Nature Mental Health

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.

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.

Adolescence is a critical developmental window for the emergence of substance use and psychosis-spectrum symptoms, yet early risk for these outcomes remains poorly understood. Using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (n=10,134), we tested whether demographic, clinical, and structural and functional neuroimaging measures assessed in childhood (mean baseline age=9.96 years) predict later adolescent substance use, psychotic-like experiences, and/or their co-occurrence. Multivariate machine learning models reliably predicted later emergence of psychotic-like experiences (AUROC=0.780) and their co-occurrence with substance use (AUROC= 0.828), as well as substance use on its own (AUROC=0.626). Distinct patterns of functional brain connectivity, task-related brain activation, demographic, and clinical factors differentiated each outcome. Findings suggest that partially dissociable developmental risk profiles are detectable as early as childhood, and results underscore the importance of explicitly modeling comorbidity when interrogating risk factors for mental health outcomes.

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.

BackgroundAutism Spectrum Disorder (ASD) is marked by pronounced biological heterogeneity, yet most neurochemical studies have relied on single-analyte comparisons that cannot capture coordinated variation across neurotransmitter systems. Whether ASD blood neurotransmitter profiles reflect discrete subtypes, a continuous landscape, or something in between remains unresolved. MethodsWe applied NeuroCLAD, a structured multivariate analytical framework, to peripheral blood neurotransmitter profiles from 261 children with ASD (mean age 6.98 {+/-} 3.13 years; 78.5% male). The pipeline incorporated z-score normalisation, natural cubic spline residualisation for age and sex, principal component analysis, k-means clustering, consensus stability assessment, Gaussian mixture modelling, Cohens d enrichment analysis, and clinical symptom mapping. Cross-compartment consistency was explored using urine neurotransmitter profiles from the same cohort. ResultsTwelve reproducible biochemical cluster patterns were identified, each characterised by distinct pathway-level fingerprints spanning trace amines, monoamines, catecholamine turnover, histamine signalling, and excitatory-inhibitory amino acid balance. Cluster stability was confirmed across 200 bootstrap iterations. Gaussian mixture modelling showed that most individuals were assigned with high confidence, while a subset occupied transitional positions between clusters, consistent with stable biochemical modes embedded within a continuous landscape. Descriptive behavioral mapping revealed graded symptom tendencies across biochemical modes, particularly for aggressiveness, self-injurious behaviour, and picky eating. LimitationsThe findings are based on peripheral blood measurements, which indirectly reflect central neurochemical activity. The study is cross-sectional, lacks a neurotypical comparison group, and behavioural associations are exploratory given cluster sizes. External replication in an independent cohort has not yet been performed. ConclusionsBlood neurotransmitter biology in ASD is neither uniform nor discretely partitioned, but organised into reproducible biochemical modes within a continuous multivariate landscape. These findings support a dimensional view of ASD neurochemistry and provide a foundation for pathway-informed, individualised approaches to biological characterisation.

BackgroundAttention-deficit/hyperactivity disorder (ADHD) has traditionally been conceptualized categorically, with efforts to identify disorder-specific neurobiological endophenotypes. However, dimensional models suggest that brain-behavior organization may follow developmental axes that cut across diagnostic boundaries. We tested whether neural dynamics and cortical excitability differentiate those with ADHD diagnoses from typically developing (TD) peers, and whether brain-behavior covariance aligns with diagnostic or developmental dimensions. MethodsWe studied 84 participants aged 8-17 years (51 ADHD, 33 TD). High-density electrophysiological (hdEEG) measures included task-free source-resolved data used to derive mean global brain fluidity (variance of dynamic functional connectivity) and region-specific cortical excitability. Behavioural measures included self- and parent-report questionnaires, cognitive control (CC) tasks, and neuropsychological tests. Partial least squares (PLS) assessed multivariate brain-behavior associations including age, followed by clustering based on latent component scores. ResultsGroup differences emerged in parent-report questionnaires and CC tasks, but not in neuropsychological measures. ADHD individuals showed higher mean global brain fluidity and increased cortical excitability. The excitability-fluidity relationship was network-dependent: higher excitability predicted higher fluidity in task-positive networks and lower fluidity in default-mode and salience networks, with no group effects. PLS identified a latent dimension linking neural metrics with age, verbal fluency, inhibitory control, and positive affect, but it did not distinguish ADHD from TD. Clustering revealed two neurodevelopmental profiles spanning both groups. ConclusionsWhile ADHD is associated with mean-level differences in neural dynamics, brain-behaviour organization follows a developmental neurocognitive-affective axis that transcends the diagnostic boundary. These findings support a dimensional framework for understanding neurobiological variation in neurodevelopmental conditions.

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.

Background. Something in discourse with a person experiencing psychosis often "feels off" before formal assessment is completed, yet this disturbance has not been quantified at the level of ongoing dyadic conversation. Prior work has largely treated patient speech in isolation, limiting our capacity to measure how communicative disruption emerges within clinical exchange. Methods. We applied a three-level decomposition of conversational alignment in 109 patients with psychotic disorders (26 female) and 60 healthy controls (22 female) at baseline and 12 months (n = 115). Register divergence (dAUCnorm) captured lexical distance between interviewer and patient; embedding-based synchrony (rembed) measured semantic trajectory coupling; within-speaker coherence was computed separately for each speaker. We used linear mixed-effects models adjusted for timepoint and participant clustering. Results. Patients showed significantly greater lexical-semantic divergence from the interviewer (d = 0.48, p < .001) and reduced embedding-based synchrony (d = -0.59, p < .001), both effects replicating at each time point. Critically, the interviewer's within-speaker coherence was reduced during conversations with patients (d = -0.33, p = .016), indicating that the disruption extends beyond the patient to the interaction itself. Register divergence tracked impoverished thinking and synchrony tracked disorganized thinking (both FDR-corrected q = .038). Group differences were persistent at 12 months, indicating a partially stable profile. Conclusions. Conversational alignment in psychosis reveals a dyadic failure of semantic coordination that destabilizes the interviewing clinician's coherence even when patient narrative continuity is preserved. These transcript-derived alignment metrics offer a scalable approach to quantifying interpersonal communicative function from routine clinical encounters.

Problematic Sexual Behaviour (PSB) is defined as difficult to control recurrent sexual behaviours that continue despite adverse consequences, leading to social and functional impairment. There is debate whether PSB is a disorder of compulsion or addiction; PSB often co-occurs with neuropsychiatric disorders, but further elucidation regarding underlying biology is required. A deficiency in reward neurotransmitter systems (reward deficiency syndrome: RDS) may underlie a shared vulnerability to addiction. We conducted the first case-control genome wide association study (GWAS) of PSB in patients (n=448), and comparison participants with (n=196) and without PSB (n=1488). We used polygenic risk scores (PRS) to test genetic overlap with related psychiatric, behavioural and personality phenotypes. Three models were used: 1) All-PSB (patient + comparison) vs. controls, 2) Patient-PSB vs controls, and 3) RDS (yes/no). Results suggested genetic overlap of PSB with psychiatric conditions, with PRS for major depression, substance use, and others predicting PSB status. PRS for related behavioural phenotypes (e.g., externalizing, age at first sex, number of lifetime sexual partners) and personality traits also predicted PSB. The patient model showed stronger associations than the All-PSB model, and RDS had both shared and distinct genetics with PSB. As expected with the sample size, only suggestive hits were observed with single variant and gene-based tests. PSB may share genetic mechanisms with various conditions. Further research in larger cohorts is needed to better understand the underlying genetics and environmental factors involved, and to improve diagnostic classification, intervention and treatment prospects.

Background: Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) exhibit high clinical overlap, but categorical diagnostic boundaries obscure their shared, dynamic physiological vulnerabilities during real-world sensory processing. Methods: We analyzed multimodal eye-tracking synchrony in a large transdiagnostic pediatric cohort (N = 2,026) during naturalistic viewing of four distinct media paradigms. A novel 2D complex correlation framework captured gaze inter-subject correlation (ISC) magnitude and spatiotemporal phase divergence, while 1D pupil ISC measured autonomic arousal synchrony. Linear models evaluated dimensional (RDoC) and categorical (2x2 ANCOVA) diagnostic frameworks alongside rigorous medication and severity controls. Results: Dimensional models revealed a domain-general vulnerability: autistic traits independently predicted widespread reductions across gaze synchrony in all media contexts, and pupillary synchrony in narrative-driven contexts, whereas continuous ADHD traits showed minimal independent effects. In contrast, severe spatiotemporal misalignment (phase divergence) did not scale dimensionally but emerged strictly at clinical boundaries, reflecting highly idiosyncratic spatial locking in isolated ASD. Furthermore, categorical models demonstrated a robust, non-additive interaction: the clinical co-occurrence of ADHD paradoxically buffered against this severe spatiotemporal decoupling. Crucially, this protective phenotype was localized strictly to character-driven social narratives and remained highly significant after rigorously adjusting for daily stimulant medication, outlier instability, and baseline autism trait severity. Conclusions: These findings validate model-free physiological synchrony as a candidate transdiagnostic biomarker. Rather than compounding impairment, comorbid ASD and ADHD reflect competing, non-additive neurocognitive strategies that yield distinct, context-dependent visual phenotypes.

Background: Cannabis use is highly prevalent among emerging adults (18-25 years), a developmental period marked by ongoing neurodevelopment and heightened risk for cannabis use disorder (CUD). Structural alterations in the orbitofrontal cortex (OFC) and medial prefrontal/anterior cingulate cortex (mPFC/ACC) have been linked to cannabis use, though findings remain inconsistent in directionality. To address this, we examined cortical thickness and surface area of the OFC and mPFC/ACC subregions using the high-resolution Glasser atlas, allowing for more granular characterization of associations with CUD severity. Method: One hundred eleven emerging adults (41% male, aged=20.6{+/-}1.1 years) reporting significant alcohol and/or cannabis use completed clinical assessments and structural MRI. The OFC and mPFC/ACC were segmented into seven and six subregions per hemisphere, respectively. Multiple linear regressions tested associations between cortical thickness or surface area and DSM-5 CUD symptom count, controlling for alcohol use and intracranial volume. Subregions surviving false discovery rate correction were examined in relation to depression, trauma-related symptoms, impulsivity, and cannabis use motives. Results: Greater CUD severity was associated with lower cortical surface area and greater cortical thickness in OFC and mPFC/ACC subregions. Lower OFC surface area was correlated with coping- and enhancement-related cannabis use motives. Lower mPFC/ACC surface area and greater thickness were associated with more severe depression, trauma-related symptoms, and impulsivity. Conclusion: In high-risk emerging adults, greater CUD symptom burden is associated with lower surface area and greater thickness in OFC and mPFC/ACC subregions. Using the high-resolution Glasser atlas, these findings provide a more precise characterization of structural correlates of CUD and highlight potential neurobiological markers linked to affective and motivational processes underlying cannabis use.

Population-level sex biases in human neuroanatomy have been highly debated, with prior literature often limited by narrow age windows and simplistic assumptions about how brain regions scale with sex differences in average total brain size. Recent work leveraging massive, global datasets to flexibly chart normative brain structures across the lifespan provides a framework to overcome these hurdles. Here, using magnetic resonance imaging (MRI) data from over 100,000 individuals (51.7% F) from mid-gestation to 99 years, we chart normative population trajectories of 241 structural features capturing age-varying sex effects. We use split-half cross-validation, fitting generalized additive models for location, shape, and scale with nonlinear scaling terms to test sexs impact on each features distribution and map changes in sex biases over time. Our results reveal replicably significant age-varying sex biases in nearly all brain structures, and show that sexs effects survive nonlinear correction for total brain size. We find that after correcting for total brain size, regions are equally likely to be larger in males versus females, while males tend to show higher interindividual variability in a majority of regions. Probing temporal dynamics reveals that sex biases tend to increase with age, with male and female trajectories diverging across the lifespan. Finally, we demonstrate that normative scores from these models of age-varying sex effects are sensitive to case-control differences in six neuropsychiatric disorders. This work resolves several existing methodological issues to establish and quantify population-level sex biases in the largest-to-date study of lifespan sex-biases in the human brain. Significance StatementWhile males and females brains are more alike than different, understanding population-level sex biases is important for ensuring that research benefits everyone. Here, we build on recent advances in modeling growth trajectories, using data from over 100,000 individuals to overcome limitations in prior studies of sex differences in brain structure. Lifespan statistical models of each region demonstrate: 1) differences in male and female normative trajectories vary with age; and 2) sex biases in brain regions are not just caused by differences in overall brain size. We also show that recognizing sex biases in the population is important for accurately mapping brain correlates of psychiatric diseases, and provide a resource for future researchers to benchmark their own neuroimaging data.

Autism spectrum disorder and subclinical variation along the autism continuum are characterized by in sensory processing and cognitive integration, phenomena increasingly linked to atypical large-scale communication across cortical hierarchies. While structural and functional connectivity differences have been extensively documented, whether autistic traits are associated with a reorganization of the directional properties of ongoing cortical activity remains less understood. Here, we recorded resting-state EEG from 201 young adults selected from the lower and upper terciles of the Autism Quotient distribution and analyzed traveling-wave dynamics over parieto-frontal lines. Individuals with higher autistic traits showed a selective shift in left-hemisphere alpha-band traveling-wave directionality, driven primarily by reduced backward-dominant propagation and accompanied by a reciprocal shift toward forward dominance. This effect was anatomically specific, absent in an occipito-central control line set, and not accompanied by a matching pattern of group differences in oscillatory power, aperiodic spectral parameters, or peak alpha frequency. These findings identify resting-state alpha traveling waves as a candidate physiological signature of altered directional organization across the autism continuum, with potential relevance as a trait-sensitive neural marker.

Schizophrenia is a complex psychiatric disorder whose pathophysiology spans multiple spatial and temporal scales. Structural and functional neuroimaging studies have identified a broad range of disease-associated markers encompassing cortical atrophy, white matter disruptions, and aberrant functional connectivity patterns. Their application to personalized diagnosis and treatment selection has remained elusive. Here, we introduce the first Virtual Brain Twin (VBT) pipeline that integrates individual connectome-based network models with multimodal neuroimaging data, incorporating patient-specific structural connectivity, cortical thickness, and resting-state fMRI features to construct personalised whole-brain dynamical models. Dopaminergic and serotonergic signaling pathways are embedded within a mean-field framework, and simulation-based inference (SBI) is used to recover key pathophysiological parameters from individual patient data. The validity of this inference is first established using synthetic patients with known ground truth parameters, confirming that the pipeline can accurately identify underlying neurochemical states from simulated functional data. Applied to a cohort of 33 subjects in three clinical centers, the framework identifies personalized pathophysiological parameter regimes consistent with current neurobiological hypotheses of schizophrenia, including reduced cortical dopaminergic drive and elevated subcortical dopaminergic drive relative to healthy controls. Simulated pharmacological interventions within the VBT generate individualized medication effect trajectories that align retrospectively with known treatment outcomes (66.6% accuracy), demonstrating the frameworks capacity to capture patient-specific pharmacological responses. These results establish a principled and extensible computational foundation for neuroimaging-guided personalized medicine in psychiatry, with direct implications for two prospective clinical trials conducted in Marseille and Munich as part of the Virtual Brain Twin project, designed to evaluate VBT-guided individualised antipsychotic treatment selection.

BackgroundInternalizing disorders and cardiometabolic disease are common conditions that frequently co-occur in later life and may be attributed to shared genetic influences. While phenotypic effects of polygenic liability of adult disorders may emerge early in life, studies have not investigated this in the context of multimorbidity. This study set out to investigate early manifestations of polygenic liability to adult internalizing-cardiometabolic multimorbidity (ICM-MM) in a UK population birth cohort. MethodsWe used data from 5,821 individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC). We modelled trajectories of 12 mental and cardiometabolic health outcomes using mixed effects models, and investigated effects of adult ICM-MM polygenic liability on these trajectories. We also investigated associations of adult ICM-MM polygenic liability with circulating inflammatory proteins (Olink Target 96 Inflammation panel) at ages 9 and 24. ResultsAdult ICM-MM polygenic liability is associated with cardiometabolic traits and inflammation, and with changes in depressive symptoms and cardiometabolic traits over time in childhood through to early adulthood. A notable early life biological footprint is inflammation. We found that higher ICM-MM polygenic liability is consistently associated with higher interleukin-6 (IL6), tumor necrosis family superfamily member 14 (TNFSF14) and hepatocyte growth factor (HGF) levels in both childhood and early adulthood. ConclusionsAdult ICM-MM polygenic liability manifests early in life through changes in mental and cardiometabolic health and blood biomarkers, especially in increases of circulating inflammatory proteins related to obesity, immune cell chemotaxis and migration that may contribute to disease pathogenesis by seeding inflammation in relevant tissues.

A fundamental principle in human neuroscience is that the brain is organized into distinct functional regions specialized for particular processes. These functional regions develop early and are shaped by sensory experiences. Strikingly, the precise location of these functional regions is relatively consistent across individuals, with specific regions found in stereotyped locations with respect to the macroanatomy (e.g., particular sulci and gyri). An important question is how flexible this functional neuroanatomy is, particularly in neurodevelopmental disorders like autism which are characterized by atypical brain development and behavior. Here, we investigated this question by focusing on the organization of the face-processing network -- a well-characterized system of regions that are reliably localized across neurotypical individuals, including, famously, the fusiform face area. Using precision fMRI, we identified subject-specific face-sensitive regions in the brains of autistic and neurotypical adults, and assessed their topographical alignment across individuals. Face regions in autism were globally and highly variably displaced - located farther from their expected locations in the brain relative to much more homogenous localization in neurotypical adults. Autistic individuals whose face regions were more displaced had greater sociocommunicative challenges. In contrast, the spatial organization of object-sensitive regions was not affected. These findings suggest that the spatial organization of the face network is atypical in autism, with behaviorally meaningful increased variability in the precise location of face-sensitive areas, and highlights the importance of individualized approaches in neuroimaging. Significance StatementThe human brain is organized into specialized functional regions positioned in remarkably consistent locations across individuals. It is unclear how altered neurodevelopment affects this organization. Using precision fMRI, we found that autism was characterized by globally and highly variably displaced face-sensitive functional brain regions. Atypical spatial organization of these regions was associated with greater sociocommunicative difficulties. In contrast, the spatial organization of object-sensitive regions did not differ in autism. These findings reveal that functional neuroanatomy in autism is altered in category-specific ways, with direct relevance to core sociocommunicative features of the condition. This work underscores the importance of individualized brain mapping in neuroimaging and may shed light on inconsistent findings in previous group-level neuroimaging studies.

Formal thought disorder (FTD) is a core psychosis feature. Disentangling its dimensions requires tasks simple enough for formal modeling yet sensitive enough to capture individual variation across the psychosis spectrum. The semantic verbal fluency task offers precisely this: a structured behavioral trace of semantic memory sampling, amenable to computational analysis using distributed word embeddings. We hypothesized that this sampling process is governed by two dissociable mechanisms mapping onto FTD dimensions: initial retrieval drive (d0), quantifying the motivational resource sustaining production, and semantic search precision (), quantifying how strongly similarity to the preceding word constrains each retrieval step from near-random to highly structured. We hypothesized that reduced d0 would track negative psychosis symptoms and alogia, while degraded would track language disorganization and left inferior longitudinal fasciculus (ILF) fractional anisotropy. We tested these predictions in a primary (N = 120) and an independent replication sample (N = 249) of German-speaking individuals across the psychosis spectrum. Both parameters decreased with greater psychosis severity and, in the primary sample, they dissociated regarding their clinical correlates. d0 correlated negatively with negative symptoms, general psychopathology, and poverty of speech, consistent with a computational signature of alogia. correlated negatively with positive symptoms and cognitive flexibility, and, in individuals with psychosis, positively with left ILF fractional anisotropy. The association between d0 and negative symptoms was replicated in the independent sample. These findings pave the way for mechanistic, automatically derived FTD markers capturing subclinical variation across the psychosis spectrum and mapping onto underlying cognitive and neural processes.

Background: Major depressive disorder (MDD) is characterized by disrupted information flow among brain regions. While effective connectivity (EC) captures these causal interactions, the underlying structural and molecular basis remain unclear. This study aims to investigate direction-specific EC alterations in MDD and their associations with laminar structural covariance (SC) and transcriptional and neurotransmitter profiles. Methods: Resting-state fMRI and structural MRI data were analyzed from the REST-meta-MDD consortium (Discovery, N=1627) and an independent cohort (Validation, N=226). We calculated the unsigned and signed EC using Liang Information Flow and laminar SC based on cortical depth, and compared them between MDD patients and healthy controls. The EC alterations were further associated with molecular profiles integrating gene expression (AHBA) and neurotransmitter receptors (PET/SPECT). Then, Chain mediation analyses were performed to map the hierarchical pathways from molecular basis to EC. Finally, we evaluated the clinical potential of EC in its therapeutic responses to medication and neuromodulation in a longitudinal dataset (N = 16 for medication, N = 11 for neuromodulation). Results: Our analysis revealed no significant changes in the EC of first-episode MDD but observed a hyper-driven cerebellar-cerebral EC pattern in recurrent MDD (RMDD), characterized by a direction-specific excitation-inhibition imbalance featuring enhanced inhibitory cerebellar output alongside a concurrent increase in both inhibitory input and excitatory output within sensorimotor/cognitive regions. These alterations were physically constrained by specific laminar SC patterns, particularly involving the middle cortical lamina. Moreover, the input EC changes in RMDD patients were primarily enriched in biological processes related to the modulation of chemical synaptic transmission, whereas output EC changes were linked to synapse structure regulation. These EC alterations were closely associated with serotonergic, GABAergic, and glutamatergic neurotransmitter systems. Importantly, we identified oligodendrocyte precursor cells (OPCs) as a key cellular mediator bridging microscale molecular features to macroscale connectional alterations in RMDD. These findings were reproducible in the validation dataset. Clinically, medication treatment primarily evoked a pattern of decreased input coupled with increased output, whereas neuromodulation elicited a reciprocal shift characterized by enhanced input and attenuated output. Conclusions: These findings underscore a direction-specific gene-neurotransmitter-cell type-laminar SC-EC pathological model in RMDD. By integrating multi-scale biological mechanisms with clinical phenotypes, this study highlights the potential of directional EC as a biomarker for stratifying refractory depression and guiding precision therapeutics.

Background: Tobacco use is prevalent in clinical high risk for psychosis (CHR-P) population and has widespread negative health consequences, but understanding of its neural substrates is limited. Abnormal default mode network (DMN) may underlie tobacco dependence in CHR-P. We investigated how tobacco use relates to DMN connectivity and how CHR-P status impacts this relationship. Methods: We used baseline substance use and resting-state functional magnetic resonance imaging data from the North American Prodrome Longitudinal Study (NAPLS2; CHR-P: n=211, mean age 19.2, 37.9% female; healthy control: n=132, mean age 19.9, 47.7% female). Voxel-wise connectivity was calculated from the left lateral parietal (LLP) node of the DMN to the rest of the brain. We regressed LLP-brainwide connectivity against tobacco use frequency in the past month to generate a spatial map of how connectivity relates to current tobacco use. Results: Brainwide connectivity analysis identified two clusters in R hippocampus (peak voxel at MNI [+30,-12,-27]) and in L parahippocampus (peak voxel at MNI [-27,-27,-27]), where higher LLP-cluster connectivity was associated with more frequent tobacco use. LLP - R hippocampus connectivity was higher in current tobacco users compared to non-tobacco users (t=-3.5466, df=101.88, p=0.0006), and higher in CHR-P than controls (t=-2.8651, df=279.47, p=0.0049). Among current tobacco users, there was a significant tobacco-by-diagnosis interaction on LLP - R hippocampus connectivity (estimate=0.306, SE=0.149, t=2.051, p=0.045) such that heavier tobacco use predicted hyperconnectivity only in CHR. Conclusions: More frequent tobacco use was associated with higher DMN-hippocampal connectivity in both CHR-P and controls. CHR-P diagnosis enhanced this relationship.

ObjectiveLate adolescence is a critical developmental period that typically precedes psychosis onset, yet the neural correlates of subclinical hallucinatory experiences that may impact psychosis risk are poorly understood. Given evidence from adult psychosis models implicating abnormal "triple network" connectivity among the frontoparietal (FPN), default mode (DMN) and salience/cingulo-opercular (CON) networks, as well as dopaminergic abnormalities, we examined whether hallucinatory experiences in adolescents are associated with altered triple network organization and dopamine-related measures in the midbrain. MethodsWe performed a cross-sectional analysis of 171 community adolescents aged 14-17 who underwent resting-state functional magnetic resonance imaging and neuromelanin-sensitive MRI. Hallucinatory experience severity was measured using the Specific Psychotic Experiences Questionnaire. Resting-state functional connectivity was calculated among a priori DMN, FPN, and CON cortical regions; we examined associations between connectivity, hallucinatory experience severity, within-network connectivity, system segregation, and neuromelanin signal in the ventral tegmental area (VTA). ResultsGreater hallucinatory experience severity was associated with stronger connectivity in a subnetwork composed of CON-DMN and CON-FPN edges. Greater hallucinatory experience severity was also associated with lower global network segregation. VTA neuromelanin signal was not directly associated with hallucinatory experience severity, but greater VTA signal predicted lower connectivity in the hallucination-related subnetwork. Greater VTA neuromelanin signal was also associated with a distinct pattern of stronger connectivity within DMN midline regions. ConclusionsThese findings implicate altered triple network organization in hallucinatory experiences during late adolescence and suggest that dopamine-related midbrain signal may reflect broader developmental variation in cortical network organization rather than symptom severity directly. Plain Language SummaryHallucinatory experiences during adolescence may signal increased risk for later psychotic disorders, but their brain basis is unclear. We studied 171 adolescents aged 14-17 using resting-state fMRI to measure brain network activity and neuromelanin-sensitive MRI to estimate dopamine-related midbrain signal. More severe hallucinatory experiences were linked to abnormal communication among three brain networks often implicated in psychosis. Dopamine-related signal was not directly related to hallucination severity but was associated with developmentally relevant network organization. Overall, this work serves to improve our understanding of the risk factors that may contribute to psychosis conversion in adulthood.