain, and we reveal cerebellar and brainstem contributions that have largely been absent in past fMRI decoding studies. These findings clarify the distributed neural basis of arousal and valence and provide a foundation for future clinical research on the role of these constructs in affective dysregulation.

Psychotic-like experiences (PLEs) are common in youth and predict later mental health problems. Bullying victimization is a robust environmental risk factor for psychopathology including PLEs, but whether its association with PLEs reflects shared genetic liability, individual-specific putatively causal effects, or reciprocal processes is unclear. We analyzed seven waves of longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study, examining associations across the sample in addition to leveraging within-family comparisons among twin and sibling pairs who were concordant or discordant for exposure to bullying victimization. Using linear mixed-effects and cross-lagged models, we found that youth reporting bullying victimization were more likely to endorse significantly distressing PLEs than non-victimized youth (caregiver-reported odds ratio=2.35; youth-reported odds ratio=4.10). Longitudinal analyses revealed bidirectional associations: prior bullying predicted subsequent increases in distressing PLEs, and prior PLEs predicted elevated risk of later bullying victimization. Genetically-informed within-family analyses indicated that both shared genetic influences and individual-specific factors contributed to these associations; critically, bullied youth exhibited higher odds of distressing PLEs than their non-exposed siblings (youth-reported odds ratio=6.67; 95%CI:4.96-8.96), consistent with an individual-specific effect of victimization. Together, these findings suggest that bullying and PLEs are linked through reciprocal developmental processes that are not fully explained by familial confounding. More broadly, our results highlight bullying prevention as a plausible leverage point for reducing early psychosis-spectrum risk and illustrate the value of integrating within-family designs to help disentangle genetic and environmental contributions to mental health outcomes in adolescence. Significance StatementUnderstanding how early adversity shapes mental health trajectories is important for science and public policy. Using nationally representative, longitudinal twin and sibling data, analyses show that bullying victimization and psychotic-like experiences in youth are linked through reciprocal processes that cannot be fully explained by shared genetics or family background. Bullied youth were more likely to endorse distressing psychotic-like experiences than their own non-bullied siblings, providing rare evidence for individual-specific effects of bullying victimization. Early psychotic-like experiences also increased subsequent risk of being bullied, suggesting a potential feedback loop that may compound risk. These findings demonstrate how social environments and mental health dynamically interact and point to bullying prevention as a population-level strategy with potential to reduce early psychopathology risk.

Major psychiatric disorders typically emerge in youth and exhibit shared and disorder-specific behavioral phenotypes and neuroanatomical alterations, yet the transdiagnostic neurobehavioral gradients and environmental interactions contributing to this heterogeneity remain poorly understood. Here, we present a transdiagnostic cohort of 1,755 youths aged 10-24 years, including 1,040 patients with bipolar disorder (BD), major depressive disorder (MDD), or schizophrenia spectrum disorder, and 715 healthy controls. Individualized gray matter volume (GMV) were quantified relative to population-based norms and integrated with behavioral phenotypes and environmental exposures. We identified transdiagnostic severity gradients across emotional and non-emotional symptoms, cognition, and personality traits, alongside widespread negative GMV deviations and diagnosis-specific effects in the pars opercularis and posterior cingulate, key hubs of the action-mode network orchestrating goal-directed functions. Two brain-behavior modes were identified: a cognitive mode linking posterior cortical variation with processing speed and an emotional mode associating prefrontal regions and the paracentral lobule with self-injurious behaviors. Further analyses indicated that adverse social environments were indirectly associated with brain structural deviations through behavioral pathways in BD and MDD, whereas air pollution (PM2.5) specifically moderated brain-behavior relationships in MDD. Together, these findings elucidate transdiagnostic neurobehavioral gradients across youth psychiatric disorders, with environmental exposures differentially embedded within neurobehavioral systems.

Autism spectrum disorder (ASD) is increasingly conceptualized as a disorder of large-scale functional brain network organization rather than isolated regional abnormalities. Graph-theoretical analysis provides a principled framework for characterizing such distributed network reconfiguration. Here, we investigated global, nodal, and system-level functional network topology in ASD using a large, multi-site resting-state fMRI dataset. Resting-state fMRI data from 996 participants (428 ASD, 568 healthy controls) were obtained from the ABIDE I and II data repositories. Whole-brain weighted resting state functional networks were constructed using Pearson correlation. To improve robustness and reduce threshold-selection bias, graph-theoretical metrics were computed across a range of network sparsity thresholds and summarized using an area-under-the-curve (AUC) approach. At the global level, ASD was associated with reduced assortativity, and local efficiency, along with altered normalized characteristic path length ({lambda}), indicating local information processing and subtle deviations in network integration relative to an optimal small-world topology. Nodal analyses revealed non-random, region-specific alterations predominantly affecting higher-order associative systems. Increased nodal centrality and hub-like properties were observed in frontal and parietal regions within the frontoparietal control and dorsal attention networks, whereas reduced nodal efficiency and centrality were primarily localized to limbic and anterior temporal regions, including the temporal pole. System-level analyses, controlling for age, sex, and acquisition site, further demonstrated network-specific topological reorganization across multiple functional systems. Clinical correlation analyses identified modest but significant associations between nodal topology and core ASD symptom severity, particularly within default mode, limbic, and attention networks. Together, these findings indicate that ASD is characterized by subtle yet reproducible multi-scale reorganization of functional brain network topology, supporting a systems-level account of ASD neurobiology and highlighting the clinical relevance of large-scale network architecture.

Neuroimaging markers that capture interindividual differences may aid diagnosis and patient stratification, whereas those sensitive to intraindividual variation may inform prognosis and treatment monitoring. Yet, most machine-learning research in ADHD has emphasised interindividual prediction, leaving open whether neuroimaging can predict within-person cognitive changes in ADHD. Using a longitudinal ADHD-enriched dataset, the Oregon ADHD-1000 study (n = 594; 1,053 annual timepoints), we developed multimodal markers from resting-state functional and structural MRI to predict cognitive functioning. These markers showed good out-of-sample accuracy (r =.459) and performed comparably in children with and without ADHD. They explained 29.09% of interindividual variance in cognitive functioning, 33.48% of intraindividual cognitive trajectories, and 60.87% of intraindividual age-related cognitive development. They also accounted for substantial portions of the association between cognitive functioning and hyperactivity (58.79%) and inattention (25.99%). Together, these findings provide foundational evidence that f/sMRI-based markers can longitudinally track cognitive functioning, underscoring their potential, once further developed, for prognosis and treatment monitoring.

Psychosis is increasingly understood as a disorder of disrupted cortical excitation-inhibition balance, yet robust non-invasive translational biomarkers remain lacking. The resting-state fMRI Hurst exponent (HE) and EEG aperiodic spectral exponent are promising complementary biomarkers, with lower values in each proposed to reflect a shift towards cortical hyperexcitability, but they have not been jointly examined in psychosis, and the spatial and molecular architecture of HE alterations remains poorly defined. We therefore tested for convergent systems-level signatures across independent cohorts and modalities, using resting-state fMRI (107 patients, 53 controls) and EEG (547 patients, 363 controls). Whole-brain and regional HE were estimated using wavelet methods, and EEG aperiodic exponents were quantified using spectral parameterisation. Compared with healthy controls, individuals with psychosis showed reduced whole-brain HE and widespread regional reductions. Regional HE case-control differences were associated with cortical gene-expression patterns, with enrichment for potassium channel and GABA receptor pathways, and correlated with noradrenergic, muscarinic, serotonergic, glutamatergic and dopaminergic receptor density maps, but not with cortical thickness or symptom or cognitive measures. In the independent EEG cohort, psychosis was similarly associated with a reduced aperiodic spectral exponent. Together, these findings provide cross-modal evidence for altered cortical resting-state dynamics in psychosis, consistent with a shift towards cortical hyperexcitability. Integration with receptor-density and transcriptomic maps implicates biologically plausible molecular pathways and supports HE and EEG aperiodic activity as scalable translational biomarkers in psychosis.

Conduct disorder (CD) is the leading global cause of mental health burden in children and adolescents and has recently been hypothesized to be a neurodevelopmental disorder. Although prior research has identified neuroanatomical differences associated with CD, it remains unclear whether these differences reflect atypical brain development. Here, we investigated the difference between an individuals brain age and chronological age as a proxy for variations in brain maturation. Using a pretrained model, we estimated brain age from structural neuroimaging data obtained from 1,119 youth with CD and 1,183 typically developing controls across 14 international cohorts participating in the ENIGMA-Antisocial Behavior Working Group. Youth with CD exhibited a statistically robust but small acceleration in brain age compared to typically developing youth (around 0.50 years), which was restricted to the adolescence-onset subtype of the disorder. Our large-scale, coordinated analysis provides the first evidence of accelerated neurodevelopment as a potential mechanism underlying CD.