Biological Psychiatry

Altered white matter microstructure, commonly indexed by fractional anisotropy (FA), has been reported in individuals at clinical high-risk of psychosis (CHR-P) across multiple white matter tracts. However, the relationship between these alterations and later transition to psychosis remains unclear. To address this gap, we investigated whether baseline and longitudinally updated FA measurements were associated with risk of transition to psychosis in a large cohort of individuals at CHR-P. We analysed diffusion tensor imaging data from N = 205 participants at CHR-P, 23 of whom developed psychosis within 3 years of follow-up. Data were collected at baseline, 6 months, and 12 months. Associations between global and tract-specific FA and transition risk were examined using Cox proportional hazards models of baseline data and longitudinally updated data. Point estimates were directionally consistent, with hazard ratios <1 for FA across all white matter tracts in both baseline and longitudinal models. Associations reached nominal significance in the hippocampal cingulum, anterior thalamic radiation, and forceps minor, but none of these remained significant after correcting for multiple comparisons (pcorr > .05). The directionally consistent estimates are compatible with small, distributed effects across multiple white matter pathways being associated with transition to psychosis, although this requires replication. Longitudinally updated FA measurements did not yield stronger associations than were available from baseline imaging data alone. Studies at an even larger scale are needed to establish whether these associations are robust and clinically meaningful.

Background Cocaine use disorder (CUD) is estimated to be 50% heritable, yet few genetic variants have been robustly associated with risk. During January 2021 to June 2024, cocaine was involved in 30% of U.S. overdose deaths. Identifying genetic contributors to CUD could inform diagnostics and treatments. The missing heritability may stem from small sample sizes, models that inadequately account for small-effect common variants, and the exclusion of large-effect structural variants like copy number changes. Epistasis likely contributes but remains challenging to detect genome-wide. Results We focused on epistasis and haplotypes rather than individual SNPs by applying the custom correlation coefficient in BlocBuster to construct allele networks from eQTL SNPs. Testing these regulatory haplotype networks for association with cocaine use in 7,826 individuals with opioid use disorder identified 12 significant associations across two ancestries. For one haplotype, raw genotyping array intensity data revealed a copy number increase at the CYP2D6 locus in individuals of European ancestry. In a subcohort of individuals who co-use cocaine and heroin (i.e., speedball), two haplotypes were also significantly associated with HIV infection. One of these involved GPR75, whose ligand, CCL5 (RANTES), is shared with CCR5, a coreceptor for HIV entry. Conclusions Our findings, integrated with long-read human pan-genomes, support a model in which non-functional copies of CYP2D6 disrupt dopaminergic, glutamatergic, and kynurenine pathways in the brain, contributing to cocaine use severity. The association of additional haplotypes with HIV status in speedball users highlights the genetic contribution to comorbid risk in high-exposure populations.

Early-life adversity and abnormal myelination are implicated in psychiatric disorders, but how developmental myelination influences adult social cognition remains unclear. Here, we tested whether disrupting developmental myelination in the medial prefrontal cortex (mPFC) impairs adult social behavior. Using complementary genetic strategies in male mice, we suppressed Myelin Regulatory Factor (Myrf)-dependent myelination locally in the juvenile mPFC or broadly in oligodendrocyte-lineage cells. Both approaches selectively impaired adult social novelty preference while sparing sociability, locomotion, and affective behaviors. Focal prefrontal dysmyelination also reduced social dominance. Spatial transcriptomics revealed coupling between myelin-related genes and parvalbumin (PV) interneuron markers in the healthy mPFC, which was disrupted by developmental hypomyelination. Consistent with this transcriptional alteration, PV interneurons showed reduced high-frequency firing despite preserved intrinsic excitability. In the human prefrontal cortex, MBP-PV expression was correlated in controls but not in individuals with schizophrenia. These findings indicate that developmental prefrontal myelination contributes to PV interneuron maturation and suggest that disrupted oligodendrocyte-interneuron coordination may contribute to impaired social recognition.

BACKGROUND: Clinical high risk (CHR) for psychosis is characterized by substantial heterogeneity in symptoms, functioning, and outcomes. Although distributed resting-state functional connectivity (RSFC) abnormalities have been reported, it remains unclear whether this heterogeneity can be organized into consistent multivariate brain-symptom dimensions and clinically meaningful subgroups. METHODS: We analyzed RSFC and 34 clinical variables from 701 CHR participants in the Accelerating Medicines Partnership Schizophrenia Program (AMP SCZ). Partial least squares analysis identified latent brain-symptom dimensions, which were further examined using transcriptomic annotation, independent psychosocial and cognitive measures, hierarchical clustering, and Month 2 follow-up data. RESULTS: Two significant brain-symptom dimensions emerged. The first reflected general psychopathology and was associated with somatomotor-salience and higher-order connectivity, with enrichment for genes related to neuronal development. The second, capturing symptom differentiation, spanned negative/internalizing features with psychobehavioral overactivation and involved cerebellar-thalamic-somatomotor circuitry, with enrichment for genes related to synaptic signaling. These dimensions showed dissociable associations with broader psychosocial and cognitive measures, and their loading patterns were substantially reproducible at Month 2. Clustering based on these dimensions identified two biotypes with distinct clinical and connectivity profiles that remained broadly similar at Month 2 despite divergent short-term RSFC changes. CONCLUSIONS: These findings support complementary dimensional and categorical representations of CHR heterogeneity that link clinical variation with distributed brain connectivity and molecular signatures, while demonstrating short-term reproducibility of the identified dimensions and divergent RSFC changes across biotypes. This multilevel framework may help characterize biologically meaningful variation in psychosis risk.

Postpartum depression (PPD) impacts nearly 20% of mothers and a much larger percentage (up to 75%) suffer from postpartum blues. Despite this high incidence, we still have an incomplete understanding of the underlying neurobiology of PPD. Deficits in neurosteroid signaling has been implicated in the vulnerability to postpartum behavioral deficits and mice with reduced expression of {delta} subunit-containing GABAA receptors (GABAARs) (Gabrd -/- or Gabrd +/-), the predominant site of action for 5-reduced neurosteroids, exhibit behavioral deficits restricted to the postpartum period. Allopregnanolone and allopregnanolone analogs (SGE-516) can improve postpartum behavioral outcomes and their ability to modulate oscillations in the basolateral amygdala (BLA) involved in emotional processing has been proposed to contribute to their antidepressant effects. Here we demonstrate that the behavioral differences in postpartum wild type, Gabrd+/-, and SGE-516-treated Gabrd+/- dams is largely driven by theta oscillations in the BLA. Previous studies have linked theta oscillations in the BLA to altered behavioral responding in response to stress or threat. Interestingly, here we demonstrate that Gabrd+/- dams exhibit a failure to properly respond to their own pup calls, with reduced theta oscillations that correlates with deficits in maternal approach behavior both of which are restored with SGE-516 treatment. Further, unbiased MoSeq analysis identified altered behavioral syllable expression in Gabrd+/- dams in response to their pup calls compared to wild type dams that are restored with SGE-516 treatment. These findings demonstrate network and behavioral signatures of postpartum behavioral deficits and characterize the effects associated with neurosteroid-based treatment response.

Background: Schizophrenia is increasingly viewed as a disorder of multi-regional brain networks, but molecular and pathophysiological hypotheses are often studied at the level of single brain regions or neurotransmitter systems. A multi-regional, multi-neurotransmitter analysis is needed to clarify how molecular abnormalities relate to circuit-level hypotheses of the disorder. Methods: Transcriptomic data from 2,209 post-mortem brain samples spanning five brain regions in 1,037 individuals were integrated. Directed connectivity strengths between regions were estimated using co-expression between presynaptic and postsynaptic neurotransmission-related genes. These estimates constitute an "interregional molecular connectome". Macaque transcriptomic data were used to benchmark the estimation pipeline against reference macaque connectomes. Connectivity differences between healthy controls and patients with schizophrenia were calculated, and a directional consistency filter was applied to identify candidate dysregulations. An LLM-augmented literature screening framework was used to assess the level of literature support for our candidates. Results: Our estimation pipeline showed concordance with reference connectomes, outperformed whole-transcriptome and random-gene co-expression measures, and remained stable when applied to macaque data. A total of 10 connections were marked as candidate dysregulations in schizophrenia at a nominal permutation threshold. Three of the candidates align with established anatomical connectivity, and another three with anatomical connections through relay regions. These candidates were characterized by bidirectional abnormalities in the cortico-striatal circuitry. The remaining four candidates had no well-established monosynaptic connection between two regions. These candidates were interpreted as co-regulated co-expression coordination changes. Literature screening quantified the number of articles discussing each candidate and how many of the articles directly support or refute each candidate. Our candidates collectively highlight the role of the cortico-striato-thalamo-cortical circuit in the pathophysiology of schizophrenia. Conclusions: Interregional co-expression of neurotransmission-related genes carries more anatomical information than whole-transcriptome co-expression. Applied to schizophrenia, our analysis recapitulates known hypotheses and also proposes hypothesis-generating data-driven findings for experimental follow-up. This approach complements conventional region-wise analyses. Our approach could support a unified, multi-regional, and multi-neurotransmitter perspective of schizophrenia and point to candidate molecular mechanisms for further studies.

The hippocampus has long been implicated in depression, yet the underlying mechanisms remain incompletely understood. Leveraging precision functional mapping in densely sampled individuals and large cross-sectional cohorts, and validating key results in direct intracranial EEG recordings from the human brain, we show that hippocampal connectivity alternates between two discrete and opposing network states on the order of minutes: one dominated by default mode network (DMN) coupling and another by sensorimotor system (SM) coupling. This dynamic switching is a fundamental property of hippocampal function in both healthy brains and in major depressive disorder (MDD). In MDD, this equilibrium is disrupted, such that the DMN-dominant state is overrepresented. In densely sampled individuals, hippocampus-DMN coupling tracks symptom fluctuations over time and normalizes after electroconvulsive therapy (ECT), which drives switches in hippocampal connectivity away from the DMN-dominant state and toward the SM-dominant state, an effect that was closely linked to a positive treatment response. Direct intracranial recordings in patients with MDD identified analogous fluctuations in hippocampal-DMN theta synchrony, which tracks symptom fluctuations over hours and days. Together, these findings identify a dynamic cortico-hippocampal mechanism underlying depression and indicate that successful neuromodulation treatments such as ECT act in part by restoring a dynamic equilibrium in hippocampal network connectivity.

Opioid withdrawal drives continued opioid use in opioid use disorder (OUD), which affected an estimated 4 million Americans in 2024. However, only one non-opioid medication is FDA-approved for withdrawal treatment, and there is great need for more diverse treatment options. Mice are powerful models to study opioid withdrawal because they allow for controlled drug and environmental exposure, express OUD-relevant phenotypes, and provide access to withdrawal-relevant tissue at exact time points. However, mouse models are hindered by their over reliance on a single genetic background (C57BL/6J). Using a single background risks limiting the translatability of preclinical studies as they are projected into genetically diverse human populations. Inferring withdrawal intensity in mice typically relies on single behaviors or indexes of behaviors, and applying these approaches across mouse genetic diversity faces a critical challenge: differing withdrawal responses could either encode differences in withdrawal severity, or differences in withdrawal-evoked behaviors independent of severity. To overcome this limitation, a more comprehensive assessment of opioid withdrawal behaviors across mouse genetic diversity is needed. Here we leverage computer vision and machine learning to automate the simultaneous quantification of many mouse behaviors during opioid withdrawal in eight diverse strains of mice. We reveal that all eight strains exhibit withdrawal-evoked behaviors, and these behaviors are sufficient to identify the withdrawal state of the mouse. However, the effect of withdrawal on individual behaviors or indexes of behaviors is not uniform across genetic diversity, and extending methods tuned in one strain risks misclassifying withdrawal in others. Finally our approach was able to distinguish graded withdrawal intensity, as defined by opioid exposurewithin3of4strainstested. We therefore conclude that opioid withdrawal behavior is profoundly shaped by genetic background, but withdrawal severity can nonetheless be established within a background. Combining automated computer-vision phenotyping with genotype-aware modeling provides a scalable path toward genetically informed withdrawal assessment in diverse mouse populations, with the ultimate goal of identifying the genetic and biological networks that modulate opioid withdrawal and may represent new targets for treating opioid use disorder.

Locomotor response to a novel environment is a heritable, addiction-relevant behavioral phenotype in rodents. We characterized its genetic basis in 9,341 male and female heterogeneous stock (HS) rats from eight studies. Locomotor activity was heritable across studies, while pairwise genetic correlations were positive but variable, indicating shared yet nonidentical genetic influences. Given nonuniform genetic architecture, we compared three complementary approaches to locus discovery that analyzed SNP effects 1) on a pooled locomotor phenotype (MEGA), 2) across studies under a fixed-effect model (METAL), or 3) on a shared genetic factor (Genomic SEM). To ensure comparability, all three locus-discovery approaches were applied to a common subset of seven studies. Together, these approaches identified 38 loci, with both convergence and method-specific discovery across analyses. Genomic SEM identified 31 loci, compared to 8 for MEGA and 13 for METAL. To interpret the loci, we integrated brain molecular QTLs, predicted coding and splice consequences, and open-field locomotor phenotypes from knockouts of orthologous mouse genes. Our results highlighted established neurobehavioral candidates spanning locomotor and reward-related biology (Negr1 and Lmo4), serotonergic signaling (Htr1b), and GABAergic signaling (Gabrg2), as well as relatively novel genes including Tcerg1l, Pcdh10, and Tmem132e. Our results illustrate how Genomic SEM can integrate genetically correlated yet heterogeneous behavioral measures and enable discovery of loci underlying their shared genetic architecture, highlighting its utility for complex-trait studies in rodents.

Background: Auditory verbal hallucinations (AVH) are among the most disabling symptoms of schizophrenia, yet the network-level mechanisms linking auditory dysfunction to hallucination severity remain poorly characterized, limiting targeted therapeutic development. Methods: Using resting-state fMRI and high-resolution surface-based parcellation (HCP-MMP), we examined auditory-language functional connectivity and mesoscale network organization in 56 patients with schizophrenia and 22 healthy controls (primary dataset, N=78). Pairwise auditory-language connectivity associations with hallucination severity (PANSS P3) were examined alongside auditory network community structure, assortativity, hub organization, and routing efficiency. To further characterize these parcel-level findings at the vertex level, we applied a region-growing approach to identify auditory-frontal connectivity patterns maximally associated with severity. In silico perturbation analyses tested the causal role of belt cortex connectivity. Key findings were independently replicated in 93 patients from the Human Connectome Project for Early Psychosis (HCP-EP), for a combined total of 171 participants. Results: Auditory-language connectivity disruptions were concentrated in belt cortex and specifically implicated ventral what-stream connections, with a vertex-wise analysis converging on the same anatomy. Auditory network segregation scaled with hallucination severity, with belt cortex emerging as the primary integrative hub governing auditory-to-language routing efficiency. Perturbation analyses showed that strengthening earlier hierarchy auditory regions reduced network segregation more than strengthening later-hierarchy regions. Reduced belt cortex coupling and elevated network segregation replicated independently in the HCP-EP sample. Conclusions: Auditory network disorganization scales with hallucination severity and replicates across independent samples, with belt cortex as the critical locus of connectivity disruption and network integration failure, identifying it as a candidate neuromodulatory target.

Biologically informed polygenic scores (PGSs) may help interpret genetic associations with treatment outcomes. Here, we computed neurotransmitter-based PGSs (NeuroPGSs) for patients with major depressive disorder (MDD) and assessed their association with SSRI treatment outcomes. By targeting genetic variants involved in key neurotransmission pathways, we developed PGSs for serotonergic (5228), gamma-aminobutyric acid (GABA, 4103), dopaminergic (7061), and glutamatergic (5690) variants. We leveraged data from two cohorts of patients who received SSRIs for at least 4 weeks: the International SSRI Pharmacogenomics Consortium (ISPC) and the Pharmacogenomics Research Network Antidepressants Medication Pharmacogenomics Study (PGRN-AMPS). Treatment response was defined as [&ge;]50% reduction from baseline depression symptoms, measured using 17-item Hamilton Rating Scale for Depression (HRSD-17) in ISPC and 16-item Quick Inventory of Depressive Symptomatology (QIDS-C16) in PGRN-AMPS. Remission was defined as HRSD-17 [&le;]7 (ISPC) or QIDS-C16[&le;]5 (PGRN-AMPS). The association between each NeuroPGS and antidepressant response or remission was evaluated using logistic regression models, adjusting for age, sex and five genetic principal components, with statistical significance determined at Benjamini Hochberg-adjusted p<0.05. The GABAergic-based PGS was associated with SSRI response (aOR per SD = 1.45, 95%CI: 1.14-1.85). Patients with the highest GABAergic PGS (10th decile) had 2.59-fold higher odds of response (95%CI: 1.46-4.60) compared to those with the lowest decile (1st decile). These exploratory findings support further evaluation of NeuroPGSs; independent replication and assessment of incremental predictive value are needed before clinical application. Keywords: Polygenic score; Neurotransmitters; GABAergic; depression; SSRIs; pharmacogenomics

Circadian disruption is a transdiagnostic feature of psychiatric illness, particularly in bipolar disorder (BD), where evening chronotype is linked to greater symptom severity. The suprachiasmatic nucleus (SCN) times pineal gland (PG) melatonin synthesis through a well-described feedforward pathway. Moreover, melatonin can also phase-shift the SCN-pacemaker, consistent with a functional feedback limb. Directed effective connectivity between the SCN-containing hypothalamus and the PG has not been estimated in human neuroimaging, and it is unknown whether that directed coupling varies by psychiatric diagnosis or chronotype. We applied cross-spectral dynamic causal modeling (DCM) to resting-state fMRI data in 431 UK Biobank (healthy controls [HC, N=136], BD [N=78], major depressive disorder [MDD, N=154] and psychotic disorders [PY, N=63]). Random-effects Bayesian model selection compared a fully connected model with reduced archiature and, separately, feedforward-only (SCN[-&gt;]PG) vs feedback-only (PG[-&gt;]SCN) models. A per-subject log Bayes factor difference (dF) indexed directionality preference and was tested against diagnosis, chronotype and their interaction using ANCOVA. The fully connected model was strongly preferred (posterior probability=97.8%, protected exceedance probability [PXP]=1.00). Feedforward architecture dominated overall (70% vs 30%, PXP=1.00). Diagnosis did not predict dF (p=.403), but chronotype did (p=.036), and there was a significant diagnosis-by-chronotype interaction (p<.001). Feedforward dominance was strongest in evening chronotypes (p<.001). Model evidence favored a fully connected architecture with overall feedforward dominance. That preference varied by chronotype and, qualified by the interaction, was strongest in BD evening chronotypes. The pattern supports BD-evening chronotype as a high-risk subgroup and argues for routine chronotype assessment in BD care.

Fragile X syndrome (FXS) features sensory, social, and cognitive dysfunction, but how fragile X messenger ribonucleoprotein 1 (FMR1) deficiency disrupts sensory corticostriatal circuitry and whether distinct FMR1 perturbations converge on shared cellular mechanisms remain incompletely defined. We integrated diffusion magnetic resonance imaging (MRI) in individuals with FXS and Fmr1-knockout (KO) mice with projection-defined calcium recording, bidirectional optogenetic manipulation, primary somatosensory cortex (S1)-targeted expression of three patient-associated FMR1 missense variants, regional transcriptomics, and optical manipulation of mitochondrial energetic state. Individuals with FXS exhibited disrupted sensory corticostriatal structural connectivity, with mouse analyses further localizing abnormalities to S1-striatal pathways. Fmr1 deficiency impaired recruitment of the S1-dorsomedial striatum (DMS) pathway during social interaction, and projection-specific manipulation demonstrated that S1-DMS dysfunction contributes to social and cognitive deficits. Three patient-associated FMR1 variants, I234T, E329G, and R586Q, converged on social behavioral deficits, which were improved by S1-DMS activation. Transcriptomic analyses revealed convergent oxidative phosphorylation (OXPHOS)-related mitochondrial remodeling in S1 and DMS across Fmr1 deficiency and variant models. Systemic treatment with the mitochondria-targeted antioxidant MitoQ attenuated novel-location-recognition, social-interaction, and social-novelty deficits in Fmr1-KO mice. Moreover, mt-EcGAPR-based Mitochondrial OptoEnergetic Activation (Mito-OptoEA) enhanced mitochondrial energetic state in S1 and improved novel-location recognition and sociability in both Fmr1-KO and I234T models. Together, these findings define a sensory corticostriatal circuit-metabolic framework for FXS in which S1-DMS circuit dysfunction and convergent mitochondrial energetic abnormalities contribute to FMR1-related social and cognitive phenotypes.

Major depressive disorder (MDD) is biologically heterogeneous, but how this variability is expressed in the temporal organization of brain activity remains unclear. Using resting-state functional MRI from 2,225 participants, we applied hidden Markov modeling to identify recurrent brain states and quantify their temporal organization. Five states formed a shared repertoire across individuals with MDD and healthy controls, while MDD-related alterations were distributed across states, functional systems, and temporal properties. Within MDD, unsupervised clustering of state occupancy, persistence, and switching delineated two prototype neurodynamic architectures characterized by preferential deployment of ACS/VSS or MAS/DMS states within a preserved state repertoire and broadly similar transition backbone. The architectures showed different within-group brain-symptom associations and symptom-network patterns. Connectome transcriptome analyses organized architecture-state imaging patterns into three transcriptomic state families shared across architectures, and contrasting group-level deployment patterns were recovered in an independent MDD cohort. These findings suggest that MDD heterogeneity is expressed not only through spatially distributed neural alterations, but also through differences in how a common repertoire of recurrent brain states is organized over time.

Dysfunctions in reward circuits, including projections from the orbitofrontal cortex (OFC) to the nucleus accumbens (NAcc), have been implicated in psychopathology. The OFC is functionally divided into the medial OFC (mOFC), which processes rewards, and the lateral OFC (lOFC), which responds to non-reward or punishment stimuli. We investigated the structural connectivity of the lOFC-NAcc and mOFC-NAcc pathways and their associations with psychopathological traits using tractography-defined ROI analysis. Diffusion-weighted imaging data from 165 participants (aged 22 - 35, 53% male) in the Human Connectome Project (HCP) were analysed using spherical deconvolution tractography. MegaTrack, a novel tool, enabled tract segmentation via a single manual dissection mapped back to native space. Virtual dissection identified four bilateral pathways connecting the lOFC and mOFC to the NAcc, from which fractional anisotropy (FA) was extracted, and an lOFC/mOFC ratio was computed. Psychopathological traits were measured using HCP Achenbach Adult Self-Report scores. The lOFC-NAcc pathway showed positive associations with psychopathology and the mOFC-NAcc pathway negative associations. The lOFC/mOFC ratio emerged as a stronger predictor than individual pathways, with significant associations for depression, somatic problems, anti-social personality traits, and inattention. Effects were lateralized to the left hemisphere. These findings show that the lOFC-NAcc and mOFC-NAcc pathways can be structurally separated with tractography and display opposing correlations with psychopathological traits in a healthy population. Their ratio may index reward-processing dysfunction, and future research should test whether it is sensitive to dysfunction in clinical populations.

BackgroundSchizophrenia liability reflects both the aggregate effects of many common genetic variants and rare structural variation at recurrent neurodevelopmental copy-number variant (ND-CNV) loci. Although both components of genetic liability have been associated with schizophrenia and related phenotypes, it remains unclear whether they exhibit similar neuropsychiatric association profiles or show evidence of interaction when examined jointly. MethodsWe analyzed 71,992 participants from the All of Us Research Program Controlled Tier version 8 dataset, including 2,115 participants carrying structural variants overlapping curated recurrent ND-CNV loci. We integrated binary ND-CNV carrier status, an ancestry-adjusted and standardized reduced schizophrenia polygenic risk score (PRS), and longitudinal electronic health record-derived psychiatric and neurodevelopmental phenotypes. Logistic regression models evaluated pooled, deletion-overlapping, and duplication-overlapping carrier associations, schizophrenia PRS effects, and PRS x ND-CNV interactions. Dimensional analyses, confirmatory factor analysis (CFA), and structural equation modeling (SEM) provided complementary summaries of phenotype covariance and assessed the robustness of primary associations across alternative representations of the phenotype space. ResultsSchizophrenia PRS and ND-CNV carrier status showed distributed association profiles extending beyond schizophrenia diagnosis across psychosis-spectrum, neurodevelopmental, cognitive, and internalizing phenotypes. ND-CNV carriers had higher estimated odds of schizophrenia diagnosis than non-carriers (OR = 1.49, 95% CI 1.09-2.03), while each 1-SD increase in schizophrenia PRS was associated with higher odds of a broad psychosis-spectrum phenotype (OR per SD = 1.07, 95% CI 1.03-1.10). Deletion-overlapping carriers exhibited generally larger and more consistently positive association estimates than duplication-overlapping carriers. Psychosis-spectrum prevalence increased from 5.4% in the lowest PRS decile to 6.7% in the highest, while ND-CNV carriers maintained consistently higher prevalence than non-carriers across the PRS distribution. PRS x ND-CNV interaction estimates remained near the null, providing little evidence of widespread multiplicative interaction. Among tested covariance structures, bifactor models showed the lowest residual misfit and were therefore carried forward for SEM analyses. ConclusionsSchizophrenia-associated polygenic liability and structural variation overlapping recurrent ND-CNV loci exhibit distributed neuropsychiatric association profiles with limited evidence of widespread multiplicative interaction. Their joint evaluation in the large, ancestrally diverse All of Us cohort demonstrates that distinct forms of genomic liability can be examined within a shared, real-world phenotype space extending beyond schizophrenia diagnosis alone.

The potential role of the cerebellum in the pathophysiology of schizophrenia (SCZ) has received relatively insufficient attention. Prior studies characterized cerebellar abnormalities mainly at the group level, without fully considering the heterogeneity of the disease. Here, we applied a machine learning approach (Subtype and Stage Inference, SuStaIn) to cross-sectional 3D volumetric MRIs of the cerebellum, including 1,588 individuals with SCZ (638 females; mean age: 31.9 +/- 12.0 years) and 2,341 healthy controls (HC) (1,041 females; mean age: 33.3 +/- 13.6 years), from 17 sites worldwide. SuStaIn identified three distinct spatiotemporal trajectories in cerebellar gray matter volume (GMV) reduction, respectively originating in lobule X (subtype 1), lobule III (subtype 2) and lobule VIIb (subtype 3), with subtypes 1 and 3 corresponding to the posterior lobe and subtype 2 to the anterior lobe. These cross-sectionally inferred trajectories were replicated in two independent samples of 1,334 and 530 patients, respectively. Multimodal analyses using neuroimaging, transcriptomic and behavioral data revealed subtype-specific biological characteristics in brain morphological patterns, cerebellar-cortical connectivity, gene expression and clinical symptoms. Specifically, subtype-related genes were enriched in metabolism-related processes and immunity-related processes, respectively, in subtype 1 and subtype 2; subtype 3 showed more severe brain abnormalities and worse cognitive symptoms. Treatment data from 381 patients, with up to 12 months of follow-up, revealed poorer response to antipsychotic medications (APM) in subtype 2 and subtype 3, but better response to transcranial magnetic stimulation (TMS) in subtype 1, which also had worse emotion-related symptoms. Together, our findings offer a comprehensive characterization of the heterogeneity of cerebellar pathophysiological progresses in SCZ, which may help in developing future clinical stratification and targeted intervention strategies.

Substance use and psychiatric disorders frequently co-occur, but establishing causality of their relationships is difficult given the multifactorial nature of these traits. To assess these relationships, we applied two genetically informed methods using the largest available European-ancestry genome-wide association studies of six substance use behaviors and twelve psychiatric and personality phenotypes. Using Genomic Structural Equation Modelling, we modelled the latent genetic architecture of the psychiatric and personality traits and examined these factors' correlations with substance use traits. A structure with three factors fit the data best: F1 (PTSD, MDD, GAD, ADHD, agreeableness), F2 (BD, SCZ, OCD, openness), and F3 (OCD, GAD, extraversion, neuroticism), which showed differential correlations with substance use. Smoking correlated significantly which each factor, however, strongest with F1, alcohol use showed weak but significant correlations with F1 and F2, and cannabis use showed moderate correlations with F1 and F2, but strongest with F2. Using Mendelian Randomization, we examined bidirectional causal relationships. We found strong evidence that smoking continuation was causally associated with increased bipolar disorder risk, cigarettes per day with increased schizophrenia, and smoking initiation with increased extraversion and decreased agreeableness. Genetic liability to schizophrenia and PTSD were causally associated with increased risk of cannabis use, and schizophrenia with increased alcohol use. Liability to neuroticism, openness and agreeableness was causally linked to smoking behaviors, and extraversion to cannabis use. Most remaining associations showed weak causal evidence, largely due to heterogeneity and pleiotropy. Together, these findings provide evidence for both shared genetic liability and, for a subset of relationships, direct causal effects between substance use behaviors, psychiatric disorders, and personality traits, which can help inform future intervention and prevention research.

Objective. NMDA-receptor hypofunction has been implicated as a core pathophysiological mechanism in schizophrenia, but conflicting evidence exists regarding the locus of the hypofunction--i.e. predominantly affecting receptors on pyramidal neurons or interneurons. To investigate the locus of NMDA-receptor hypofunction we integrated imaging data from humans and mice with biophysical model simulations. Methods. Translational imaging measures--intrinsic neural timescales (INT) and spontaneous co-activation pattern similarity--were investigated in three schizophrenia-relevant mouse models (chronic ketamine administration, n=6; Df(16)A+/-, n=7; and SETD1A+/-, n=9) and matched control mice (n=5; n=7; and n=8, respectively) using two-photon calcium imaging, and individuals with schizophrenia (n=127) and matched non-clinical controls (n=152) using functional MRI. The stabilized supralinear network biophysical model was used to simulate NMDA-receptor hypofunction and its impact on the translational imaging measures. Results. We identified convergent neural phenotypes of shorter intrinsic neural timescales (INT) and reduced spontaneous co-activation pattern similarity in both the schizophrenia-relevant mouse models and the individuals with schizophrenia compared to matched controls. In silico simulations using the stabilized supralinear network biophysical model recapitulated the convergent neural phenotypes through reduced excitatory-to-excitatory connection strength (i.e. NMDA-receptor hypofunction on pyramidal neurons) and predicted that INT and pattern similarity are correlated; this prediction was confirmed in both the mouse and human data. Conclusions. These results identify NMDA-receptor hypofunction on pyramidal neurons as a general and translationally valid mechanism for NMDA-receptor hypofunction in schizophrenia. These convergent translational findings suggest prioritizing the development of treatments for schizophrenia that increase recurrent excitation.

Understanding the mechanisms that underpin the pervasive sex bias in risk for specific psychiatric and neurodegenerative conditions could substantially advance efforts promoting personalized medicine. As a first step towards addressing this issue, we applied imaging transcriptomics and multivariate analysis techniques to data from an adolescent sample enriched in anxiety/depression cases, a child sample enriched in attention deficit hyperactivity disorder (ADHD) cases, and adult patients with treatment resistant major depressive disorder (MDD). We tested whether, across sexes, alignment with female-typical, rather than male-typical, patterns of neural functional connectivity (FC) would relate to a diagnosis (adolescent sample) or severity of symptoms (adult sample) associated with internalizing disorders, which tend to be more often diagnosed in females. To provide preliminary evidence on its functional specificity, we compared the sex-aligned FC profile that distinguished neurotypical adolescents from those diagnosed with depression/anxiety against the sex-aligned FC patterns that distinguished neurotypical children from those diagnosed with ADHD. All the analyses controlled for age within each sample and sex. Finally, we examined the spatial overlap between the sex-aligned FC profile linked to an internalizing diagnosis in adolescence and the transcriptomic maps of genes that show sex-dependent dysregulation in neurodegenerative conditions. Across sexes, increasing alignment with female-typical FC patterns, from sensory-perceptual to default mode regions, discriminated between typically developing and anxious/depressed teens and tracked longitudinal fluctuations in depressive symptoms within adult MDD patients. The identified internalizing-related gradient showed a negative correlation with risk for ADHD in the child sample. The brain regions most strongly aligned with female-, rather than male-, typical FC in depression/anxiety overlapped the spatial expression profiles of genes linked to gonadal hormone synthesis, as well as genes dysregulated in female autism spectrum disorder (ASD) and male neurodegenerative conditions, specifically, Alzheimer's Disease (AD), Huntington's Disease (HD) and Multiple Sclerosis (MS). The brain areas most strongly aligned with male-typical, rather than female-typical, FC patterns overlapped the transcriptomic maps of genes related to androgen and estrogen receptors, as well as of genes dysregulated in male ASD and female neurodegenerative conditions (i.e., AD, HD, MS). Our results raise the possibility that the link between mood pathology and neurodegeneration could reflect a partial, regional reversal of sex-normative functional brain profiles.