Nature Mental Health

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.

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.

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.

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

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

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.

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

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.

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

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.

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

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

Self-referential processing is a fundamental cognitive function, and abnormalities in its neural implementation have been reported across a range of psychiatric disorders, leading to the proposal that such alterations may constitute a transdiagnostic neurobiological feature. Yet claiming transdiagnostic requires rigorous evidence. Here, we examined the evidence for such a hypothesis by conducting a systematic review and coordinate-based meta-analysis of psychiatric neuroimaging studies that employed self-referential tasks. The systematic review identified 36 neuroimaging studies across 9 broad categories of psychiatric disorders, suggesting that the neural aberrancy of self-referential processing is indeed of great interest across different diagnosis. Of these, 27 studies were eligible for the ALE meta-analysis. The ALE results revealed hypoactivation of the right precuneus in psychiatric groups relative to health controls, alongside hyperactivation of the right triangular part of the inferior frontal gyrus (IFGtri) during self-referential processing in psychiatric groups. Notably the precuneus and IFGtri are core nodes of the default mode network and the frontal-parietal control network, respectively, suggesting that aberrant self-referential processing across psychiatric disorders may be characterized by disrupted default mode network engagement accompanied by compensatory or maladaptive recruitment of control-related frontal regions. Together, our findings revealed a strong research interest in neural aberrancy of self-referential processing as a transdiagnostic feature. However, available evidence only provided preliminary evidence for such statement. To move forward, the field needs coordinated efforts to systematically accumulate data and collecting new datasets.

Internet gaming disorder (IGD) presents a significant public health challenge, yet its complex biopsychosocial mechanisms and dynamic risk trajectories remain poorly understood due to a scarcity of comprehensive longitudinal and multimodal cohorts. To address this critical gap, we established the Chinese College Student Gamers Cohort (CCSGC), a prospective, multimodal longitudinal study of 793 first-year undergraduates primarily playing Honor of Kings from 2022 Sept. The CCSGC integrates semi-annual psychosocial questionnaires, annual neuroimaging (EEG/fMRI), and biospecimen collection over multiple years. Baseline data revealed individuals with IGD (n=211) exhibited significantly higher gaming craving, psychological distress (depression, anxiety), impulsivity, and maladaptive motivational features compared to non-IGD gamers (regular players (RP) n=400; casual players (CP) n=182). Longitudinal analyses across four waves indicated bidirectional temporal associations between IGD severity and mental symptoms, and a stabilization of IGD incidence after an initial decrease. Furthermore, specific neurophysiological (e.g., N400 amplitude to game cues) and neuroimaging (e.g., superior parietal activation) markers were identified that correlated with IGD severity and predicted one-year outcomes in gaming disorder or social functioning. The CCSGC provides an invaluable resource for dissecting the heterogeneity, comorbidity, and intricate biopsychosocial mechanisms of IGD, holding significant potential to advance risk prediction, early identification, and targeted intervention strategies.

Intermittent explosive disorder (IED) is associated with impulsive aggression in ambiguous social contexts. Prior neuroimaging studies have treated IED as a homogenous group, but identical social situations may elicit divergent responses across IED individuals. Here, we test the hypothesis that IED is characterized by idiosyncratic neural responses to social cues during naturalistic social-emotional processing. IED individuals and healthy controls completed a validated paradigm where they were presented with video vignettes of interpersonal interactions while undergoing fMRI. We computed the intersubject correlation (ISC) in neural time courses between pairs of participants to quantify neural similarity, and assessed whether similarity differed between Healthy-Healthy and IED-IED dyads using Bayesian multilevel models, controlling for self-reported emotional responses and intention attributions for each vignette. Healthy-Healthy dyads showed significantly higher ISC than IED-IED dyads, indicating that neural responses to the videos were similar among healthy participants, but idiosyncratic in IED individuals. These effects were observed in regions in the default mode and salience networks, including the precuneus, medial prefrontal cortex, superior temporal sulcus, insula, and dorsal anterior cingulate cortex. Individuals with IED exhibited idiosyncratic neural responses during naturalistic social-emotional processing, even after accounting for differences in emotional reaction and intention attribution. This neural idiosyncrasy may reflect atypical integration of social cues, giving rise to maladaptive interpretations and impulsive aggression. Assessing neural synchrony during ecologically valid paradigms offers a promising tool for identifying neural markers of interpersonal dysfunction and informing targeted interventions.

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.