Biological Psychiatry

BackgroundMajor depressive disorder (MDD) is prevalent and debilitating, and development of improved treatments is limited in part by insufficient understanding of the mechanism of disease remission. In turn, efforts to elucidate mechanisms have been challenging due to disease heterogeneity and limited effectiveness of treatments, which require weeks-to-months to induce remission. We recently developed a form of repetitive transcranial magnetic stimulation that induces remission in 90% of individuals with severe, treatment resistant MDD in 1-5 days (SAINT). This provides a new tool to begin exploring the network-level mechanisms of MDD remission. ObjectiveDetermine the functional connectivity (fc) changes that occur with SAINT in brain regions associated with emotion regulation. MethodsResting-state fMRI scans were performed just prior to (baseline), and following, open-label SAINT in 18 participants with severe, treatment-resistant MDD. Fc was determined between regions of interest (ROIs) defined a priori with well-described roles in emotion regulation. ResultsFollowing SAINT treatment fc was significantly decreased between the following ROI pairs: dorsolateral prefrontal cortex (DLPFC)-striatum, DLPFC-amygdala, default mode network (DMN)-subgenual cingulate cortex (sgACC), DMN-amygdala, DMN-striatum, amygdala-striatum, and between amygdala subregions. Greater clinical improvements were associated with larger decreases in fc between DLPFC-amygdala and DLPFC-insula. Greater clinical improvements were associated with smaller decreases in fc between sgACC-DMN. Significant increases and decreases in fc between insula-amygdala were observed depending on the subregions. Greater clinical improvements were associated with lower baseline fc between DMN-DLPFC, DMN-striatum, and DMN-ventrolateral prefrontal cortex. ConclusionSAINT-induced remission from depression is associated with fc changes that suggest improved regulation of emotion. Although preliminary, this leads us to hypothesize that interventions that augment top-down regulation of emotion may be effective depression treatments.

ObjectiveDrugs targeting the N-Methyl-D-aspartic acid (NMDA) system and the ability to learn new associations have been proposed as potential adjunct treatments to boost the success of exposure therapy for anxiety disorders. However, the effects of the NMDA partial agonist d-cycloserine on psychological treatment have been mixed. We investigated potential neurocognitive mechanisms underlying the clinical effects of d-cycloserine-augmented exposure, to inform the optimal combination of this and similar agents with psychological treatment. MethodsUnmedicated patients with panic disorder were randomised to single-dose d-cycloserine (250mg; N=17) or matching placebo (N=16) 2hrs before one session of exposure therapy. Neurocognitive markers were assessed one day after treatment, including reaction-time based threat bias for fearful faces and amygdala response to threat. Clinical symptom severity was measured using self-report and clinician-rated scales the day before and after treatment, and at 1- and 6-months follow-up. Analysis was by intention-to-treat. ResultsOne day after treatment, threat bias for fearful faces and amygdala threat response were attenuated in the drug compared to the placebo group. Lower amygdala magnitude predicted greater clinical improvement during follow-up across groups. D-cycloserine led to greater clinical recovery at 1-month follow-up (d-cycloserine 71% versus placebo 25%). DiscussionD-cycloserine-augmented single-session exposure therapy reduces amygdala threat response, and this effect predicts later clinical response. These findings highlight a neurocognitive mechanism by which d-cycloserine may exert its augmentative effects on psychological treatment and bring forward a marker that may help understand and facilitate future development of adjunct treatments with CBT for anxiety disorders. (D-cycloserine Augmented CBT for Panic Disorder; clinicaltrials.gov; NCT01680107)

Clozapine is currently the only antipsychotic with demonstrated efficacy in treatment-refractory schizophrenia (TRS). However, response to clozapine differs widely between TRS patients, and there are no available clinical or neural predictive indicators that could be used to increase or accelerate the use of clozapine in patients who stand to benefit. Furthermore, it remains unclear how the neuropharmacology of clozapine contributes to its therapeutic effects. Identifying the mechanisms underlying clozapines therapeutic effects across domains of symptomatology could be crucial for development of new optimized therapies for TRS. Here, we present results from a prospective neuroimaging study that quantitatively related heterogeneous patterns of clinical clozapine response to neural functional connectivity at baseline. We show that we can reliably capture specific dimensions of clozapine clinical response by quantifying the full variation across item-level clinical scales, and that these dimensions can be mapped to neural features that are sensitive to clozapine-induced symptom change. Thus, these features may act as "failure modes" that can provide an early indication of treatment (non-)responsiveness. Lastly, we related the response-relevant neural maps to spatial expression profiles of genes coding for receptors implicated in clozapines pharmacology, demonstrating that distinct dimensions of clozapine symptom-informed neural features may be associated with specific receptor targets. Collectively, this study informs prognostic neuro-behavioral measures for clozapine as a more optimal treatment for selected patients with TRS. We provide support for the identification of neuro-behavioral targets linked to pharmacological efficacy that can be further developed to inform optimal early treatment decisions in schizophrenia.

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

BackgroundPsychosis has been conceptualised as a continuum extending from healthy individuals with psychotic-like experiences to clinical populations with schizophrenia. It is unclear which biological mechanisms found in chronic schizophrenia extend across the psychosis continuum to healthy individuals with high positive schizotypy (HS). In this study, we used computational modeling to test whether changes in effective connectivity and excitation/inhibition (E/I) balance reported in schizophrenia are also found in HS. MethodsA total of 2425 individuals from the general population were screened for HS. A subset (N=141) was invited for in-depth phenotyping. Resting-state functional magnetic resonance imaging (rsfMRI) and proton magnetic resonance spectroscopy (1H-MRS) were recorded in n=69 HS individuals and n=72 group-matched controls with low schizotypy (LS). We used dynamic causal modeling to estimate effective connectivity between bilateral primary auditory cortex (A1), superior temporal gyrus (STG), and inferior frontal gyrus (IFG). ResultsBilateral backward connectivity from IFG to STG was significantly reduced in HS compared to LS. Widespread cortical disinhibition in the auditory cortex-IFG network correlated with more severe positive schizotypy scores and impulsive nonconformity. Reduced excitability in the same network was correlated with stronger cognitive disorganisation. ConclusionsOur results favour a psychosis-continuum hypothesis, suggesting that reduced top-down drive from frontal cortex and compensatory allostatic upregulation of cortical excitability, as observed in chronic schizophrenia, also extend to groups with sub-clinical psychotic symptoms. Frontal cortex dysfunction may serve as a biologically interpretable biomarker of psychosis risk and a target for preventative interventions.

This study aims to identify Psychosis Imaging Neurosubtypes (PINs)-- homogeneous subgroups of individuals with psychosis characterized by distinct neurobiology derived from imaging features. Specifically, we utilized resting-state fMRI data from 2103 B-SNIP 1&2 participants (1127 with psychosis, 350 relatives, 626 controls) to compute subject-specific multiscale functional network connectivity (msFNC). We then derived a low-dimensional neurobiological subspace, termed Latent Network Connectivity (LNC), which captured system-wide interconnected multiscale information across three components (cognitive-related, typical, psychosis-related). Projections of psychosis participants msFNC onto this subspace revealed three PINs through unsupervised learning, each with distinct cognitive, clinical, and connectivity profiles, spanning all DSM diagnoses (Schizophrenia, Bipolar, Schizoaffective). PIN-1, the most cognitively impaired, showed Cerebellar-Subcortical and Visual-Sensorimotor hypoconnectivity, alongside Visual-Subcortical hyperconnectivity. Most cognitively preserved PIN-2 showed Visual-Subcortical, Subcortical-Sensorimotor, and Subcortical-Higher Cognition hypoconnectivity. PIN-3 exhibited intermediate cognitive function, showing Cerebellar-Subcortical hypoconnectivity alongside Cerebellar-Sensorimotor and Subcortical-Sensorimotor hyperconnectivity. Notably, 55% of relatives aligned with the same neurosubtype as their affected family members--a significantly higher rate than random chance (p-valueRelatives-to-PIN-1 < 0.001, p-valueRelatives-to-PIN-2 < 0.05, p-valueRelatives-to-PIN-3 < 0.001) compared to a non-significant 37% DSM-based classification, supporting a biological basis of these neurosubtypes. Cognitive performance reliably aligns with distinct brain connectivity patterns, which are also evident in relatives, supporting their construct validity. Our PINs differed from original B-SNIP Biotypes, which were determined from electrophysiological, cognitive, and oculomotor data. These findings underscore the limitations of DSM-based classifications in capturing the biological complexity of psychotic disorders and highlight the potential of imaging-based neurosubtypes to enhance our understanding of the psychosis spectrum.

1Background and objectivesThe extent to which structural connectivity constrains effective connectivity in both depression and neurodegenerative contexts remains poorly understood. In particular, the relationship between structural connectivity aberrations and effective dysconnectivity associated with depression in Huntingtins disease remains uncharacterized. Here, we applied a novel procedure that implements structural connectivity-informed spectral dynamic causal modelling to examine how structural connectivity shapes directed inter-regional influences in premanifest Huntingtons disease gene expansion carriers (HDGECs) with and without depression history. MethodsUsing spectral dynamic causal modeling embedded in a hierarchical empirical Bayes framework, we analyzed fMRI data from 98 premanifest HDGECs across default mode network and striatum (caudate and putamen). HDGECs were split into two groups based on either having a history of depression or not. Depression severity on both the Beck Depression Inventory, 2nd Edition (BDI-II) and Hospital Anxiety and Depression Scale, Depression Subscale (HADS-D) was used to measure clinically elevated depression symptoms. Leave-one-out cross-validation was implemented to test predictive validity. ResultsModel evidence substantially favored structurally informed over uninformed approaches across all participants. For HDGECs, having a history of depression was associated with reduced baseline variability in effective connectivity (decreased parameter), with particularly tight regularization of near-zero-valued structural connections toward zero effective connectivity values while leaving strongly connected pathways relatively unaffected. Effects converged on striatal self-connectivity and hippocampal-striatal pathways, with distinct patterns emerging between depression history groups. Notably, clinically elevated depression revealed differential connectivity signatures, with right caudate self-connectivity showing positive correlations with clinical cut-offs for HDGECs with and without depression history. In leave-one-out cross-validation, specific connections including DMN-to-striatum (BDI: r = -0.31, p = .002; HADS-D: r = -0.33, p = .001), right hippocampus-to-left caudate (BDI: r = -0.46, p < .001; HADS-D: r = -0.30, p = .002), and left caudate-to-left putamen (BDI: r = -0.48, p < .001; HADS-D: r = -0.30, p = .003) significantly predicted individual differences in depression severity scores. DiscussionTogether, these findings link reduced network flexibility to depression vulnerability in premanifest neurodegeneration, providing a mechanistic bridge between anatomical constraints, effective connectivity alterations, and clinical depression phenotypes.

Increased dopamine (DA) signaling in the striatum has been a cornerstone hypothesis about psychosis for over 50 years. Increased dopamine release results in psychotic symptoms, while D2 dopamine receptor (DRD2) antagonists are antipsychotic. Recent schizophrenia GWAS identified risk-associated common variants near the DRD2 gene, but the risk mechanism has been unclear. To gain novel insight into risk mechanisms underlying schizophrenia, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in postmortem caudate nucleus from a cohort of 444 individuals. Integrating expression quantitative trait loci (eQTL) analysis, transcriptome wide association study (TWAS), and differential expression analysis, we found many new genes associated with schizophrenia through genetic modulation of gene expression. Using a new approach based on deep neural networks, we construct caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. Interestingly, we found that genetic risk for schizophrenia is associated with decreased expression of the short isoform of DRD2, which encodes the presynaptic autoreceptor, and not with the long isoform, which encodes the postsynaptic receptor. This association suggests that decreased control of presynaptic DA release is a potential genetic mechanism of schizophrenia risk. Altogether, these analyses provide a new resource for the study of schizophrenia that can bring insight into risk mechanisms and potential novel therapeutic targets.

AbstractO_ST_ABSBackgroundC_ST_ABSReduced mismatch negativity (MMN) and P300 event-related potential (ERP) components are widely replicated in schizophrenia and are also observed in individuals at clinical high risk for psychosis (CHR-P) who subsequently convert to psychosis. It is unknown whether they reflect changes in excitatory and/or inhibitory synaptic function, both implicated in schizophrenia and considered potential drug targets. MethodsWe analyzed baseline MMN and P300 ERPs from the NAPLS2 study, asking whether altered synaptic excitation, inhibition, or both could explain amplitude reductions in CHR-P (n=583). CHR-P participants who converted to psychosis (CHR-Converters; n=77) or remitted by 24-month follow-up (CHR-Remitters; n=94) were compared on MMN evoked by pitch+duration double-deviant tones and P300 elicited by target tones from passive and active auditory oddball paradigms, respectively. Biophysical modeling was used to infer (excitatory) pyramidal cell and (inhibitory) interneuron function from both MMN and P300 ERPs. ResultsMMN and P300 amplitude reductions in future CHR-Converters relative to CHR-Remitters were best explained by reduced pyramidal cell excitability (posterior probability P>.95 of a group-by-condition interaction effect). In simulations, reduced pyramidal cell excitability suppressed deviant and target ERPs. Within CHR-Converters, but not CHR-Remitters, more severe positive symptoms were associated with disinhibition of pyramidal cells (P>.99). ConclusionsResults mirror previous findings in schizophrenia and suggest that reduced pyramidal cell excitability is present at baseline in future CHR-Converters, supporting the hypothesis that hypofunction of pyramidal cells is a primary pathology in schizophrenia, rather than a consequence of chronic illness. Positive symptoms among CHR-Converters may reflect compensatory downregulation of inhibition.

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.

Cholinergic dysfunction has been implicated in the pathophysiology of psychiatric disorders such as schizophrenia, depression, and bipolar disorder. The basal forebrain (BF) cholinergic nuclei, defined as cholinergic cell groups Ch1-3 and Ch4 (Nucleus Basalis of Meynert; NBM), provide extensive cholinergic projections to the rest of the brain. Here, we examined microstructural neuroimaging measures of the cholinergic nuclei in patients with untreated psychosis ([~] 31 weeks of psychosis, <2 defined daily dose of antipsychotics) and used Magnetic Resonance Spectroscopy (1H-MRS) and transcriptomic data to support our findings. We used a cytoarchitectonic atlas of the BF to map the nuclei and obtained measures of myelin (quantitative T1, or qT1 as myelin surrogate) and microstructure (axial diffusion; AxD). In a clinical sample (n=85; 29 healthy controls, 56 first-episode psychosis), we found significant correlations between qT1 and 1H-MRS-based dorsal anterior cingulate choline in healthy controls, while this relationship was disrupted in FEP. Case-control differences in qT1 and AxD were observed in the Ch1-3, with increased qT1 (reflecting reduced myelin content) and AxD (reflecting reduced axonal integrity). We found clinical correlates between left NBM qT1 with manic symptom severity, and AxD with negative symptom burden in FEP. Intracortical and subcortical myelin maps were derived and correlated with BF myelin. BF-cortical and BF-subcortical myelin correlations demonstrate known projection patterns from the BF. Using data from the Allen Human Brain Atlas, cholinergic nuclei showed significant enrichment for schizophrenia and depression-related genes. Cell-type specific enrichment indicated enrichment for cholinergic neuron markers as expected. Further relating the neuroimaging correlations to transcriptomics demonstrated links with cholinergic receptor genes and cell type markers of oligodendrocytes and cholinergic neurons, providing biological validity to the measures. These results provide genetic, neuroimaging, and clinical evidence for cholinergic dysfunction in schizophrenia and other psychiatric disorders such as depression.

Background: Opioid use disorder (OUD) is heritable, yet most genome-wide association studies (GWAS) have focused on European populations, leaving the genetic architecture of OUD in non-European populations underexplored. Methods: We conducted GWAS of OUD across three ancestries using electronic health records and genomic data from 52,357 All of Us Research Program participants (8,912 cases; 43,445 matched opioid-exposed controls; 48.5% female). Participants were stratified into European (EUR), African (AFR), and Admixed American (AMR) ancestry groups for logistic regression GWAS, with independent replication in the Million Veteran Program. We then applied the deep-learning model AlphaGenome to predict the tissue-specific transcriptomic and splicing consequences of top risk variants across 13 reward-pathway brain regions. Results: We identified and replicated a novel DDX6 risk locus, alongside established OPRM1 and FURIN signals. AlphaGenome predicted the DDX6 regulatory allele downregulates the stress-resistance gene FOXR1 in the nucleus accumbens, while the protective OPRM1 variant (rs1799971) upregulates OPRM1 expression across reward networks. Other signals of interest included IL6R and SHISA9 (EUR); GHR (AFR); and ASTN2 (AMR). Conclusions: This study identifies DDX6 as a novel OUD risk locus, replicates associations with OPRM1 and FURIN, and highlights biologically plausible ancestry-specific signals in AFR and AMR populations. We also replicated top variants in an independent population. Finally, integrating GWAS with deep-learning annotations provides specific, localized biological hypotheses to guide future experimental validation and targeted therapeutics.

Major depressive disorder (MDD) exhibits substantial clinical heterogeneity complicating prognosis definition and treatment selection. Characterizing MDD subtypes through distinct clinical manifestations could enhance personalized therapeutic approaches. We developed a topological data analysis (TDA) framework with graph-based community detection to identify homogeneous patient subgroups using multimodal data integration. We implemented a TDA pipeline in UK Biobank MDD participants with gene-environment (G-E, N=20,715) and gene-environment-neuroimaging (G-E-I, N=3,044) data. We systematically compared predictive capabilities across genetic, environmental, and neuroimaging features, alone and combined, for 18 health-related outcomes. For the best-predictive set of features identified for each outcome, a novel two-stage feature ranking approach identified features relevant for graph construction and community-based outcome differentiation. Cross-cohort validation utilized two independent datasets. G-E interactions demonstrated superior predictive performance for 13 clinical outcomes, including treatment-resistant depression (TRD), symptom subtypes, and suicidal phenotypes. Community profiling revealed distinct vulnerability pathways: trauma-stress exposures linked to TRD and episode severity, while substance-behavioral profiles associated with anxious symptoms. Environmental factors emerged as primary determinants of most health outcomes, whereas neuroimaging features optimally predict medical comorbidities. Cross-cohort validation confirmed replication for multiple outcomes: self-harm behavior and anxious features (GSRD), TRD and vascular diseases (HSR), with consistent environmental stress-related predictive features across cohorts. TDA successfully identified clinically relevant MDD subgroups with unique multimodal signatures. These findings underscore the essential role of integrating genetic, environmental, and neuroimaging characteristics for robust health outcome prediction, establishing TDA-based community detection as an effective framework for MDD patient stratification and advancing precision medicine approaches in depression management. Significance StatementTopological Data Analysis (TDA) combined with community detection was used to identify clinically meaningful subgroups within Major Depressive Disorder (MDD) from multimodal UK Biobank data integrating genetic, environmental, and neuroimaging features. We systematically compared unimodal and multimodal feature sets to stratify patients across 18 health-related outcomes, with cross-cohort validation in independent datasets. Gene-by-environment interactions emerged as optimal predictors for mental health outcomes, revealing distinct vulnerability pathways: trauma-stress profiles predicted treatment resistance and episode severity, while substance-behavioral patterns were linked to anxious and neurovegetative symptoms. Brain imaging features best predicted medical comorbidities, particularly vascular diseases. Cross-cohort validation confirmed replication across populations. These findings establish TDA-based community detection as a powerful framework for MDD stratification, advancing precision psychiatry and personalized intervention strategies.

BackgroundStress is a significant risk factor for depression and anxiety, two highly comorbid disorders with sex differences in symptom presentation and prevalence. The Chronic Variable Stress (CVS) mouse model is a useful method for examining sex-specific susceptibility, as stress can be titrated in a sex-specific manner to produce depressive- and anxiety-like behaviours across both sexes. However, the sex-specific mechanisms regarding how CVS reorganizes brain anatomy remain unclear. MethodsUsing structural magnetic resonance imaging (MRI), we provide the first whole-brain characterization of neuroanatomical changes induced by 6 or 28 days of exposure to CVS in female and male mice, respectively, and their association to behavior. We then examined the structural connectome underlying sex-specific latent dimensions of stress-susceptibility and potential molecular mechanisms using spatial gene expression analyses. ResultsCVS induced significant neuroanatomical changes in regions already implicated in depression in both sexes (e.g. nucleus accumbens and hippocampus) as well as female- and male-specific neuroanatomical changes. In females, these changes were associated with both depressive- and anxiety-like behavior. While in males, we identified two orthogonal dimensions of neuroanatomical changes associated with anxiety-like behavior or social preference. These latent dimensions are associated with sex-specific hub regions and, in females, were associated with genes enriched for protein localization to the cell surface. ConclusionOur findings indicate that different durations of CVS result in similar neuroanatomical changes in both sexes, however the direction of change and association to behavior is sex-specific. In females, these changes may be attributed to alterations in synaptic connectivity.

BackgroundGenetic variants may impact connectivity in the fear network such that genetically driven alterations of network properties (partially) explain individual differences in learning. Our aim was to identify genetic indices that predict physiological measures of fear learning mediated by MRI-based connectivity. MethodsWe built prediction models using exploratory mediation analysis. Predictors were polygenic scores for several psychological disorders, neuroticism, cross-disorder risk, cognitive traits, and gene expression-based scores. Candidate mediators were structural and functional connectivity estimates between the hippocampus, amygdala, dorsal anterior cingulate, ventromedial prefrontal cortex and cerebellar nuclei. Learning measures based on skin conductance responses to conditioned fear stimuli (CS+), conditioned safety cues (CS-), and differential learning (CS+ vs. CS-), for both acquisition and extinction training served as outcomes. ResultsReliable prediction of learning indices was achieved by means of conventional polygenic score construction but also by modelling cross-trait and trait-specific effects of genetic variants. A latent factor of disorder risk as well as major depressive disorder conditioned on other traits were related to the acquisition of conditioned fear. Polygenic scores for short-term memory showed an association with safety cue learning. During extinction, genetic indices for neuroticism and verbal learning were predictive of CS+ and differential learning, respectively. While mediation effects depended on connectivity modality, prediction of fear involved all regions of interest. Expression-based scores showed no associations. ConclusionsOur findings highlight the utility of leveraging pleiotropy to improve complex trait prediction and brain connectivity as a promising endophenotype to understand the pathways between genetic variation and fear expression.

Genome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed "mental health phenotypes") using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

Bullying is an adverse childhood experience affecting up to one-third of the global population and linked to psychosis-like experiences (PLEs), which increase the risk of psychotic disorders. This study aimed to investigate the association between the severity and persistence of bullying and PLEs and the neurobiological pathways from bullying to psychosis-like experiences by assessing multiscale brain functional network connectivity (msFNC). We used data from the ABCD Study, a large, ongoing, multisite, population-based prospective cohort study following U.S. adolescents. We included adolescents with complete bullying and PLEs assessments at the 2- and 3-year follow-ups (T1: n=10,939; T2: n=10,102). We examined the associations between bullying severity and temporal exposure and PLEs using linear mixed-effects models. In a 2-year rsfMRI subsample (n=5,280), we used a Neuromark framework to analyze whether msFNC mediated the pathway from bullying to PLEs. Higher PLEs were associated with the presence and severity of bullying (non-bullied vs. mild bullying: d=-0.19, CI: -0.39 to -0.19, p<0.0001; moderate vs. severe bullying: d=-0.49, CI: -0.69 to -0.56, p<0.0001). When bullying ceased, PLEs returned to non-bullied levels (d=-0.13, CI:-0.20 to -0.05, p=0.16), whereas persistence over two years led to greater elevations (d=-0.36, CI:-0.43 to -0.29, p<0.0001). We observed similar patterns for non-paranoid and hallucination-like experiences and their distress. msFNC in paralimbic, default mode, central executive, somatomotor, temporoparietal, insulotemporal, and frontal networks mediated the association. Bullying is time- and dose-dependently associated with psychosis-like outcomes. msFNC between functional brain networks is a novel neurobiological pathway that mediates the link from bullying to PLEs.

Dysconnectivity in schizophrenia is a pervasive trait across various levels of systems biology. To better understand disrupted patterns of molecular connectivity distinguishing schizophrenia from control non-clinical populations, we applied novel approaches to gene co-expression networks in large samples of postmortem brains from multiple regions relevant to schizophrenia: the dorso-lateral prefrontal cortex- (DLPFC) (Ndonors=297), hippocampus (Ndonors=250) and caudate (Ndonors=349). We identified differentially connected genes (DCGs) in schizophrenia networks that deviated from architectural relationships characteristic of control gene co-expression networks, by assessing three network metrics - total connectivity (K), clustering coefficient (C), and intra-module degree (kIn) determined by projecting the modular community structure of the control networks onto the schizophrenia co-expression networks. Genes showing significant absolute case-control differences for these metrics (i.e., irrespective of difference directionality) were then tested for their relationships with common genetic variants conferring risk of schizophrenia and their biological significance through post-GWAS analyses (stratified LDSC and MAGMA), gene ontology annotations and enrichment in schizophrenia-relevant gene sets. We identified multiple DCGs, with case-control differences of connectivity metrics, consistent across brain regions. When parsed by parameter specificity, these genes show shared and specific enrichment in schizophrenia genetic signal, biological ontologies and selected cell-type markers. Notably these findings revealed widespread disturbances in co-expression connectivity affecting both neuronal and glial cells, particularly oligodendrocytes. Overall, our results highlight disrupted co-expression network architecture in schizophrenia, implicating disrupted neuronal-glial crosstalk and its effect on synaptic transmission.

ObjectivesPsychotic-like experiences (PLEs) may arise from genetic and environmental risk leading to worsening cognitive and neural metrics over time, which in turn lead to worsening PLEs. Persistence and distress are factors that distinguish more clinically significant PLEs. Analyses used three waves of unique longitudinal Adolescent Brain Cognitive Development Study data (ages 9-13) to test whether changes in cognition and structural neural metrics attenuate associations between genetic and environmental risk with persistent distressing PLEs. MethodsMultigroup univariate latent growth models examined three waves of cognitive metrics and global structural neural metrics separately for three PLE groups: persistent distressing PLEs (n=356), transient distressing PLEs (n=408), and low-level PLEs (n=7901). Models then examined whether changes in cognitive and structural neural metrics over time attenuated associations between genetic liability (i.e., schizophrenia polygenic risk scores/family history) or environmental risk scores (e.g., poverty) and PLE groups. ResultsPersistent distressing PLEs showed greater decreases (i.e., more negative slopes) of cognition and neural metrics over time compared to those in low-level PLE groups. Associations between environmental risk and persistent distressing PLEs were attenuated when accounting for lowered scores over time on cognitive (e.g., picture vocabulary) and to a lesser extent neural (e.g., cortical thickness, volume) metrics. ConclusionsAnalyses provide novel evidence for extant theories that worsening cognition and global structural metrics may partially account for associations between environmental risk with persistent distressing PLEs.

Schizophrenia is a polygenetic mental disorder with heterogeneous positive and negative symptom constellations, and is associated with abnormal cortical connectivity. The thalamus has a coordinative role in cortical function and is key to the development of the cerebral cortex. Conversely, altered functional organization of the thalamus might relate to overarching cortical disruptions in schizophrenia, anchored in development. Here, we contrasted resting-state fMRI in 99 antipsychotic-naive first-episode early-onset schizophrenia (EOS) patients and 100 typically developing controls to study whether macroscale thalamic organization is altered in EOS. Employing dimensional reduction techniques on thalamocortical functional connectome, we derived lateral-medial and anterior-posterior thalamic functional axes. We observed increased segregation of macroscale thalamic functional organization in EOS patients, which was related to altered thalamocortical interactions both in unimodal and transmodal networks. Using an ex vivo approximation of core-matrix cell distribution, we found that core cells particularly underlie the macroscale abnormalities in EOS patients. Moreover, the disruptions were associated with schizophrenia-related gene expression maps. Behavioral and disorder decoding analyses indicated that the macroscale hierarchy disturbances might perturb both perceptual and abstract cognitive functions and contribute to negative syndromes in schizophrenia, suggesting a unitary pathophysiological framework of schizophrenia.