Biological Psychiatry

Aims Excitatory/inhibitory imbalance - the equilibrium between glutamatergic excitation and {gamma}-Aminobutyric acid (GABA)ergic inhibition - is implicated in psychosis and may precede psychosis onset. Methods To investigate whether excitatory/inhibitory balance relates to subclinical psychotic experiences, anxiety and depression, we developed a network-level measure integrating Magnetic Resonance Spectroscopy Glutamate/GABA ratios with effective connectivity during resting-state fMRI. In 20 healthy young adults (aged between 18-32; 50% female), we first measured excitatory/inhibitory balance, operationalised as Glutamate/GABA and Glutamate+Glutamine(Glx)/GABA ratio, in the dorsal anterior cingulate cortex. Second, we applied Two-state Dynamic Causal Modelling to assess effective connectivity of the default-mode network, using these ratios as empirical priors to determine how they translate into network connectivity. Third, we ran Bayesian Model Selection at the model and family levels to identify the optimal models. Results The two-state model with Glx/GABA ratio as direct input into the posterior cingulate cortex best explained the data. This best-fitting model incorporated the neurometabolite ratio with Glutamate and GABA and used two-state connectivity. Conclusions Excitatory/inhibitory balance underlies resting-state effective connectivity, with the Glx/GABA ratio shaping connectivity networks. This pathway highlights excitatory/inhibitory balance as a predictor of two-state brain networks, potentially aiding identification of high-risk individuals and informing new interventions.

Psychotic disorders are severe mental conditions whose biological underpinnings remain elusive. This is in part due to marked heterogeneity, which may have precluded advances in precise understanding and treatment of the disorders. Previous work has tried to elucidate this heterogeneity; nevertheless, sex, as an important biological variable, has not been well accounted for despite established sex differences. Using resting-state fMRI data from the BSNIP consortium (N=1753; 64.3% probands with psychosis include 571 females and 556 males; 35.7% of controls includes 369 females and 257 males), we extracted multiscale functional network connectivity and employed unsupervised learning methods to identify potential subgroups with distinct neurobiological profiles, characterized by an overrepresentation of males or females (termed here as sex-dominant Psychosis Imaging Neurosubtypes, PINs). Four sex-dominant neurosubtypes emerged, two female-dominant (fPIN-1, fPIN-2) and two male-dominant (mPIN-1, mPIN-2). Each PIN exhibited distinct, replicable dysconnectivity patterns that uniquely contributed to cognitive deficits. Brain-Predicted Cognitive performance derived from these PIN-specific dysconnectivity, prominent among the triple network (default mode and salience), subcortical-basal ganglia, higher cognition-insular temporal, and visual-occipitotemporal subdomains, correlated with measured cognitive performance and showed PIN-specific reductions relative to controls, confirming neurosubtype-congruent associations. Moreover, fPIN-2's distinct dysconnectivity also predicted positive and negative symptom scores. Our findings lend support for sex-dominant neurobiological heterogeneity, with sex-dominant PINs presenting unique dysconnectivity patterns that contribute to cognitive and symptom outcomes, even though neurosubtypes appear clinically similar. These results establish sex as a critical biological variable in deconstructing psychosis heterogeneity, which holds promise in revealing more precise biomarkers for disease characterization and guiding personalized treatment strategies.

While excess tonic dopamine signalling is a hallmark of schizophrenia and psychotic disorders, it has been difficult to reconcile with dopamine-dependent learning deficits seen in schizophrenia. Excess spontaneous activity of tonic dopamine neurons, coupled with reduced coordinated activity of phasic dopamine neurons, may explain the observed discrepancy between increased tonic signalling and impaired learning. Although intriguing, this chaotic dopamine hypothesis lacks empirical support. In the current study, Pavlovian fear conditioning is used to test this hypothesis in healthy individuals with and without a family history of psychosis using simultaneous [11C]raclopride PET/fMRI. In 16 healthy individuals without a family history of psychosis, we first show that fear conditioning releases dopamine and link this release to BOLD responses. We then report that in 12 first-degree relatives of individuals with psychotic disorders, this adaptive dopamine release in the posterior caudate is lacking, despite no differences in behavioural learning. Furthermore, reduced dopamine release is associated with increased self-reported paranoid thinking, but not with anhedonia. These findings provide novel in vivo evidence supporting the chaotic dopamine hypothesis, suggesting that an adaptive, stimulus-driven dopamine release is lacking in psychotic disorders and may contribute to positive symptoms like paranoia.

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.

Rare genetic variants associated with psychiatric disorders are thought to have larger individual effect sizes and direct biological roles, offering a unique opportunity for understanding causal relationships between genetic architecture and disease phenotypes. A recent whole-exome sequencing meta-analysis identified an increased burden of ultra-rare protein-truncating variants (PTVs) in AKAP11 in patients with bipolar disorder and schizophrenia. AKAP11 encodes an A-kinase anchoring protein that mediates the association of protein kinase A (PKA) and its substrates. How ultra-rare AKAP11 variants contribute to psychiatric disorder pathogenesis remains unknown. Here, using CRISPR-Cas9 genome editing, we introduced into the mouse Akap11 locus an ultra-rare PTV identified in a human patient with bipolar disorder. This ultra-rare PTV reduced Akap11 mRNA and protein levels without producing stable novel mRNA isoforms, leading to Akap11 haploinsufficiency. Male heterozygous mice exhibited behavioral abnormalities consistent with anxiety- and depression-like phenotypes. Transcriptomic and proteomic profiling of the neocortex revealed molecular changes involving synaptic organization, synaptic signaling, and neurite morphogenesis. Notably, the heterozygous mutant showed selective elevation in type I, but not type II, PKA regulatory subunits, as well as PKA catalytic subunits, in the neocortex. Functionally, these changes were accompanied by reduced phosphorylation of PKA substrates, attenuated stimulus-dependent PKA activity in cortical excitatory neurons, and impaired nascent dendritic development in cortical neurons. Together, our study presents a clinically relevant and genetically precise mouse model that provides mechanistic insight into how ultra-rare PTVs may contribute to psychiatric disorder pathogenesis, and a platform for testing rational therapeutics.

Fragile X syndrome (FXS), a leading monogenic cause of intellectual disability and autism-related features, results from loss of fragile X messenger ribonucleoprotein (FMRP). Although early synaptic and cellular abnormalities associated with the loss of FMRP are well described, it remains unclear how these changes shape the maturation of large-scale functional networks, or whether early pharmacological intervention can normalize circuit development. Our previous work demonstrated that cognitive deficits in Fmr1-/y rats emerge during development and can be prevented by brief early-life lovastatin treatment. Here, we asked whether large-scale functional connectivity (FC) shows a similarly dynamic developmental trajectory and whether early intervention alters its emergence. Using longitudinal resting-state functional magnetic resonance imaging (rsfMRI), we found that Fmr1-/y rats displayed an age-dependent FC phenotype, with increased connectivity within the retrosplenial cortex (RSC) at 4 weeks but reduced connectivity within the RSC and distributed brain networks by adulthood compared with wild-type controls. This suggests FC abnormalities emerge over development rather than representing a stable deficit. In contrast to its effects on cognitive measures, brief early-life lovastatin treatment rescues did not prevent the emergence of connectivity abnormalities. Reduced RSC FC was also observed in a small cohort of individuals with FXS (n = 5 per group), supporting further investigation of functional connectivity measures alongside behavioural and molecular endpoints in translational studies of FXS.

Early life adversity (ELA) has lasting impacts on emotion circuit function1. Exposure to early maltreatment, neglect, or household instability is associated with enduring alterations in threat evaluation and response, hallmarks of mood and anxiety disorders2-4. These alterations often emerge during adolescence and are characterized by excessive avoidance that limits engagement in rewarding experiences5,6. However, the neurodevelopmental mechanisms linking ELA to increased threat responses remain unclear. We identified a stress-sensitive medial prefrontal cortex (mPFC)-basolateral amygdala pathway that co-expresses corticotropin-releasing hormone (CRH) and glutamate. We find that ELA strengthens this pathway and increases threat avoidance behavior during adolescence. Optogenetic inhibition and developmental CRH knockdown in this pathway both rescue ELA-induced elevations in adolescent avoidance, whereas broadly reducing mPFC CRH expression in typically reared mice mimics ELA phenotypes. The same manipulations produced no behavioral effects in adults. Together, our findings indicate that cell type-specific CRH signaling regulates developmental circuit plasticity, biasing emotional circuit function and potentially contributing to psychiatric vulnerability.

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

In this study, we present the largest rare coding variant association study of schizophrenia to date, with 87,959 schizophrenia cases and 150,587 controls. We identify 16 genes at exome-wide significance: SETD1A, ZMYM2, HERC1, RB1CC1, SCAF1, XPO7, SP4, FYN, PPP3CA, CUL1, HDAC9, JARID2, ATP9A, PTK2, STAG1, and SCN2A, and an additional 24 at a 5% false discovery rate. Cases carrying ultra-rare, damaging variants, primarily protein-truncating and deleterious missense mutations, show strong enrichment in constrained genes with consistent effects across ancestries. Half of the 40 identified genes overlap with those implicated in developmental delay, autism, or bipolar disorder, underscoring shared neurodevelopmental risk. All 40 genes identified are highly expressed in excitatory and inhibitory neurons, and their expression patterns span diverse developmental stages. Functionally, the identified genes implicate chromatin regulation, protein degradation, and synaptic function. Overall, our results advance understanding of the genetic architecture of schizophrenia, provide a foundation for modeling risk genes in cellular and animal systems, and offer a framework for future work toward biologically informed patient stratification.

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.

Schizophrenia, bipolar disorder, and major depression vary in their age of onset, cognitive impacts, degree of impairment, and clinical course. However, since psychosis is a transdiagnostic feature that occurs across all three of these disorders, they can be represented as a spectrum. We leveraged genomic data to explore different functionally distinct components of the psychosis spectrum. We used Genomic Structural Equation Modelling (Genomic SEM) to define four factors indexing specific aspects of psychosis-related symptoms shared across depression, schizophrenia, and bipolar disorder: a general Psychosis factor with low Cognitive functionality (gPsychosisCognition), a specific Psychosis factor independent of cognition (sPsychosis), a specific Affective factor independent of psychosis (sAffective) and a specific Mania factor independent of depression (sMania). We then used Stratified Genomic SEM and transcriptomic analyses to characterize biological functions and temporal trends associated with these factors. sPsychosis, sAffective, and sMania exhibited differential genetic correlations with personality, with sPsychosis and sAffective showing positive correlations with Openness, while Neuroticism showed a strong positive correlation with sAffective and a negative correlation for sMania. GABAergic, cortical, and CA1-CA3 enrichments were significant across the continuum with evidence for factor specific enrichment: dentate gyrus, CA4 and cerebellum were non-significant only for sAffective and oligodendrocytes enriched only in gPsychosisCognition. Transcriptomic analyses revealed different temporal expression trends across factors, identifying a mostly prenatal expression for sMania and sAffective, and more consistent expression across development for sPsychosis. We demonstrated that a clinically informed genomic model can shed light on the functional heterogeneity of psychosis, identify both unique and shared biological components and highlight its partial neurodevelopmental origin.

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.

Psychosis has long been conceptualized as a disorder of disrupted hierarchical integration across distributed brain systems, yet it remains unclear whether alterations in macroscale cortical hierarchy are already present before illness onset and are associated with subsequent transition to psychosis. Using connectome gradient mapping, we characterized baseline cortical hierarchical architecture along the unimodal-to-transmodal axis in 580 participants from the NAPLS-3 cohort, including converters (CHR-C, n = 56), non-converters (CHR-NC, n = 434), and healthy controls (HC, n = 90). Group differences were assessed at regional, network, and global levels. Group comparisons revealed that CHR-C individuals, relative to the other two groups, exhibited bidirectional alterations selectively along the sensorimotor-to-association gradient, with reduced values in the visual network alongside elevated values in the default mode network, indicating greater separation between sensory and transmodal systems along the gradient. At the global level, CHR-C showed increased explained variance, range, and variation of this gradient, collectively indicating hierarchical expansion. Notably, greater explained variance of this gradient was associated with a shorter time to conversion to psychosis, while increased gradient range and variation were associated with higher positive symptom severity across CHR individuals. These findings indicate that expansion of the sensorimotor-to-association connectome hierarchy is already present before psychosis onset in individuals who subsequently convert to psychosis. This altered hierarchical organization may reflect greater decoupling between sensory and transmodal systems and may characterize neurobiological changes associated with progression from a clinical high-risk state to psychotic illness.

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.

The nucleus accumbens (NAc) is a key structure translating dopaminergic inputs into social avoidance following repeated social defeat. Within the NAc, dopamine (DA) release is modulated in part by cholinergic interneurons (CINs). CINs express the vesicular glutamate transporter type 3 (VGLUT3), which supports glutamate co-release and regulates vesicular acetylcholine (ACh) loading, thereby fine-tuning cholinergic signaling. A rare human VGLUT3-p.T8I missense variant is associated with compulsive behaviors toward food and drugs of abuse. We hypothesized that p.T8I disrupts NAc ACh dynamics and affects social avoidance following social defeat. Male and female mice expressing the VGLUT3-p.T8I variant were exposed to a 10-day chronic social defeat stress (CSDS) paradigm. Social avoidance toward the aggressor strain and a C57BL/6 (B6) conspecific was measured using the social interaction and 3-chamber tests, and anxiety-like behavior was evaluated in the elevated zero maze. Fiber photometry was used to monitor ACh and DA dynamics in the NAc medial shell (NAcMsh) during CSDS and social interaction. After CSDS, VGLUT3T8I/T8I males showed aggressor strain-specific social avoidance and increased anxiety-like behavior compared to WT mice. Mutants showed blunted CSDS-induced ACh increase during attacks but enhanced ACh release upon re-exposure to the CD1 aggressor strain correlating with avoidance severity. DA dynamics were minimally affected. Females, regardless of genotype, displayed a male-mutant-like phenotype. In males, the VGLUT3-p.T8I variant alters NAcMsh ACh signaling and promotes selective social avoidance toward aggressors and elevated anxiety after CSDS, highlighting a potential cholinergic mechanism underlying stress-induced social avoidance with broader relevance to psychiatric disorders.

Psychiatric disorders are complex conditions characterized by substantial overlap in genetic risk and shared biological mechanisms. However, how individual risk genes contribute to shared disease mechanisms and disorder-specific phenotypes remains poorly understood. BRD1 has emerged as a chromatin-associated regulator with transdiagnostic relevance across psychiatric disorders and a central role in gene regulatory networks enriched for psychiatric risk genes. To investigate the biological consequences of reduced BRD1 function in a translationally relevant system, we generated a minipig model harboring a monoallelic deletion in BRD1 and performed longitudinal neuroimaging together with behavioral and multi-omics profiling. BRD1 haploinsufficient minipigs displayed normal growth and exploratory behavior but exhibited subtle age-dependent differences in motivational behavior. Despite the absence of overt developmental abnormalities, longitudinal neuroimaging revealed genotype-associated structural differences primarily involving the cerebral cortex and caudate nucleus, suggestive of altered neurodevelopmental trajectories. Integrated multi-omics analyses revealed striking convergence across transcriptomic and metabolomic datasets, identifying coordinated perturbations of mitochondrial function, redox regulation, and phospholipid metabolism across multiple brain regions. Notably, these molecular alterations were not restricted to the central nervous system, as peripheral multi-omics profiling revealed systemic metabolic alterations, including altered phospholipid composition and glucose metabolism. Together, these findings indicate that BRD1 haploinsufficiency is associated with coordinated neurodevelopmental and metabolic alterations across brain and peripheral tissues. More broadly, this study provides systems-level insight into how a psychiatric risk gene influences interconnected neurodevelopmental and metabolic processes across multiple levels of biological organization and highlights the value of large-animal multi-omics models for translational neuropsychiatric research.