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.

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.

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.

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.

Language transforms subjective internal states into observable behavior, enabling investigation of the neurocognitive dynamics central to psychiatric conditions. Intense emotional and physiological responses to evocative drug-related contexts precipitate craving and drug seeking in substance use disorders, yet sensitive, accessible behavioral markers of the underlying neural dynamics remain elusive. In this fMRI study, we analyzed individuals unconstrained verbal recall of their subjective experiences after watching a drug-themed movie, including inpatients in treatment for heroin use disorder (HUD) and healthy controls, with speech recorded outside the scanner under minimal instructions. The semantic context of these accounts, assessed using transformer-based embeddings, effectively predicted both HUD diagnosis and treatment progression, with earlier treatment characterized by heightened self-reference and drug focus but less socially oriented recall. Semantic similarities between individuals reflected synchronized activation patterns in dorsal attention and visual networks during movie viewing, linking motivated attentional processing to later recall. With treatment, most HUD-biased semantic features shifted toward control-like patterns, while drug focus persisted; the semantic-neural link was attenuated though partially remained. These results suggest that spontaneous speech, collected offline, can provide a sensitive behavioral readout of both treatment effects and neural encoding of a complex, personally relevant, drug-related context. offering a scalable approach for monitoring neurocognitive dynamics in real-world settings.

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

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a range of psychiatric disorders, the neural mechanisms giving rise to this behavioral variation remain elusive. Here, we tested whether the ability to flexibly recruit recurring brain activation patterns (i.e., brain states) may serve as an intermediate phenotype supporting cognitive control in individuals with a spectrum of clinical symptoms. We leveraged machine learning and external validation to explore this question in three independent, transdiagnostic datasets (N>600), including participants with anxiety disorder, schizophrenia, mood disorder, substance use disorders, post-traumatic stress disorder, obsessive-compulsive disorder, and neurodevelopmental disorder. To capture the multifaceted nature of cognitive control, we assessed two of its components, inhibition and shift, using both task-based and questionnaire data. Flexible brain state engagement predicted all cognitive control metrics in previously unseen individuals transdiagnostically, regardless of which dataset was used for model training. Connectome-based predictive modeling also revealed that shared brain networks underpinned flexible brain state engagement in a transdiagnostic manner. Leveraging brain network dynamics, we further observed that moments of more flexible brain state engagement aligned with moments of network connectivity related to better cognitive control within the same individual. This temporal alignment was replicated in all three datasets with heterogeneous samples. Altogether, this study provides robust evidence that flexible engagement of brain state may support both inter- and intra-individual differences in cognitive control across individuals with diverse mental health profiles. Funding sources F31AA032179; R01MH121095

Interoception--the sensing and perception of the internal viscera--is widely cast as a transdiagnostic mechanism linking brain-body interaction to mental illness. Prevailing models propose that altered interoceptive ability reflects a core liability underlying diverse psychiatric symptoms. We tested this hypothesis in a large community sample (N = 547) using psychophysically optimised tasks spanning cardiac and respiratory domains, combined with hierarchical Bayesian modelling and comprehensive symptom profiling. Contrary to this central prediction, objective interoceptive sensitivity, precision, and metacognitive insight were largely unrelated to general symptom burden or specific mental health dimensions. In contrast, self-reported interoceptive sensibility showed moderate associations with symptoms, but semantic similarity analyses suggest these reflect conceptual rather than mechanistic overlap. These findings challenge the prevailing view that objective interoceptive sensitivity is a broad marker of psychopathology, prompting a reconsideration of how we measure interoception in mental health research.

Dissociative identity disorder (DID) remains debated because identity-state phenomena are privately experienced and may be attributed to suggestion, simulation or role enactment. Most neuroimaging studies rely on symptom provocation or traumatic recall, which complicates interpretation and is poorly suited to co-conscious presentations where simultaneous awareness should make state differences hardest to detect. We applied Identity-State Characterization and Analysis (ID-SCAN), a non-traumatic, identity-cued functional magnetic resonance imaging protocol, to a DSM-5-diagnosed woman with persistent co-consciousness between Adult and Adolescent identity-states. One task used identical insect images that evoked opposite preferences across identity-states; the other used trait judgments about self, the other identity-state and a shared intimate other. Analyses combined Bayesian single-case general linear modelling, generalized psychophysiological interaction connectivity and searchlight representational similarity analysis. Identity-instruction cue epochs were pooled across tasks to assess switch direction. The same insect stimuli engaged different valuation-related configurations across identity-states: Adolescent-selective effects centred on striato-thalamic regions, whereas Adult-selective effects extended to amygdala and orbitofrontal/medial prefrontal cortex, with distinct task-evoked coupling. Adult-as-self and Adolescent-as-self occupied separable positions within canonical self-referential regions. Cue-locked activity differed by switch direction across tasks, with larger reconfiguration when switching to Adult (mean between-cluster beta separation 4.30 versus 0.85; permutation p = 0.0001). Cross-task overlap localized a limited shared task-related substrate mainly to posterior visual and dorsal parietal cortex. Even under persistent co-consciousness and without trauma provocation, identity-state expression and switching showed convergent within-person neural signatures. The findings support non-traumatic mechanistic phenotyping of dissociative presentations and motivate cohort and longitudinal studies, including treatment-tracking work.

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.

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.

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

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.

Human social interactions rely on the ability to reflect on ones own and others internal states and traits--a process known as mentalizing. Impaired or altered mentalizing is a hallmark of multiple psychiatric and neurodevelopmental conditions. Yet, replicable and easily testable brain markers of mentalizing have so far been lacking. Here, we apply an interpretable machine learning approach to multiple datasets (total N=390) to train and validate fMRI brain signatures that predict i) mentalizing about the self, ii) mentalizing about another person, and iii) both types of mentalizing. Self-mentalizing and other-mentalizing classifiers had positive weights in anterior/medial and posterior/lateral brain areas, respectively, with accuracy rates of 82% and 77% for out-of-sample prediction. The classifier trained across both types of mentalizing showed 98% predictive accuracy and separated (mental) attributional from factual inferences. Classifier patterns revealed better self/other separation in healthy adults compared to individuals with schizophrenia and with increasing age in adolescence. Together, our findings reveal consistent and separable neural patterns subserving trait-based mentalizing about self and others--present at least from the age of adolescence and functionally altered in severe neuropsychiatric disorders. These mentalizing signatures hold promise as candidate neuromarkers of social-cognitive processes in different contexts and clinical conditions. Author NoteThis work was funded by a Starting Grant from the European Research Council (ERC, 101041087) to LKo, a German Academic Exchange Service (DAAD) doctoral grant and a Network of European Neuroscience Schools (NENS) exchange fellowship to DA, an R01 grant from the U.S. National Institutes of Mental Health (R01MH125414-01) to JAH and DAS, a Junior Leader Fellowship from "la Caixa" Foundation (LCF/BQ/PR22/11920017) to PFC, a Consolidator Grant from the European Research Council (ERC, 648082) to LKr, R37, R01 support from the U.S. National Institutes of Mental Health (R37MH076136 to TDW, MH116026 to TDW and L. Chang [PI], R01EB026549 to TDW and M. Lindquist [MPIs]), an NIMH grant (P50MH094258-01A1) to R. Adolphs, and a CIC Brain and Mental Health Chair from the Neurodis foundation to AT. LvdM acknowledges a European Science Foundation EURYI grant (044035001) that funded her doctoral studies (PI: A. Aleman). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The funders had no role in study design, data analysis, manuscript preparation, or publication decisions.

Cocaine use disorder (CUD) detrimentally impacts personal health, social relationships, and economic opportunity. Here, we assess CUD-associated shifts in brain dynamics using Network Control Theory and examine how they align with previously identified changes in neurological systems and behavioral profiles of people with CUD. The SUDMEX CONN dataset consists of multi-modal MRI, cocaine use metrics, behavioral measures, and demographics of individuals with CUD (N=132, 71 CUD). We identified recurring brain activity states and used NCT to calculate the transition energy (TE) between pairs of states. ANCOVAs examined global and regional TE associations with drug use group (CUD vs controls (NC)), years of CUD, and risk-taking behaviors. We identified potential mechanisms driving the differences by correlating CUD-related regional TE effects with neurotransmitter/receptor systems. People with CUD had significantly lower global TE and default mode, dorsal attention and limbic network TE compared to non-user controls, particularly in regions enriched for noradrenaline and mu opioid receptors. Longer duration of CUD was associated with more decreased global TE, top-down TE, default mode, control and ventral attention network TE, and regional TE enriched for excitatory neurotransmitters and receptors. People with CUD needed to expend more global and top-down TE to perform better on a risk-taking task (the Iowa Gambling task), an effect which was not found in NCs. Our analysis of whole-brain activity dynamics provides a link between the effects of upstream glutamatergic excitotoxicity and/or opioid receptor dysfunction, and downstream weakening of inhibitory control that is central to CUD.

Psychotic-like experiences (PLEs) include a range of sub-threshold symptoms that resemble aspects of psychosis but do not necessarily indicate the presence of psychiatric illness. These experiences are highly prevalent in youth and are associated with developmental disruptions across social, academic, and emotional domains. While not all youth who report PLEs develop psychosis, many develop other psychiatric illnesses during adolescence and adulthood. As such, PLEs are theorized to represent early markers of poor mental health. Here, we characterized the similarities and differences in the neurobiological underpinnings of childhood PLEs across the sexes using a large sample from the ABCD Study (n=5,260), revealing sex-specific associations between functional networks connectivity and PLEs. We find that although the networks associated with PLEs overlap to some extent across the sexes, there are also crucial differences. In females, PLEs are associated with dispersed cortical and non-cortical connections, whereas in males, they are primarily associated with functional connections within limbic, temporal parietal, somato/motor, and visual networks. These results suggest that early transdiagnostic markers of psychopathology may be distinct across the sexes, further emphasizing the need to consider sex in psychiatric research as well as clinical practice.

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

Complex clinical presentations are common in youth seeking mental health treatment, complicating attempts to identify specific biological underpinnings to guide precision psychiatry. We defined four classes of such youth based on their symptom profiles and identified unique patterns of amygdala functional connectivity in each class. Subjects were 215 youth who varied along major symptom dimensions commonly associated with pediatric affective psychopathology: depression, irritability, anxiety, and attention-deficit/hyperactivity (ADHD). We used latent profile analysis to identify classes of symptom patterns. Functional MRI data were obtained while subjects completed a gender identification task of face-emotions that varied in emotion type and intensity. We used generalized psychophysiological interaction analysis to examine associations between the probability of being in each symptom class and amygdala functional connectivity. The likelihood of being in the class with high parent-reported irritability and ADHD symptoms was associated with amygdala connectivity to the insula and superior temporal gyrus while processing high-intensity angry and fearful faces; to the precuneus while processing intensity across emotions; and to the ventrolateral prefrontal cortex across all task conditions. The likelihood of being in the class with high anxious and depressive symptoms was negatively associated with amygdala-thalamic connectivity across task conditions. This is the first study to identify distinct associations between symptom profile classes and amygdala connectivity in a transdiagnostic sample of youth. These neural correlates provide external validity to latent classes derived from symptom clusters. This is an essential first step toward identifying the biological basis of common transdiagnostic symptom presentations in youth.

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.

BackgroundNormative modeling of brain development has gained traction for quantifying individual deviations in maturation. The brain age gap (BAG), the difference between predicted age from MRI features and chronological age, offers a potential individualized normative metric of neurodevelopment. However, consistent patterns across psychiatric disorders remain elusive, and no studies have examined whether BAG can predict developmental trajectories within an inclusive continuous model of youths cognition and behavior. MethodsUsing longitudinal data from the Adolescent Brain Cognitive Development Study (ages 9-15, n=9,074), we built 8 region-specific brain age models using volumes, thicknesses, and surface areas of parcels from the Brainnetome adolescent atlas. We derived psychiatric diagnoses from a parental questionnaire. Multivariate linear regression was used to assess case-control differences and cross-sectional continuous cognitive/behavioral profiles. We modeled cognitive/behavioral trajectories using a multivariate joint latent-class mixed model and assessed the relationship with BAG values using multinomial logistic regression. ResultsChildren with ADHD showed delayed maturation across multiple regions (Cohens d: - 0.12 to -0.08), while subcortical BAG emerged as a transdiagnostic indicator of delayed development (d: -0.07, pfdr = 0.024). Accelerated maturation characterized the high cognition and low symptom profile, while the inverse was found for the low cognition profile. Three developmental trajectories were identified: stable, towards externalizing behaviors, or internalizing behaviors. Widespread accelerated maturation predicted evolution towards internalizing behaviors but was protective against the externalizing trajectory. ConclusionsIntegrating BAG with continuous cognitive and behavioral profiles yielded a plausible framework for early identification of atypical trajectories, potentially contributing to personalized medicine in psychiatry.