Translational Psychiatry

IntroductionRodent chronic-stress models are central to preclinical depression research, yet behavioral phenotypes are often assessed assay-by-assay, which can reduce cross-cohort comparability and mask heterogeneity across symptom domains (e.g., anhedonia, anxiety-like behavior, and behavioral despair). An integrative behavioral index may provide a more robust and interpretable measure of overall stress-induced severity and facilitate stratification of susceptible versus resilient individuals. MethodsWe developed the Multimodal Behavior Scoring (MBS) algorithm to integrate outcomes from standard assays--sucrose preference test (SPT), open field test (OFT), and forced swim test (FST)--into a unified severity score. MBS was evaluated in two independent paradigms, chronic unpredictable mild stress (CUMS) and chronic social defeat stress (CSDS), quantifying group discrimination, reproducibility (intraclass correlation coefficient, ICC), and phenotypic stratification. ResultsMBS robustly separated stressed from control mice (CUMS: +39.8%, p<0.0001; CSDS: +121.8%, p<0.0001) and showed high cross-cohort reproducibility (ICC>0.88). MBS further identified stress-resilient subpopulations and uncovered paradigm-specific behavioral signatures, with despair-dominant patterns in CUMS (FST-MBS, r=0.61) and anxiety-centric patterns in CSDS (OFT-MBS, r=-0.74). Excluding TST did not reduce discrimination performance. ConclusionMBS provides an integrative framework for quantifying depression-like behavioral severity and heterogeneity, enabling streamlined protocols and improving the translational utility of preclinical cohorts for biomarker discovery and antidepressant screening.

Hypothalamic-pituitary-adrenal axis (HPA axis) dysregulation is a risk factor for poor mental and physical health. Animal studies indicate that DNA methylation may be one mechanism through which stress can influence the function of the HPA axis, however human studies have not identified consistent individual loci. Machine learning can be used to develop methylation profile scores (MPSs), but this method has not yet been applied to HPA axis function. Using a novel machine learning pipeline, we developed an MPS to predict the salivary cortisol response (AUCi) to the Trier Social Stress Test (TSST) from whole blood Illumina Infinium HumanMethylation 450K BeadChip data (N = 84, mean age = 34, 49% female). The MPS was associated with the cortisol response in an independent, cross-tissue cohort (N = 53, mean age = 20, 51% female), both before ({beta} = 0.33, 95% CI [0.09, 0.54]) and after a social stressor ({beta} = 0.3, 95% CI [0.09, 0.47]). Functional characterisation revealed several immune, stress, and disease-related pathways and genes, including tolerance induction to self antigen, chronic myeloid leukemia, NR3C2, and PSMB4 (putatively causal in depression). We have developed and validated a novel epigenetic biomarker for stress reactivity, identifying a set of genomic loci where DNA methylation is associated with the cortisol response. Future research could investigate if HPA axis-related MPSs could be used alongside traditional risk factors to improve clinical risk assessment.

Background: The Neuro-Immune-Metabolic-Oxidative Stress (NIMETOX) theory identified systemic dysregulation in Major Depressive Disorder (MDD), yet the precise gut-derived metabolic triggers initiating this cascade remain elusive. This study investigated the interplay between fecal short-chain fatty acids (SCFAs), systemic immune activation, and clinical phenotypes to identify a potential gut-immune biotype for MDD. Methods: Fecal SCFA profiles and serum immune-inflammatory markers were quantified in 102 patients with MDD and 38 matched healthy controls. A multistage statistical approach was employed: binary logistic regression and 10-fold cross-validated linear discriminant analysis (LDA) were utilized to evaluate diagnostic accuracy, while multivariable regression models were applied to identify robust predictors of clinical phenotypes, including the overall severity of depression (OSOD), physiosomatic symptoms, and recurrence of illness (ROI). Results: MDD patients exhibited a significant depletion of protective straight-chain SCFAs (acetate, propionate, butyrate) and an elevation in branched-chain SCFAs (BSCFAs), indicating a pathological shift from saccharolytic to proteolytic fermentation. This metabolic shift correlated with elevated acute phase-inflammatory index (API) and epidermal growth factor (EGF). A multidimensional model combining BSCFAs, acetate, API, EGF, and T helper 2 discriminated MDD from controls with adequate accuracy (AUC = 0.874). Furthermore, elevated BSCFAs and decreased protective SCFAs strongly predicted higher OSOD, more severe physiosomatic symptoms, and increased ROI. Notably, 5-Hydroxytryptamine receptor 1A agonists were independently associated with elevated BSCFAs. Conclusion: MDD is characterized by a distinct gut-immune biotype tightly linked to toxic proteolytic gut fermentation. This metabolic-immune fingerprint offers an objective diagnostic tool and highlights the need for microbiome-targeted interventions in precision psychiatry.

Stressful events are a leading factor in development of depression. The medial prefrontal cortex (mPFC) is strongly associated with depression etiology and exposure to uncontrollable stressors results in synaptic dysfunction and loss. Learned helplessness is a behavioral paradigm that measures effects of repeated exposure to uncontrollable, inescapable stress on later responses to escapable stress. We therefore performed a proteomic analysis of mPFC synaptosomes in a mouse learned helplessness model to identify molecular changes that could contribute to functional consequences of inescapable stress. Male and female mice were evaluated at baseline and following exposure to escapable or inescapable stress followed by an active avoidance test. Label-free mass spectrometry followed by pathway and protein-protein interaction network analyses identified alterations in signaling pathways involved in energy metabolism, neurotransmitter signaling, and protein shuttling. Furthermore, phosphoproteomics revealed alterations related to synaptic function, neurotransmitter signaling and protein internalization, as well as changes in activity of kinases previously identified as mediators of antidepressant efficacy (GSK3B) and receptor internalization (ADRBK1). We more deeply examined alterations in the Acetylcholine Receptor Signaling Pathway, and identified muscarinic receptor proteins (Chrm1, Chrm2, Chrm4) and key proteins involved in their translocation to and from the membrane. These results identify substantial changes in the mPFC proteome following exposure to inescapable stressors. In addition, mPFC muscarinic cholinergic signaling is well placed to mediate responses to an inescapable stressor. This proteomic study will be useful in guiding studies of human mPFC relevant to depression. Data are available via ProteomeXchange with identifier PXD073765.

Anxiety and depressive disorders impose a major global burden, prompting interest in non-pharmacological interventions that may influence affective processes. Music exposure has often been reported to affect anxiety- and depression-like behaviors, but preclinical findings remain heterogeneous and have not been quantitatively synthesized. Prior work has also focused almost entirely on mean behavioral responses, largely overlooking inter-individual variability as a biologically meaningful dimension. We conducted a preregistered systematic review and multilevel meta-analysis of experimental studies testing music exposure in laboratory rodents. Following PRISMA and PRISMA-EcoEvo guidelines, we synthesized 298 effect sizes from 20 studies using multilevel models to account for non-independence among effect sizes. We quantified effects on mean behavior with the log response ratio (lnRR) and effects on variability with the log variability ratio (ln V R). Overall, music exposure was associated with a statistically significant reduction in anxiety- and depression-like behaviors, corresponding to an average decrease of about 18% relative to controls. This mean effect was detected across outcome types and life stages despite substantial heterogeneity. By contrast, music exposure did not produce a statistically significant overall change in inter-individual behavioral variability. Instead, variability responses were context dependent: behavioral assay type and music meta-genre significantly moderated ln V R, with anxiety-like assays tending to show increased variability and depression-like assays tending to show reduced variability under music exposure. These results suggest that music exposure reliably shifts average affect-related behavior without uniformly changing behavioral stability across individuals. Because the evidence comes mainly from short-term exposures in young adult laboratory rodents, generalization beyond similar contexts should remain cautious.

Major depressive disorder (MDD) is a highly prevalent neuropsychiatric disorder characterized by depressed mood, feelings of sadness, loss of interest, and reduced pleasure related to daily activities. The clinical etiology of depression has been extensively studied, with research indicating biological, social, and psychological factors related to onset of depressive symptoms. Despite increased knowledge related to MDD, there is no tangible biomarker developed for MDD. Neuroimaging modalities such as single photon emission computed tomography (SPECT) have been utilized to characterize regional cerebral perfusion (rCBF). Functional dysconnectivity in depressed patients have been examined, with depressed individuals showing elevated depression scores and decreased rCBF in cognition and executive functioning networks. While SPECT can be utilized to monitor rCBF changes with respect to symptom severity, it alone cannot be utilized to develop a potent biomarker. Advanced multivariate methods such as independent component analysis (ICA) have been used to visualize disconnected functional patterns across disorders including depression and schizophrenia. Given no current SPECT studies examine transdiagnostic clinical profiles, the current study aims to bridge this gap. We utilized the 68 NeuroMark SPECT template across six patient groups. Factor scores investigating three key symptoms of depression: worry/rumination, moodiness, and social disinterest, and measured the loading parameter strength (i.e. component expression for each NeuroMark domain/subdomain) across the 68 components were examined. We identified significant relationships between symptoms and frontal, triple network, sensorimotor, and visual components across the three symptom profiles. Future studies should examine these trends across larger sample sizes, and increased clinical samples.

Autism Spectrum Disorder (ASD) is a heterogenous condition that has no biologically relevant subtypes yet. Here, we utilized a multidimensional approach considering social deficits in ASD alongside negative valence and empathy dysfunction to distinguish ASD from Neurotypicals (NT) and to generate ASD subtypes using machine learning approaches. 114 subjects were analyzed, with 70 being NT and 44 ASD, all male with an IQ greater than 70, with 5 domains of personality (NEO-PI-r) and Reading the Mind the Eyes Test (RMET) scores included in the main classifier. We then used a multitude of behavioral (such as IQ, Broader Autism Phenotype, Autism Quotient, Interpersonal Reactivity Index) and clinical measures such as Autism Diagnostic Interview-Revised (ADI-R) alongside biological methods including DNA methylation of OXTR gene and resting-state functional connectivity (rsFC) to validate the putative subtypes. 30 ASD who received IN-OXT in a randomized, placebo-controlled, within-subject design and 17 new NT were part of the rs-FC analysis. A random forest tree algorithm was used to classify NT and ASD and Shapley Additive Explanation Values were used to describe the model and to cluster ASD subtypes using K-Means clustering. Three subtypes were generated with two of them being highly distinctive in behavioral and brain functional traits. One subtype named NASA (or Negative Affect and Social Aloofness) was characterized by high Neuroticism and Low warmth alongside lower rsFC between networks involved in social cognition, self-awareness, and sensory processing, such as Superior Temporal Sulcus and Sensorimotor Network; or ACC/Insula with visual cortex, Posterior Cingulate Cortex and visual cortex. The second subtype NADR (Neurocognitive and Affect Dysregulation with Resistance to Change) was characterized by higher DNA methylation of OXTR, hyperconnectivity between default mode network, reward areas and inferior frontal and fusiform networks. NADR has more cognitive difficulties and higher ADI-R scores as well as higher Neuroticism, higher personal distress, higher rigidity and lower openness. In a mixed model analysis, we found that IN-OXT in a dose dependent manner impacted NASA subtype by modulating rsFC between PCC and cerebellum and between Brainstem/Cerebellum and Parietal cortex to probably enhance social cognition and to reduce negative valence in this subtype.

Polygenic risk scores (PRS) have shown increasing utility for risk stratification across complex diseases, but for psychiatric disorders such as bipolar disorder (BD), current PRS explain only a fraction of disorder liability (~1-9%), with predictive performance further diminished in non-European populations and real-world clinical cohorts. To explore the potential of integrating social and environmental risk factors alongside genetic liability to improve risk prediction, we evaluated the relationship between a PRS for BD (PRSBD) and six social risk measures - perceived stress, discrimination in medical settings, neighborhood social cohesion, perceived neighborhood disorder, cost-related medication nonadherence, and adverse childhood experiences - to BD case status in 115,275 participants (7,000 cases; 108,275 controls) from the All of Us Research Program. PRSBD was associated with BD case status across ancestry groups, though liability-scale variance explained was attenuated relative to what has been reported for curated research cohorts (R2 = 1.86% in European, 0.60% in African, 1.65% in Latino/Admixed American ancestries). Each social risk factor tested exhibited a larger effect size than PRSBD, with perceived stress (OR = 2.05 per SD) and adverse childhood experiences (OR = 2.68 for [&ge;]4 ACEs) demonstrating the strongest associations. Individuals in the lowest genetic risk decile with high social burden exhibited BD prevalence comparable to or exceeding those in the highest genetic risk decile with low social burden. These findings demonstrate the substantial explanatory power of social risk factors and support the development of integrated genetic-social risk frameworks for more accurate and equitable psychiatric risk prediction.

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different symptom profiles, potentially reflecting distinct underlying mechanisms. Identifying common patterns of symptoms using data-driven approaches could help clarify the heterogeneity of depression. Furthermore, examining the sociodemographic and lifestyle characteristics, health status, and polygenic scores of individuals with specific symptom profiles may offer insights into underlying risk factors. Unsupervised machine learning models were applied to large-scale data from the UK Biobank. Independent groups of individuals were assessed at two time points (the Mental Health Questionnaire: Q1; and the Mental Well-being Questionnaire: Q2) and reporting on historical or current episodes of depression. Two machine learning models, multivariate Bernoulli-mixtures and agglomerative hierarchical clustering, were used to identify common sets of symptoms and cluster individuals by symptom similarity. Consistency of results was examined between Q1 and Q2 and between clustering models. Associations between cluster membership probabilities and sociodemographic and lifestyle factors (sex, age, body mass index, smoking status, ethnicity, and deprivation), eight health conditions, and polygenic scores for bipolar disorder, schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) were examined using regression models. Symptom clusters were highly consistent across Q1 and Q2 (mean correlation > 0.81) and between machine learning models (Rand Index > 0.83). Clusters aligned with the existing clinical subtypes, atypical and melancholic depression, alongside other potentially novel clusters reflecting a range of different symptom profiles. Atypical clusters (hypersomnia with weight gain) appeared in both Q1 and Q2 and were associated with younger age and higher body mass index. Distinct clusters combining insomnia, weight gain, and having thoughts of death were associated with asthma, suggesting potential inflammatory dysregulation. Further clusters were characterised by psychomotor changes and showed strong associations with Parkinsons disease, both before and after the mental health questionnaire was conducted. These findings highlight robust and clinically meaningful symptom subtypes within depression and support the use of data-driven approaches to improve diagnostic refinement and inform personalised treatment strategies.

Major Depressive Disorder (MDD) is increasingly conceptualized as a neuroimmune-metabolic-oxidative stress (NIMETOX) condition, partly driven by environmental stressors. While new antidepressant strategies have emerged to target NIMETOX pathways, the mechanism by which chronic stress reshapes metabolic regulation-particularly in the absence of clinically overt metabolic disease-remain poorly understood. Using a 6-week chronic unpredictable mild stress (CUMS) paradigm in metabolically healthy male and female mice, we evaluated depressive-like behaviors alongside circulating glucose and metabolic hormones, including insulin, resistin, Glucose-dependent Insulinotropic Polypeptide (GIP), Glucagon-like Peptide-1 (GLP-1), glucagon, ghrelin, leptin, and Plasminogen Activator Inhibitor-1 (PAI-1). We further assessed whether simvastatin, curcumin, S-adenosyl methionine (SAMe), or pyrrolidine dithiocarbamate (PDTC), an NF-{kappa}B inhibitor, normalize CUMS-induced metabolic and behavioral alterations, using fluoxetine as a reference antidepressant. CUMS induced a hypoinsulinemic state accompanied by enhanced basal insulin sensitivity and reduced insulin resistance, while central appetite regulation and adipose inflammatory profiles remained preserved. Indices of lower insulin resistance were strongly associated with sucrose preference and immobility time. Glucagon and PAI-1 showed positive associations with novel object recognition performance, whereas GIP and leptin were inversely related to open-field activity. Glucose levels correlated positively with rearing behavior. Despite significant improvements in depressive-like behaviors, none of the pharmacological interventions normalized the stress-induced metabolic hormone profile. Marked sex differences were observed, with females displaying a catabolic, immune-ready phenotype and males showing a relative anabolic bias. These findings indicate that CUMS induces a non-pathological, adaptive metabolic hormone reprogramming rather than metabolic dysfunction, supporting the interpretation of stress-related metabolic changes as resilient adaptations within the NIMETOX framework.

Major depressive disorder (MDD) is a common psychiatric illness with a high proportion of patients being nonresponsive to therapy. Selective serotonin reuptake inhibitors (SSRI) are widely prescribed for treating depression. Chronic SSRI administration is needed for therapeutic effects, a process implicating in part, increased neurogenesis in the hippocampus. Recent genome wide association studies (GWAS) identified the DrD2 locus, which encodes the dopamine D2 receptor (D2R) as a major risk factor in MDD. Here we demonstrate that behavioural effects associated with chronic administration of the SSRI drug fluoxetine and its accompanying neurogenic effects require D2R. Administration of fluoxetine to congenital D2R-knockout mice, or co-administration of the antidepressant with the antipsychotic D2R antagonist drug haloperidol prevented the neurogenic effects of fluoxetine. Furthermore, while acute behavioural responses to fluoxetine did not require D2R, this receptor was essential for the behavioural effects of chronic fluoxetine. The neurogenic impact of chronic fluoxetine was further associated with beta-arrestin 2-mediated signalling and the hippocampal regulation of the pro-neurogenic factor BDNF. These results support a role of D2R in regulating the therapeutically relevant chronic effects of fluoxetine on mood, BDNF signalling, and associated hippocampal neurogenesis. Furthermore, our findings suggest an unappreciated interaction between genetic risk for MDD and treatment responsiveness as well as a negative interaction between SSRIs and antipsychotic drugs in the regulation of hippocampal neurogenesis.

New treatments for depression are needed that combine robust efficacy with improved scalability. Although psilocybin has demonstrated antidepressant effects in Phase 3 clinical trials, some of its psychedelic effects limit tolerability, necessitating administration in highly supervised clinical settings, and thus motivating development of serotonergic therapeutics that preserve antidepressant efficacy while reducing the acute psychedelic experience. We combined psilocybin with a phosphodiesterase-9 inhibitor (PDE9i), which raises cyclic GMP levels, and observed substantial reduction in the mouse head twitch response (HTR) -- a proxy for 5-HT2A receptor-mediated psychedelic-like behavior in rodents -- suggesting attenuation of acute psychedelic effects. Significantly, rescue of chronic stress-induced depressive-like behavior by psilocybin was maintained with the coadministration of PDE9i. Proteomic analysis of medial prefrontal cortex (mPFC) synaptosomes showed that the combination of PDE9i and psilocybin enhanced synaptogenesis pathways relative to psilocybin alone, while reducing pathways involved in G protein-coupled receptor (GPCR) signaling. Together, these results suggest that the combination of PDE9i and psilocybin may be a promising direction for psychedelic treatment, and point towards molecular pathways that dissociate acute psychedelic and antidepressant responses.

Early life stress (ELS) in primates alters dopamine function, contributing to addiction, hyperactivity, cognitive deficits, aggression, and social subordinance. To assess whether dopamine receptor densities are affected by ELS, male juvenile rhesus monkeys (Macaca mulatta) were either mother-reared (MR, N=6) in a semi-natural environment or nursery-reared (NR, N=6) with peers in a laboratory. At 1 [1/2] years of age, subjects were sacrificed and the left prefrontal cortex (PFC), striatum (caudate and putamen), nucleus accumbens (NAcc), and claustrum (CLA) were explored through quantitative autoradiographic studies of dopamine receptor-1 (DRD1) and -2 (DRD2) conducted using [125I]-(+)-SCH 23982 and 125I-Epidepride, which have high affinity and selectivity for DRD1 and DRD2, respectively. No group differences emerged in striatal or NAcc receptor binding. However, MR monkeys exhibited significantly greater DRD1 binding in the left orbital PFC and significantly greater DRD2 binding in both the left medial PFC and right CLA compared to NR. These findings implicate the medial PFC (stress vulnerability, cognition), orbital PFC (reward valuation), and CLA (anxiety modulation) as critical sites disrupted by maternal deprivation. Therefore, we propose that nursery-rearing induces a hypodopaminergic prefrontal-claustral ecophenotype, underlying the cognitive, affective, and social impairments observed in NR monkeys.

Cognitive function, psychological processes, mental states, and behaviors are key dimensions of human subjective experience that separately relate to mental disorders across diagnostic categories. However, whether these dimensions are linked to common or distinct brain morphological patterns that convey risk or resilience for psychiatric disorders remains unclear. The current study is a longitudinal investigation on 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study aged 9-10 years at baseline. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of cortical morphology (4 metrics: surface area, cortical and subcortical volume, cortical thickness, and sulcal/gyral depth) with multidomain behavioral assessments including cognitive scores and psychological measures indexing motivation, impulse control, mental states, and behaviors across a normative continuum from healthy to pathological. Across morphological measures, we identified a robust latent brain structural variate that correlated positively with cognitive performance and negatively with psychological measures indexing greater psychology. Notably, higher scores on this brain variate reflected larger cortical surface area and cortical volume--especially in the temporal gyri--together with a posterior-anterior gradient in cortical thickness, showing relatively greater thickness in occipital, parietal, and temporal cortices and lower thickness in cingulate and frontal regions. This brain variate and the related cognitive-psychological-behavioral variate remained stable at the 2-year follow-up, demonstrating temporal consistency. Importantly, the brain variate showed a dose-dependent relationship with the cumulative number of psychiatric diagnoses assessed concurrently and at 2-year follow-up, with lower brain variate scores being associated with higher numbers of comorbid diagnoses. In addition, the brain scores were associated with longitudinal transitions between healthy and diagnosed states over the 2-year study period, in which lower scores at baseline were associated with persistent psychiatric diagnoses whereas higher scores at baseline were associated with persistent healthy states, suggesting that the brain scores capture a vulnerability- resilience continuum for psychopathology. By revealing shared brain structural substrates across conventional diagnostic boundaries, these findings advance the neurodevelopmental understanding of psychiatric disorders and highlight the potential utility of morphology-informed approaches for early screening and intervention in youth.

Nicotine use disorder shows heterogeneity in treatment response, potentially reflecting differences in underlying neural circuitry, particularly in the presence of depression. We examined real-time neural dynamics during nicotine inhalation in two chronic users - one with depression and one without - using simultaneous hippocampal recordings from responsive neurostimulation (RNS) electrodes and scalp EEG. Oscillatory activity and hippocampal-cortical connectivity were analyzed in relation to mood and craving. Oscillatory activity tracked mood in the non-depressed individual but was attenuated or reversed in the depressed individual, suggesting reduced reward-related neural responsiveness. In contrast, both participants showed reduced alpha hippocampal-cortical connectivity following nicotine use, suggesting a shift from reward-seeking to reward and relief processing. These findings support a network-based framework of nicotine-driven neural dynamics and provide preliminary evidence that depressive status may modulate these processes. Although limited to two cases, this work highlights the potential for identifying neurophysiological subtypes of nicotine users and informs future efforts toward personalized treatment approaches.

Negative cognitive biases in depression are more pronounced in females than in males. This sex difference emerges during adolescence, a sensitive developmental stage when chronic stress exposure increases the risk of depression in adulthood. The neurobiology linking adolescent stress to sex-specific cognitive bias and resting-state network reorganization in adults remain poorly understood. The study aimed to investigate the longitudinal effects of chronic restraint stress (CRS) during adolescence on cognitive bias and functional connectome in emerging adulthood. 28 Wistar rats (sex-balanced; aged five weeks on arrival) were trained on a judgment bias task with distinct tactile cues signalling differential rewards. Cognitive bias was quantified from responses to ambiguous probe trials. Following training, animals were randomly and equally assigned to CRS or control groups (sex-balanced). Offline resting-state functional MRI scans were conducted at adolescent baseline (pre-CRS) and again in adulthood (post-CRS), followed by probe trials to assess neural and behavioural changes. Following CRS, females showed a greater tendency to shift toward negative bias than males (ratio of odds ratio=3.67). Furthermore, CRS significantly reduced functional connectivity between the left cerebellar-auditory and hypothalamic-thalamic networks only in females. Repeated-measures correlation between cognitive bias and network connectivity were not statistically significant across sex-by-group strata, potentially due to offline imaging and small sample size. However, intra-individual association revealed sex-specific trends, with CRS females showing moderately positive correlations and CRS males exhibiting a weak negative association. The results could inform stratified connectome-based interventions targeting adolescent stress exposures to potentially reduce the risk of adult depression. Six keywords: Resting-State Functional MRI, Chronic Restraint Stress, Judgement Bias, Open Field Test, Sex Differences

A growing number of studies point to a key role of the amygdala in Autism Spectrum Disorders (ASD). The amygdala is involved in several processes in ASD including emotional valence, facial recognition, regulation of social learning, empathy, and anxiety. Brain imaging and postmortem studies demonstrate altered amygdala development in children with ASD, associated with impairment in social behavior and anxiety. There is limited information regarding the molecular pathology of the amygdala in children with ASD. We conducted RNAseq profiling on postmortem amygdala samples from male children (4-14 yrs old) with ASD (n=8) and normotypic male children (n=6). Furthermore, we conducted drug repurposing analysis to identify compounds predicted to reverse the transcriptomic signatures identified in order to identify potential therapeutic targets for development of early intervention treatments. Full transcriptome gene expression profiling implicated molecular pathways involved in neuroimmune signaling, glycogen and carbohydrate metabolism, matrix metalloproteases, neurodevelopment, estrogen receptor signaling, and synaptic signaling. Targeted pathway analysis of the top 10% of differentially expressed genes implicated pathways involved in extracellular matrix organization, immune signaling, and synaptic signaling. Our drug repurposing analysis identified sleep modifying compounds and anti-inflammatory compounds including COX2 and GSK3 inhibitors amongst the top predicted therapeutic compound classifications. PDGF receptor tyrosine kinase inhibitors were identified as a top potential therapeutic mechanism of action. Our results point to alterations in immune signaling, extracellular matrix organization, and synaptic signaling in the amygdala of children with ASD. Furthermore, our results identified a number of potential therapeutic drug targets for development of early intervention strategies.

Background and Hypothesis: Extracellular vesicles (EVs) are phospholipid bilayer vesicles released from cells containing proteins, lipids and nucleic acids derived from the parent cell. Alterations in miRNA expression within blood-derived EVs have been proposed as potential biomarkers of disease. Specifically, identification of differentially expressed miRNAs in patients with schizophrenia (SZ) compared to healthy individuals could be used as a "miRNA signature" to aid in diagnosis and treatment. We therefore aimed to identify differentially expressed miRNAs in plasma-derived EVs between people living with SZ and healthy controls and to correlate miRNA levels with SZ-relevant clinical measures. Study Design: Plasma-derived EVs were isolated from a cohort of 33 individuals with SZ and 34 controls. Expression of 84 miRNAs was examined using a RT-qPCR panel. Study Results: Three miRNAs (hsa-miR-30e-5p, hsa-miR-103a-3p, hsa-miR-200b-3p) were differentially expressed between controls and patients. Gene ontology analysis of putative target genes shared between these miRNAs revealed enrichment of biological process terms related to neurogenesis. Analysis of miRNA expression compared to clinical measures showed that hsa-miR-103a-3p expression was associated with working memory and negatively correlated with white matter integrity in the combined patient-control group. Conclusions: We have identified a miRNA signature for SZ in plasma-derived EVs and shown for the first time that hsa-miR-30e-5p expression is significantly increased in plasma-derived EVs in SZ. The genetic links between differentially expressed miRNAs and neurogenesis, along with the correlations of hsa-miR-103a-3p with working memory and white matter integrity may underlie the functional importance of altered expression of the identified miRNAs in SZ.

Background: Adolescence is a critical period of neurodevelopment with the emergence of chronic medical conditions and increasing exposure to long-term medications. P-tau217 is a sensitive blood-based biomarker of neuropathology in older adults, yet its developmental behavior and susceptibility to common clinical factors in youth are unclear. Here we tested whether p-tau217 varies with age, comorbidity, or medication use during adolescence; and whether collection method (venous vs Tasso+ capillary) yields comparable concentrations. Methods: In an adolescent cohort, plasma p-tau217 was measured by Simoa-X. Paired venous and Tasso+ capillary samples were also analyzed from adult volunteers for methodological comparison Results: In adolescents (n=41; mean age 16{+/-}2.6 years), p-tau217 did not correlate with age or BMI z-score and did not differ by psychiatric, cardiometabolic, or gastrointestinal comorbidity, nor by corresponding medication use. In contrast, p-tau217 concentrations were >10-fold higher in Tasso+ capillary plasma than venous plasma, a discordance replicated in paired adult samples. Conclusion: Plasma p-tau217 appears physiologically stable across common clinical variables in adolescence, but highly sensitive to biospecimen collection method. Venous and Tasso+ capillary plasma should not be directly compared or pooled until methodological differences are resolved. These data provide a developmental baseline and critical methodological caution for pediatric neuroscience and decentralized biomarker studies.

Background: Major depressive disorder (MDD) is associated with altered brain structure and evidence of accelerated brain aging. However, previous studies have been limited by clinical samples with mixed medication status and multiple mood states, modest sample sizes, small percentage of MDD individuals older than 65 years of age, and/or reliance on summary-level data. Methods: Harmonized T1-weighted MRI from MDD (n = 645), all medication-free and in a current depressive episode, and matched healthy controls (n = 645), segmented into 145 regional volumes, from 11 sites in COORDINATE-MDD consortium. Brain age gap (BAG) was estimated using gradient boosting regression with nested cross-validation. Group differences in BAG (and age-corrected BAG [cBAG]) were examined across age strata. Regional contributions were evaluated using Shapley Additive exPlanations. Results: MDD was associated with significantly elevated cBAG compared with healthy controls (mean difference + 2.01 years). Age-stratified analyses showed no differences before mid-30s, with progressively larger gaps thereafter, reaching +6.85 years in MDD aged 55 and older. cBAG differed across neuroanatomical phenotypes associated with differential antidepressant response, cognitive impairment, increased adverse life events, increased self-harm and suicide attempts, and a pro-atherogenic metabolic profile. Key contributing regions included lateral and medial prefrontal regions, middle temporal gyrus, putamen, supplementary motor cortex, central operculum, and cerebellum. Conclusions: Accelerated structural brain aging in MDD is age-dependent and is most pronounced in a neuroanatomical phenotype associated with worse key clinical outcomes. The findings support neuroprogression models of MDD while demonstrating that cBAG is not a uniform feature of MDD and seem to be more strongly expressed in a specifically clinically vulnerable disease phenotype.