Molecular studies of brain receptors and transporters have typically relied on recombinant systems, limiting insight into their organization in native tissue. Here, we develop nanobody-based immunoprecipitation coupled with native mass spectrometry to interrogate endogenous protein assemblies from post-mortem mouse and human brain sections. We exemplify our approach by characterizing the synaptic proteins VGluT1 and mGluR2. From a single mouse brain, we discover mGluR2/3 heterodimers, alongside mGluR2 homodimers. Considering regions of human brain heterodimeric mGluR2/3 is highly abundant in the OFC and sgACC (~70% and 50%, respectively) and forms regional-specific interactions with additional synaptic proteins. In a modest cohort of biobanked human tissue, associated with depression and suicide, we find increased mGluR2/3 in the OFC. Consistent with this, mice exhibit similar associations between heterodimer levels and stress-susceptibility. Overall, our approach provides a direct means for establishing molecular-behavioural links at the level of receptor organization in brain.

Anhedonia emerges in adolescence, has putative substrates in neural reward and dopamine systems, and is a hallmark of poor course in depression. Psychoneuroimmunology models suggest altered immune and reward systems may jointly underlie its development. When dopamine availability (rather than function) is considered, immune activation may be understood as a moderator amplifying how lower dopamine levels translate into motivational deficits. This cross-sectional study analyzed data from 55 youth with current depression (Mage=21.4 years; Female=75%). Plasma was assayed for relative quantification of 92 immune proteins, followed by dimensionality reduction via principal component (PC) analysis, forming five PC scores. Dopamine availability was indexed by basal ganglia tissue iron, quantified by MRI R2', a measure detectable in youth. Anhedonia was assessed using the Snaith-Hamilton Pleasure Scale (SHAPS), thought to capture overall anhedonia, and Positive Valence Systems Scale (PVSS), which provides overall, anticipatory, and consummatory anhedonia scores. PC1 scores-whose loadings indicated broadly distributed immune protein elevations consistent with general immune activation-moderated the relationship between basal ganglia dopamine availability and anhedonia: At higher PC1 levels, lower dopamine availability was linked to greater overall anhedonia (as measured by SHAPS) and anticipatory anhedonia, but not to consummatory anhedonia. Lower PC2 scores were associated with greater PVSS overall and consummatory anhedonia. Analyses examining individual basal ganglia regions suggest potential differences in dopaminergic-immune interactions across these regions, with effects most consistently observed in the putamen. Findings highlight nuanced neuroimmune pathways underlying anhedonia components in youth and support inflammation-based models of depression emphasizing dopaminergic-immune interplay.

Purpose: Substantial evidence supports the effectiveness of implanted Vagus Nerve Stimulation (VNS) in the management of unipolar difficult-to-treat depression (DTD). While the treatment is included in several national and international guidelines, there is limited information to guide clinicians regarding patient selection and use of VNS in clinical practice. Patients and Methods: A group of 32 experts in the use of VNS were identified from the main countries currently providing the treatment globally. A modified Delphi technique was used to document views on 55 statements regarding the goals, patient selection, and use of VNS treatment in routine clinical practice. Statements were rated on a 9-point Likert scale from "strongly disagree" to "strongly agree". Over the course of three rounds of voting, with statements modified based on anonymous comments from panelists, consensus agreement or disagreement was deemed if at least 75% of panel members scored a statement between 7 and 9, or 1 and 3, respectively. Results: Consensus was reached by the panel on 75% of the statements covering a wide range of issues. There was agreement that the main goals for VNS are long-term management of symptoms and improvement in quality of life, that the treatment is appropriate for all ages of patients and that there are few contraindications. Conclusion: A set of expert recommendations for the use of VNS for DTD was generated. These should be of value to clinicians to ensure current best practices are followed when considering this treatment.

BACKGROUND:While counterfactual thinking ('what could have been') guides adaptive decision-making, it remains unclear how this process is altered by the negative biases and motivational deficits characteristic of Major Depressive Disorder (MDD). METHODS:We used a sequential economic decision-making task designed to emulate a volatile stock market to assess choice behavior in adults with or without MDD (Total N=178); a subset of these participants completed the task during functional MRI (N=53). The task allowed participants to make either positive ('invest') or negative ('short') bets, under either positive or negative contextual valence, defined by whether the immediately preceding stock price change was positive or negative. Fictive errors were defined as the difference between realized and best-possible outcomes. RESULTS:Across the full cohort, group differences in behavioral adjustments to fictive error signals emerged exclusively under negative contextual valence, when stock prices decreased. Compared with controls, participants with MDD showed heightened sensitivity to invest-and-loss fictive errors, reflected in a greater reduction in subsequent bets (interaction beta = -0.63, p < .001), but blunted adjustment to short-and-gain fictive errors (beta = -0.86, p < .001). In the imaging cohort, blunted short-and-gain adjustment was accompanied by heightened anterior cingulate (ACC) activity and attenuated ventromedial prefrontal (vmPFC)-to-ACC coupling in MDD. vmPFC activity following negative market returns also tracked depression symptom severity. CONCLUSIONS:Depression selectively disrupts the use of counterfactual outcomes to guide adaptive choice under negative contextual valence, implicating altered frontocingulate function in maladaptive decision-making.

OBJECTIVE:This open-label clinical trial primarily examined the safety and tolerability of ketamine treatment in patients with mild cognitive impairment and Major Depressive Disorder (MCI-D). Preliminary efficacy was also explored. METHODS:The trial was conducted between November 2023 and March 2025. Patients with MCI-D and moderate to severe symptom levels of depression received a single intravenous ketamine infusion (0.5 mg/kg). Safety and tolerability were evaluated. Antidepressant efficacy was also explored by evaluating change in the Montgomery-Asberg Depression Rating Scale (MADRS) scores from baseline to 24 hours after the infusion. RESULTS:Thirteen eligible patients received treatment and completed study procedures. No serious adverse events were reported and all participants tolerated study procedures. The treatment was associated with large-magnitude improvement in depression symptom severity from baseline (mean MADRS = 27.4[SD = 6.4]) to 24 hours after the infusion (mean MADRS = 5.7[SD = 4.7]) in all patients with improvements in MADRS persisting for 8 individuals up to 1 month after treatment (mean MADRS = 12.1[SD = 6.9], ≥50% improvement). CONCLUSIONS:Findings from this open-label clinical trial support the safety and tolerability of ketamine treatment is in individuals with MCI-D. Ketamine may also be effective for improving depression in this population. Large-scale randomized controlled trials are needed to determine the efficacy and potential cognitive effects of this promising treatment in this patient population.

BACKGROUND:Among individuals with neuropsychiatric disorders, those with bipolar disorder (BD) have one of the highest rates of suicide with this risk even further elevated in the Veteran population. The assessment of suicide risk is clinically challenging, but one of the best predictors of a future suicide attempt is having a prior attempt. Although prior studies have implicated proteomic abnormalities separately in BD and among those with a suicide attempt history, little is known regarding their combined contribution to suicide risk in BD and/or their relationship to the brain white matter. METHODS:We analyzed 368 proteins targeting disease and pathway specific biomarkers using the OLINK platform in Veterans with BD either having (BD/SA+) or not having (BD/SA-) a past suicide attempt (SA) history and healthy control (HC) Veterans. We used neurite orientation and dispersion density imaging to derive average indices of fiber coherence (i.e., orientation dispersion index, ODI), axon and dendrite density (i.e., neurite density, NDI) and extracellular free-water indicative of diffusion (isotropic volume fraction, ISOVF) within the brain white matter. RESULTS:Three proteins (NEP, KYNU and IL-12B) had significantly lower expression in both the BD/SA+ and BD/SA- groups compared to HC. In the combined group of individuals with BD, lower expression of these 3 proteins correlated significantly with lower neurite density across the brain white matter. Two proteins had significantly higher (i.e., AZU1 and CEACAM8) and one significantly lower (i.e., MMP7) expression in the BD/SA+ group compared to both the BD/SA- and HC groups. In the BD/SA+ group, higher protein expression of AZU1 and CEACAM8 was associated with a higher ISOVF in the brain white matter. Pathway analysis indicated enrichment of innate immune, neutrophil degranulation, and cytokine signaling pathways among the differentially expressed proteins. DISCUSSION:In this exploratory study we found that among all individuals with BD, proteomic abnormalities were associated with an index of neurite density (i.e., axonal and dendritic packing) within brain white matter, whereas in BD/SA+ abnormal protein expression was associated with a higher ISOVF, potentially reflecting inflammation, edema and/or atrophy. Immune-related biology may represent a mechanistic link between peripheral inflammatory signals and alterations in brain microstructure among individuals with BD at heightened risk of suicide, but should be considered preliminary until replicated in larger cohorts.

Importance:The effectiveness of pharmacogenetics to guide prescribing of selective serotonin reuptake inhibitors (SSRIs) for depression remains unclear, despite the well-established association between SSRI pharmacokinetics and genetic variation. Objective:To determine whether pharmacogenetic-guided prescribing of SSRIs improves treatment response in patients with depression. Design, Setting, and Participants:The ADOPT PGx (A Depression and Opioid Pragmatic Trial in Pharmacogenetics) Depression pragmatic randomized clinical trial was conducted from August 10, 2021, through April 27, 2024, at primary care, psychiatry, or family medicine clinics at enrolling sites throughout the US. Patients were aged 8 years or older and had experienced depression for 3 months or longer. Intervention:Patients were randomized to genotype-guided SSRI prescribing (intervention group) or usual care (control group). Actionable drug metabolism phenotypes were defined as those for which pharmacogenetic clinical guidelines recommend alternative medication selection or dose adjustment. Main Outcomes and Measures:The primary outcome was change in Patient-Reported Outcomes Measurement Information System (PROMIS) depression T scores at 3 months among patients with the actionable phenotype. Secondary end points included adverse effect severity of SSRIs at 3 months and depression remission (measured with PROMIS depression scores and Patient Health Questionnaire-8 [PHQ-8] scores) at 6 months. Results:This study of 1460 patients included 1239 adults (84.9%) (mean [SD] age, 40.6 [16.7] years) and 221 children (15.1%) (mean [SD] age, 14.6 [1.8] years). Most patients were female (1096 [75.1%]). A total of 692 patients (47.4%) had an actionable phenotype; 351 (50.7%) were assigned to the intervention, and 341 (49.3%) were assigned to usual care. At baseline, 463 of the 692 patients (66.9%) reported having depressive symptoms for more than 2 years, 603 (87.1%) were receiving pharmacologic treatment, and 354 (51.2%) were receiving nonpharmacologic treatment. At 3 months, no significant differences were observed between the intervention and usual care groups in change in PROMIS depression T scores (mean [SD] change, -4.3 [8.4] vs -4.0 [8.1]; P = .68), medication adverse effect burden (mean [SD] change, 8.2 [4.3] vs 7.8 [4.5]; P = .37), or Patient Health Questionnaire-8 score change (mean [SD] change, -3.3 [5.2] vs -2.7 [4.8]; P  = .13). However, at 6 months, the PROMIS depression T-score remission rate (score ≤16) was higher in the intervention group compared with the usual care group (153 of 317 patients [48.3%] vs 122 of 310 patients [39.4%]; P = .02). Conclusions and Relevance:In this randomized clinical trial, genotype-guided prescribing of SSRIs did not improve control of depression symptoms at 3 months compared with usual care but was associated with higher depression remission rates at 6 months. These findings suggest a possible longer-term clinical benefit and indicate that future studies should focus on the durability and long-term impact of genotype-guided prescribing in the management of depressive symptoms. Trial Registration:ClinicalTrials.gov Identifiers: NCT04445792 (Master Protocol Research Program platform trial) and NCT05966155 (ADOPT PGx Depression trial).

Major depressive disorder (MDD) is a prevalent neuropsychiatric disorder associated with significant morbidity and mortality. Increasing evidence suggests that a subset of MDD patients exhibit a dysregulated immune system. However, few clinical studies have tested the efficacy of anti-inflammatory drugs in reducing symptoms of depression. In contrast, targeted immunomodulatory drugs have revolutionized the treatment of inflammatory skin disorders, such as atopic dermatitis (AD) and psoriasis. To assess the viability of a targeted treatment approach in MDD, we first compared the blood proteomic profiles of patients with MDD to those of patients with AD, psoriasis, and healthy controls (HCs). We demonstrated that the proteomic signatures of MDD patients share Th2 skewing and dysregulation of other immune/neurovascular-related proteins with AD. Next, we performed an in-silico drug repurposing analysis to test whether common biologics used in dermatology could also affect the dysregulated proteomic signature observed in MDD patients. This computational approach identified dupilumab, which targets the IL-4 receptor α subunit (IL-4Rα) and thus inhibits the Th2 axis, as significantly affecting the MDD signature by reversing the dysregulation of several inflammatory proteins related to Th2 signaling. Finally, in a mouse model of chronic social defeat stress (CSDS), we showed that pharmacological inhibition of IL-4Rα prevented stress-induced social avoidance behavior. Our findings underscore the potential role of the Th2 axis in MDD, highlighting the potential of specifically targeting Th2 as a disease-modifying treatment. Additionally, the back-translational drug repurposing strategy employed in this study may offer a novel approach to identify immunomodulatory drugs in psychiatry.

Ketamine has demonstrated rapid antidepressant efficacy in treatment-resistant depression (TRD), but clinical decision-making is challenging due to variability in individual response. Current trial-and-error prescribing practices may expose patients to ineffective treatment and avoidable adverse effects, underscoring the need for reliable predictive tools to optimize treatment selection and support personalized, evidence-based care. We developed a machine-learning model (support vector classifier) to predict antidepressant response to ketamine using pre-treatment structural MRI data. The model was trained on 99 adults with TRD given a single intravenous ketamine infusion (0.5 mg/kg). Clinical response was defined as a ≥50% reduction in MADRS scores 24 h post-infusion. Internal validation used repeated nested cross-validation, and generalizability was tested in two independent ketamine-treated cohorts (n = 51) and a saline-treated control group (n = 49). Among ketamine-treated participants, 52 (52.5%) responded to treatment. The model achieved a balanced accuracy of 72.2% (sensitivity = 72.3%, specificity = 73.1%, AUC = 0.72) in the discovery sample and 60.0% (p = 0.01, AUC = 0.65) in external validation. Greater gray matter volume in frontal regions predicted response, whereas greater cerebellar volume predicted non-response. Performance dropped to chance in the saline cohort (BAC = 41.1%, AUC = 0.45), supporting pharmacologic specificity. These findings present the first machine-learning model for the prediction of ketamine response in TRD using structural neuroimaging and highlight its potential utility for stratified treatment planning and biomarker-informed interventions while providing mechanistic insight into neuroanatomical predictors of antidepressant response.

The majority of individuals experience potentially traumatic events in their lifetimes, yet most do not develop severe outcomes such as post-traumatic stress disorder (PTSD). To understand this discrepancy, a growing area of study is psychological resilience, defined as the ability to adapt in response to adversity. Altered connectivity among large-scale brain networks—default mode (DMN), central executive (CEN), and salience (SN)—is well-documented in PTSD. However, less established is these networks’ functioning in highly resilient, such as World Trade Center (WTC) responders who have never developed WTC-related psychopathology despite substantial trauma exposure. We used resting-state fMRI data from a parent study of WTC responders (N=89) to investigate effective network connectivity (i.e., influence of network X on network Y) via Granger causality analysis. Planned contrasts compared three groups: WTC-related PTSD (n=29), high WTC-exposed responders with no psychopathology (“Highly Resilient”, n=32), and lower WTC-exposed controls (n=28). We also examined whether self-report and cognitive measures were correlates of effective network connectivity. We identified a group difference in effective connectivity, with greater influence of CEN on SN in Highly Resilient responders versus Lower WTC-exposed responders. Higher CEN-to-SN connectivity was also associated with better cognitive function in the full sample and with higher estimated IQ in the Highly Resilient group. In conclusion, greater top-down control of SN by CEN may represent a compensatory adaptation after substantial trauma exposure in highly resilient individuals, associated with better cognitive function.

IntroductionMajor depressive disorder (MDD) affects approximately one in six individuals over their lifetime, with many patients experiencing treatment-resistant depression, characterized by inadequate or insufficient response from at least one antidepressant treatment. Current classification strategies for depression rely primarily on clinical assessment of symptom severity, which are prone to reader bias and test-retest variability. Moreover, these symptom-based subtypes have shown limited utility in predicting treatment response. This study introduces a data-driven, non-biased classification framework that integrates clinical features with high-resolution magnetic resonance imaging (MRI)-derived features. Using canonical correlation analysis (CCA) and hierarchical clustering, the approach identifies distinct subtypes of MDD, offering a more objective and potentially predictive alternative to traditional methods.Materials & methodsSixty-four participants with MDD currently experiencing a major depressive episode and not currently undergoing treatment completed a battery of 11 clinical symptom severity assessments and scanned with 7T T1-weighted MRI with parameters: TE/TR = 3.62/6000 ms, 320x240x240 array size with 224x168 mm2 field-of-view (FOV) for voxel dimensions of 0.7 mm3 isotropic. The images were automatically segmented using the FreeSurfer 6.0 package and 87 resulting imaging features, along with 11 clinical measures were processed through CCA to derive highly-correlated clinical-imaging phenotypes. An analysis using the Sillhouette and other methods determined an optimal number of clusters for this dataset. Participants with MDD were plotted on axes consisting of highly correlated clinical-imaging phenotypes derived from CCA and subsequently grouped through hierarchical clustering.ResultsCCA identified three highly correlated (r > 0.9) clinical-imaging variable pairs. The first, an anhedonia-related phenotype, showed high loadings from anhedonia severity and brainstem features. The second phenotype was associated with childhood trauma and anhedonia, with the right frontal pole as the primary imaging feature. The third phenotype linked general distress and perceived stress with the right superior temporal lobe. Hierarchical clustering along these canonical axes revealed two distinct clusters: one characterized by high childhood trauma scores and the other showing scores comparable to healthy controls.ConclusionTaken together, this study presents a novel ML framework for classifying depression using CCA and clustering.

Clinical decision-making in psychiatry has traditionally relied on rating scales and clinical impressions documented in the electronic health record (EHR). Yet, clinical interviews contain rich behavioral signals that remain underutilized in psychiatric care. Recent advances in artificial intelligence (AI) now enable quantification of these signals, and prior work demonstrates that computational measures of speech, language, and facial expression can inform diagnosis and estimate symptom severity. Despite this progress, most prediction efforts remain confined to single modalities and individual diagnoses and focus on diagnostic classification rather than clinically actionable outcomes such as treatment discontinuation or the need for crisis care. Here, we contextualize advances in behavioral quantification and multimodal data fusion, and present the Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR) study, a prospective cohort study of 2100 patients entering outpatient mental health care. PREDiCTOR is designed to develop and validate dynamic, multimodal prediction signatures that predict treatment discontinuation, emergency department visits, and hospitalizations over a one-year follow-up period. The study audiovisual recordings of clinical encounters, EHR data, cognitive assessments, smartphone passive sensing, therapeutic alliance measures, and audio/text diaries within a Contextual Bandit framework that continuously updates individualized outcome estimates as new data become available. Both interpretable features and learned embeddings are leveraged, with large language models serving as feature extractors rather than clinical decision-makers. We describe the study design, data collection, and processing pipelines, hybrid predictive modeling approach, and prospective validation strategy, and discuss the potential for translating dynamic behavioral quantification into individualized clinical prognostics.

BACKGROUND:Capacity to experience positive emotions supports psychological resilience. Reward neural circuitry, implicated in positive emotionality, tends to be dampened in individuals with posttraumatic stress disorder (PTSD). Less understood is the relationship between neural response to reward and being highly resilient in the face of trauma exposure. METHOD:We recruited World Trade Center rescue and recovery workers (N=94) from three groups: "Highly Resilient" (higher WTC-related trauma exposure plus no current/lifetime psychopathology; n=34), "Lower WTC-exposed" (lower WTC-related trauma exposure plus no current/lifetime psychopathology; n=31), and "PTSD" (chronic WTC-related PTSD; n=32). We examined reward anticipation during the Incentive Flanker Task, focusing on a priori regions of interest associated with reward salience (nucleus accumbens) and reward valuation (ventromedial prefrontal cortex; vmPFC). RESULTS:Highly Resilient and Lower WTC-exposed groups scored comparably on self-report measures of positive emotionality, and higher than those in the PTSD group. However, left nucleus accumbens modulation by cue valence (reward, neutral, loss) was significantly greater in the Highly Resilient group compared to the Lower WTC-exposed group (Cohen's d=0.70) and, after statistically adjusting for psychotropic medication, the chronic WTC-related PTSD group (d=0.62), Similar results were observed for the vmPFC. CONCLUSIONS:Findings support the premise that neural responsivity to potential rewards may enable resilience, potentially by buffering negative effects of substantial trauma exposure. Integrating neurobiological assessments with psychological measures may help better understand how positive emotionality is linked to psychological resilience. Improved understanding of this link could be leveraged for clinical assessment and intervention in trauma-exposed populations.