Nature Mental Health

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.

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.

Patterns of brain circuit dysfunction underlying depression and anxiety have been increasingly characterized, including dimensional and subtype variation. A key challenge is determining how such patterns generalize across populations and measurement frameworks. Here, we introduce HARMONY, a harmonized multimodal neuroimaging dataset supporting large-scale investigation of brain behavior associations across symptom-defined dimensions. HARMONY integrates four Human Connectome Project style Connectomes Related to Human Disease cohorts spanning adolescence to later adulthood and capturing anxious misery symptoms. The resource combines standardized HCP style preprocessing, quality control, imaging-derived phenotypes, and harmonized symptom measures into a clinically enriched public dataset. Proof of concept analyses using HARMONY showed that pooling heterogeneous cohorts increased statistical power for detecting associations between imaging-derived phenotypes and anhedonia and depression severity. Effect sizes remained modest, consistent with symptom-based measures across heterogeneous samples. Functional imaging derived phenotypes showed the strongest multivariate predictive performance. In summary, HARMONY provides a large multi cohort resource for reproducible mental health neuroimaging research.

BackgroundA central goal in psychiatry is to move from symptom-defined diagnoses toward biologically interpretable and reliable phenotypes. In cocaine use disorder (CUD), many resting-state abnormalities have been reported, but few circuit-level findings have been explicitly screened for reliability. We tested whether prespecified thalamocortical features yield a reproducible phenotype in CUD and whether that phenotype reflects diagnosis, recent cocaine use, or longer-term illness history. MethodsDiscovery analyses used resting-state data from 105 participants (46 healthy controls, 59 CUD). From a 13-region thalamocortical circuit, we derived an HC-trained LEiDA state model, generated 11 prespecified features, and advanced only those meeting split-half reliability criteria (ICC[3,1] [&ge;]0.40). A separate paired TMS sample (n=44) was used for extension analyses. ResultsFive features survived reliability screening. Within CUD, longer duration since beginning cocaine use was associated with greater occupancy of a control-like state (standardized {beta}=0.37, q=0.005) and stronger whole-thalamus connectivity with control frontoparietal cortex (standardized {beta}=0.30, q=0.018). Neither days since last use nor CUD vs. healthy diagnosis were associated with any reliable feature after correction. Joint-history models indicated that the signal was better explained by longer-term use history than by recent use. Localization analyses indicated the connectivity effect was concentrated in dorsal thalamic regions. TMS-interaction and effective-connectivity follow-ups were null. ConclusionsReliability screening identified a thalamocortical control-network phenotype in CUD that tracks longer cocaine-use history rather than diagnosis or recent use. More broadly, this workflow offers a practical framework for screening candidate circuit-level psychiatric phenotypes for reliability.

Identifying generalizable brain-based biotypes across independent cohorts is critical for parsing heterogeneity in Major Depressive Disorder (MDD), yet robust subtypes spanning micro- and macroscales remain poorly defined. We applied stability-based clustering to cortical thickness data from 1,531 MDD individuals in UK Biobank (UKB), with external validation in 144 inpatients from IRCCS Ospedale San Raffaele (HSR). Two distinguishable clusters emerged (accuracy=87.5%), with one showing widespread cortical thinning, anergy-related symptoms, childhood trauma, and diabetes comorbidity. This profile generalized with 96.5% accuracy in a hold-out UKB sample and 80.6% in HSR. Mapping clusters cortical profiles onto Neurosynth meta-analytic activation patterns revealed a ventral-dorsal gradient linked with emotion regulation, interoceptive, and motivational processes. Spatial correlations with 19 neurotransmitter receptors and transporters obtained from positron emission tomography identified dopamine transporter as the dominant contributor in UKB, and histamine receptor H3 in HSR. These findings provide a reproducible framework linking MDD subtypes to multiscale biological complexity.

The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p-factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.

Adolescence is a period of rapid neurodevelopment during which psychiatric symptoms may emerge, yet symptom-specific markers show inconsistent associations with cognition and brain structure and can rarely be generalised longitudinally. Using data from the ABCD Study, we derived a transdiagnostic mental-health burden measure that integrates multiple symptom domains and examined its cognitive and structural brain correlates in early adolescence longitudinally. Adolescents with higher burden showed consistently lower performance in vocabulary, memory, and processing-speed, alongside widespread reductions in whole-brain, cortical, and white-matter volumes at baseline and after 2 years. These effects were strongest in a subgroup with persistent high burden and replicated in cross-sectional analyses. After 4 years, mental-health differences remained robust, although brain-behaviour associations weakened, likely reflecting developmental reorganisation and reduced sample size. Our study demonstrates that global mental-health burden provides a scalable, developmentally appropriate marker of early psychiatric vulnerability that overcomes limitations of symptom-specific approaches.

Tourette syndrome (TS) is a neurodevelopmental disorder characterized by symptoms that emerge in childhood and often improve or even disappear in adulthood, providing a model for understanding how altered brain development shapes neural structure and function. We investigate brain structural alterations in TS and Chronic Tic Disorders (TS/CTD) across development, presenting the largest structural neuroimaging analysis for TS/CTD to date (1,803 individuals from the ENIGMA-TS Working Group), and integrating with large-scale genomewide association studies. Nonlinear age effects were observed in cortical thickness across development and in thalamic volume in children, indicating altered trajectories of brain maturation . Pediatric and adult TS/CTD showed distinct structural patterns, with widespread alterations in childhood and more focal changes in adulthood. Children also showed the most prominent effects highlighting the involvement of orbitofrontal cortex and putamen, alongside additional regions such as frontal and paralimbic areas. Genetic pleiotropy analyses identified overlap between TS/CTD-associated genetic effects on brain structure and neuroanatomical differences. Cross-disorder comparisons revealed correlations with ADHD and OCD and age-related patterns. These findings demonstrate altered neurodevelopmental trajectories in TS/CTD and implicate systems underlying inhibitory control and urge regulation.

Background: Obsessive-compulsive disorder (OCD) is characterized by disturbing thoughts (obsessions) that initiate anxiety-reducing thoughts or behaviors (compulsions). For patients with treatment-resistant OCD (tr-OCD), neuromodulation techniques, like capsulotomy (a lesion in the anterior limb of the internal capsule) and deep brain stimulation (DBS), have emerged as interventions that likely regulate connectivity between the prefrontal cortex (PFC) and subcortical targets. Three patients (Cap-DBS1-3) underwent a failed capsulotomy followed by successful DBS. Here, we aimed to understand the brain connections disrupted by failed capsulotomy vs modulated by successful DBS. Methods: We used diffusion-weighted magnetic resonance imaging (dMRI) tractography in a control cohort with tr-OCD (n=12) and in two of the Cap-DBS patients themselves to determine connectivity profiles of the capsulotomy, volume of tissue activated (VTA), and potentially necessary tracts (VTA minus capsulotomy tracts). We used whole-brain, PFC-focused, and subcortically-focused tractography algorithms to fully explore the space of possible connections. Results: Capsulotomy regions-of-interest (ROIs) connected with a variety of PFC and subcortical regions. VTA ROIs and potentially necessary tracts had limited and inconsistent PFC connectivity but substantial subcortical connectivity. While correlated to the average OCD connectome (r = 0.214, 95% CI [0.177, 0.251]; r = 0.756, 95% CI [0.739, 0.772]), the Cap-DBS connectomes had many edges that were stronger (z-score > 3). Conclusions: The connectivity profile of potentially necessary tracts for successful DBS treatment after failed capsulotomy revealed a surprising proportion of subcortical regions and inconsistent PFC involvement, highlighting an often-ignored set of connections that may be critical to effective DBS.

Apathy is a leading driver of functional disability in schizophrenia, yet effective mechanism-based therapies are lacking. We evaluated whether cerebellum-ventral tegmental area functional connectivity (CB-VTA FC) meets the criteria for clinical translation as a therapeutic neuromodulation target. Using resting-state fMRI in three independent repeated-imaging cohorts (healthy controls, early psychosis or chronic schizophrenia patients, minutes-months inter-scan intervals), CB-VTA FC was always stable and individual-specific (stability r=0.52-0.69; differential identifiability {Delta} r=0.21-0.35; all p<10-5). In paravermal cerebellar territories, it tracked apathy severity in two patient cohorts (early psychosis, Crus I/II: n=99; r97=0.36, p=2.65 {middle dot} 10-4; chronic schizophrenia, Lobules VIIB/VIIIA: n=87 scans [65 patients]; t85=4.06, p=1.1 {middle dot} 10-4). In a meta-analysis of 39 randomized controlled transcranial magnetic stimulation trials (n=1,624; 867 active), connectivity of neighboring areas to stimulation site predicted negative symptoms improvement. CB-VTA FC thus emerges as a stable, individual-specific, and symptom-related therapeutically relevant circuit, constituting a mechanism-informed precision neuromodulation target in schizophrenia, ready for prospective clinical trials.

BACKGROUNDAutism spectrum disorder (ASD) is marked by profound neurobiological heterogeneity, which drives inconsistent neuroimaging findings and impede the discovery of reliable biomarkers for precise diagnosis and phenotypic prediction. Although deep learning has shown promising predictive power, its black-box nature obscures the mechanistic interpretability underlying high-dimensional learned representations, limiting their translation into actionable neurobiological insights. METHODSWe present IBSS-GAT, a novel interpretable deep learning framework that explicitly models the spatiotemporal landscape of individual-specific internal brain states and integrates a two-stage mechanistic interpretability pipeline to bridge model-derived features to well-characterized neurodynamic processes and clinical phenotypes. RESULTSAcross three independent large-scale neuroimaging cohorts, IBSS-GAT achieved state-of-the-art classification performance in both cognitive decoding (99.30% accuracy in the HCP-task cohort) and ASD identification (77.26% accuracy in the ABIDE-I, and 77.49% accuracy in the ABIDE-II). Interpretability analyses revealed the frontoparietal control network (FPCN) as a convergent hallmark of ASD, mechanistically anchored in the pathological hyperexpression of an FPCN-dominated metastate. Moreover, both the increased metastate occupancy and model-derived feature strength of FPCN emerged as robust predictors of clinical symptom severity in ASD across ABIDE-I and ABIDE-II. CONCLUSIONSOur work establishes a robust, mechanistically interpretable link between individual high-dimensional brain dynamics and heterogeneous ASD phenotypes, revealing generalizable, neurobiologically grounded brain markers with the potential to inform precision medicine in ASD.

This study examined whether connectivity-guided accelerated intermittent theta-burst stimulation (iTBS) improves depressive symptoms beyond routine multimodal inpatient care in hospitalized patients with treatment-resistant depression (TRD). In this randomized, double-blind, sham-controlled trial, patients with unipolar TRD received active or sham iTBS. Stimulation targeted an individualized left dorsolateral prefrontal cortex site showing most functional anticorrelation with the subgenual anterior cingulate cortex on resting-state functional MRI. Treatment was delivered as 3 daily sessions over 10 weekdays (30 sessions; 54,000 pulses) as an inpatient augmentation strategy. Primary and secondary outcomes were changes in Montgomery-Asberg Depression Rating Scale (MADRS) and Beck Depression Inventory-II (BDI-II) scores during the 2-week stimulation phase. Exploratory endpoints included response and remission rates. Of the 57 randomized patients, 51 completed treatment (active, n=27; sham, n=24). The cohort exhibited moderate-to-severe treatment resistance (mean Maudsley Staging Method score, 10.9) and high psychiatric comorbidity. Active iTBS was associated with significantly steeper MADRS improvement than sham (-3.54 points/week; 95% CI, -5.53 to -1.55; PFDR=.02), corresponding to model-estimated reductions of 12.06 versus 4.98 points with a large effect size (d=-0.89). BDI-II trajectories similarly favored active treatment, though with a smaller effect (group-by-time estimate, -0.23 points/day; 95% CI, -0.41 to -0.05; PFDR=.04; d=-0.22). MADRS response rates were higher with active iTBS (42.3% vs 13.0%), while remission rates were numerically but not significantly higher (26.9% vs 12.5%). No serious adverse events occurred. In conclusion, connectivity-guided iTBS produced significant add-on antidepressant effects during acute inpatient treatment of TRD. Larger multicenter trials are needed to establish durability and optimize implementation.

Major depressive disorder (MDD) is a leading cause of disability worldwide with risk of onset and recurrence linked to depressive ruminative thought patterns. Mindfulness-based cognitive therapy (MBCT) is an evidence-based treatment for depression that targets the ability to recognise, decenter, and disengage from ruminative thought patterns. Elucidating how MBCT impacts hierarchical brain organisation may be key to understanding the processes by which MBCT can modulate ruminative tendencies. In a randomised controlled functional magnetic resonance imaging (fMRI) trial on individuals with MDD (N=80) before and after MBCT in addition to treatment as usual (TAU), we investigated changes in hierarchical brain organisation during resting-state and rumination. We built whole-brain models to obtain generative connectivity (GEC) matrices per patient and quantified brain hierarchy by measuring the global directedness and regional trophic levels in each GEC, in which greater directedness reflects more directional information flow and less recurrence. Global directedness in MBCT+TAU compared to TAU increased during rumination, with no changes during resting-state. Furthermore, increased regional breadth of hierarchy during rumination was related to improvements in clinical and behavioural outcomes following MBCT+TAU. Increased brain hierarchy during rumination following mindfulness training may be consistent with a shift away from self-reinforcing negative mental loops towards more differentiated and less coupled cognitive and bodily cycles, supporting MBCT's ability to interrupt ruminative processes. Hierarchical brain dynamics may hold promise as a treatment-sensitive marker and a potential mechanism of therapeutic change in MBCT for depression.

Background: Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising treatment across addictive disorders including Cannabis Use Disorder (CUD). Stimulation of two rTMS-targets, the ventromedial prefrontal cortex (vmPFC) and the left dorsolateral prefrontal cortex (LDLPFC), limbic and executive control network hubs respectively, may yield differential effects. In this pilot trial, we explored the differential effects of 36-sessions of rTMS applied to either the vmPFC or LDLPFC. Methods: Treatment-seeking participants with moderate or severe CUD (n=20, 10F, age=33.3+9.8SD) were randomized to 36-sessions of open-label rTMS (two sessions-per-visit, two or three visits-per-week) to either the LDLPFC (3000-pulses; 10Hz) or vmPFC (900-pulses; 1Hz) using personalized functional Magnetic Resonance Imaging (fMRI) targets along with three-sessions of Motivational Enhancement Therapy. At baseline and following rTMS, the Time-Line Follow-Back was used to measure Days-per-week of cannabis use and the fMRI Regulation of Craving (ROC) task was used to measure network activation to cues associated with long-term negative ('Later') and short-term positive ('Now') consequences of cannabis use. Results: Eighty percent of participants completed study-rTMS. There was a significant decrease in days-per-week of cannabis use in both groups (vmPFC: d=7.9; DLPFC, d=3.1) between the four-weeks of baseline and seven-weeks of follow-up. LDPFC-rTMS reduced fMRI BOLD signal magnitude and increased LDLPFC functional connectivity in response to cues, while vmPFC-TMS reduced functional connectivity. Conclusions: Treatment-seeking participants with CUD reduced the number of days-per-week they used cannabis when receiving rTMS applied to either the LDPFC or vmPFC, while fMRI effects differed by treatment target. Future larger sham-controlled trials are needed for efficacy and biomarker determination.

Background: Major depressive disorder remains a leading cause of disability. While subgenual cingulate cortex (sgCC) deep brain stimulation (DBS) shows promise for medically refractory depression, clinical outcomes have been heterogeneous, suggesting that individual differences in neural circuitry engagement may critically influence therapeutic efficacy. We aimed to define the electrophysiological signatures of sgCC efferent connectivity using single-pulse electrical stimulation (SPES) with intracranial stereo-EEG (sEEG) to inform rational targeting and physiological biomarkers for sgCC-DBS. Methods: In four patients undergoing clinically indicated sEEG for seizure mapping, SPES was delivered through sgCC pairs, while distributed brain stimulation-evoked potentials (BSEPs) were recorded across cortical and subcortical sites. Responses were characterized using Canonical Response Parameterization to extract reproducible waveforms and per-trial reliability. Results: sgCC stimulation elicited reproducible, spatially organized BSEPs across frontal, limbic, and paralimbic networks, aligning with known anatomical pathways. Frontal recruitment featured robust, lateralized orbitofrontal activation favoring the ipsilateral central, medial OFC and bilateral ventromedial prefrontal responses. Limbic effects demonstrated bilateral cingulate activation with stronger ipsilateral recruitment and lateralized amygdala and hippocampal responses. Paralimbic engagement included insular responses with subject-specific anterior predominance and bi-hemispheric temporal-polar slow-wave deflections. Conclusion: These findings provide direct electrophysiological evidence of distributed, lateralized sgCC divergent network connectivity in the human brain, offering physiologic confirmation of its role in affective circuitry. The observed topography and laterality have direct applications for sgCC-DBS targeting and implicate BSEP signatures as candidate biomarkers to guide patient-specific therapy.

Importance: 22q11.2 deletion syndrome (22q11DS) is among the strongest genetic risk factors for neuropsychiatric disorders and has marked effects on brain structure. Yet, it remains unclear which neuroanatomical features reflect uniform effects of the deletion versus inter-individual biological processes relevant to psychiatric outcomes. Identifying these features is critical for developing targeted treatments and interventions. Objective: To identify brain regions where 22q11DS exerts its most consistent and most variable impacts, and to test whether these patterns align with normative neurotransmitter receptor distributions and cortical growth trajectories. Design: Multisite cross-sectional case-control study. Setting: T1-weighted brain MRI data were obtained across 15 scanners. MRI data underwent standardized processing, quality control procedures and statistical site-adjustment using ComBat. Participants: A total of N = 438 individuals with 22q11DS (5-54 years, 48% females) and 380 typically developing controls (6-58 years, 48% females). Main Outcomes and Measures: Primary outcomes were global and regional cortical thickness and surface area . Mean and dispersion estimates were calculated using double generalized linear models, correcting for age, age2, sex (and intracranial volume for surface area). Quantile shift functions characterized fine-scale distributional differences. Sensitivity analyses adjustedt for co-occurring neuropsychiatric disorders, antipsychotic use and deletion subtype. Secondary outcomes included spatial correspondence between regional structural alterations and normative maps of neurotransmitter receptor density and cortical expansion. Results: Compared with controls, individuals with 22q11DS showed widespread mean differences in cortical thickness and surface area. Notably, 22q11DS was associated with greater regional heterogeneity in both measures, except for reduced dispersion in the anterior cingulate. Effects were attenuated after covariate adjustment. Cortical thickness differences spatially overlapped with regions enriched for glutamatergic and GABAergic receptors. There was partial evidence linking surface area dispersion patterns to normative cortical growth trajectories. Conclusions and Relevance: 22q11DS exerts broad effects on cortical structure consistent with a global developmental mechanism, reflected in widespread mean shifts. Beyond these, region-specific variability, particularly in cortical thickness, suggests individualized neurobiological processes. The anterior cingulate emerges as a region of consistent structural deviation. Overall, structural variability in 22q11DS aligns with normative patterns of excitatory-inhibitory signaling and cortical development, implicating these pathways as potential targets for intervention.

Pathological activity within frontal cortical circuits is common in many neuropsychiatric disorders, such as obsessive-compulsive disorder (OCD). We developed an invasive brain mapping protocol in which temporary electrodes are implanted in candidate sites to identify personalized stimulation targets that can acutely relieve OCD symptoms. We found that stimulation within segments of the anterior limb of the internal capsule (ALIC) focally suppressed the structurally and functionally connected region of prefrontal and cingulate cortex. By leveraging the topographic organization of the ALIC, we reversibly inactivated frontal cortical sites with ALIC stimulation to determine which cortical regions are necessary for sustaining OCD symptoms. Stimulation of ventral capsule (VC) near the globus pallidus within the ALIC was associated with suppression of lateral orbitofrontal cortex activity and acute and long-term improvements in OCD symptoms. These results provide a paradigm for leveraging ALIC topography to deliver targeted connectomic neuromodulation to frontal cortex to treat neuropsychiatric disorders.

Understanding the dynamics of brain-behaviour relationships during adolescence is critical for elucidating the neurodevelopmental basis of mental health. Leveraging two large-scale longitudinal cohorts--the Adolescent Brain Cognitive Development (ABCD) and IMAGEN studies, comprising over 10,000 participants aged 10 to 22 years with six waves of multimodal neuroimaging and behavioural data, we applied multi-view sparse canonical correlation analysis to investigate evolving associations between structural MRI, resting-state functional connectivity, and multi-domain behavioural measures. Our findings reveal four fundamental patterns of developmental reorganisation in brain-psychopathology relationships. First, symptom profiles evolved from predominantly externalising features (aggression, attention problems) at ages 10-12 toward global psychopathology by age 14, then transitioned toward internalising features (e.g., anxiety, depression) by ages 19-22, reflecting fundamental shifts in vulnerability from behavioural dysregulation to affective disturbance. Second, cortical thickness exhibited negative associations with externalising symptom profiles throughout development. During early adolescence (ages 10-14) this was driven by broadly distributed decreases across sensorimotor, temporal, visual, and cingulate regions alongside overall mean cortical thickness. After 14, this diffuse pattern shifted towards late maturing association cortices, notably the dorsolateral prefrontal and lateral temporal cortices. Third, this was accompanied by subcortical effects that exhibited greater age-specificity: whilst cerebellar volume contributions were evident at most timepoints, basal ganglia volume influence was principally evident in early development (ages 10-12), with thalamic structures and global subcortical grey matter volume becoming dominant at age 14, marking a transition in which subcortical structures mediate psychopathology associations. Fourth, functional connectivity showed a more dynamic developmental trajectory. During early adolescence, symptom associations were driven by positive connectivity between cognitive control and sensorimotor networks, whereas late adolescence exhibited predominantly positive connectivity patterns, transitioning from dense sensorimotor-frontoparietal configurations to more specific patterns involving the central executive and default-mode networks. These findings fundamentally challenge static biomarker models, demonstrating that adolescent psychopathology reflects developmentally contingent brain-behaviour relationships rather than static neural markers. Age 14 emerges as a critical inflection point marked by convergent thalamic reconfiguration, global subcortical grey matter dominance, and symptom profile transitions. This work provides an empirical foundation for precision mental health strategies tailored to specific developmental windows, with implications for reducing psychiatric burden in youth.

Non-suicidal self-injury (NSSI) is common among adolescents with emotional disorders, yet biological indicators of current NSSI status remain limited. We developed a genome-aware multi-omics modeling framework in 107 adolescents with emotional disorders, including 53 without NSSI and 54 with current NSSI. The model integrated metabolomic, inflammatory, clinical blood and genome-derived features, with polygenic risk score and rare variant burden used as genetic-context variables. The fusion model achieved the strongest classification performance (mean AUC = 0.811) and outperformed single-omics alternatives, indicating that NSSI status was better represented by distributed multi-omics patterns than by a single biomarker layer. Repeated modeling prioritized 42 stable features, many of which were not significant in conventional univariate testing. Group-specific network reconstruction further revealed peripheral reorganization, including convergence of non-NSSI modules into an NSSI-associated module that linked inflammatory recruitment with weaker immune-communication, repair and support-related signals. Exploratory MRI, gut-related and stress-endocrine analyses provided additional biological anchors, while a compact sentinel marker panel translated the full model into clinically readable profiles. These findings support a distributed, genome-aware peripheral state associated with current NSSI and provide a framework for future validation of multi-omics state markers in adolescent emotional disorders.

IntroductionTotal and social cognition deficits independently predict functioning in psychosis, but targeting these in clinical trials are unsuccessful in improving function. The admixture of schizophrenia and affective psychoses (aff-P) cases could be a roadblock if these differ in cellular pathology. MethodsWe examined cognitive functioning (MATRICS) and hippocampal cellular pathologies based on metabolite biomarker concentrations (1H-MRSI), using categorical and transdiagnostic classifications in 80 participants: 22 non-psychotic affective disorder (NP-aff), 25 healthy controls (HC), and 33 with psychosis, including 20 schizophrenia and 13 aff-P cases. ResultsNP-aff and HC had similar total cognition (46.64{+/-}12.01 vs 41.10{+/-}17.88), both superior psychosis (28.34{+/-}12.34; ps<0.01). Metabolite concentrations were similar across all groups but showed significant within-group associations to cognitive tests. For HC, total cognition, working memory and reasoning deficits were associated with reduced neuronal integrity (-.414, -.422, -.433, ps<.05), although no biomarker predicted total cognition in the clinical groups. For NP-aff, elevated myelin/membrane concentrations accompanied cognitive deficits; significantly so for visual learning deficits (.446, p<.05), which were also associated with decreased glia (-.503, p<.05). In all psychotic cases only reduced myelin/membrane concentrations predicted deficits (-.514, p<.05); but separating schizophrenia from aff-P, respectively showed reduced glutamate/excitation in schizophrenia (-.673, p<.05) but higher myelin/membrane and neuronal integrity concentrations (.575, .581, ps<.05) in aff-P. ConclusionsSchizophrenia and aff-P significantly differed for biomarkers of cellular pathology related to social cognition. Distinctly different underpinnings for cognition were also identified for other groups, aligning with DSM-5 and ICD disorder based categories. These findings include support for heterogeneous, but not transdiagnostic, conceptualizations of cognition and psychosis.