Psychosomatic disorders are characterized by complex interactions between psychological factors and somatic symptoms and frequently present with comorbid insomnia, anxiety, and depressive symptoms. Although conventional pharmacological treatments are widely used, their limitations highlight the need for integrative therapeutic approaches. Tianmeng oral liquid, a multi-component Chinese herbal formulation, has been increasingly used in clinical practice for the treatment of psychosomatic symptoms. This review aims to develop expert consensus on the clinical application of Tianmeng oral liquid in the treatment of psychosomatic disorders. This consensus was developed through a comprehensive review of the literature, evaluation of available clinical and experimental evidence, and structured expert panel discussions. Recommendations were formulated based on evidence strength, clinical experience, and principles of psychosomatic medicine. Tianmeng oral liquid exhibits a multi-target pharmacological profile, modulating central nervous system activity, neuroendocrine regulation, and immune homeostasis. Available evidence suggests that it may improve sleep quality, alleviate anxiety and depressive symptoms, and enhance overall well-being in patients with psychosomatic disorders. The formulation may be used as monotherapy in mild cases or as an adjunct to standard pharmacological and psychological interventions. It is generally well tolerated when administered according to recommended dosing strategies. Tianmeng oral liquid may be considered a complementary therapeutic option within a comprehensive biopsychosocial treatment framework for psychosomatic disorders. Further high-quality randomized controlled trials and mechanistic studies are warranted to strengthen the evidence base and refine clinical application strategies.

Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

Major depressive disorder (MDD) remains clinically diagnosed based on subjective symptoms rather than objective neurobiological markers, which limits diagnostic accuracy and the ability to tailor treatment. We present an ensemble hybrid framework that integrates graph neural networks (GNN) with unsupervised clustering to classify and subtype MDD using resting-state functional connectivity (rs-fMRI) profiles. A GNN was trained to distinguish MDD from healthy controls using functional connectivity derived brain graphs, and the resulting subject level embeddings were clustered to uncover subtype structure. We evaluated the approach on two public multisite cohorts, REST-meta-MDD (China; N = 1,604; 17 sites) and SRPBS (Japan; N = 446; 4 sites), using leave-one-site-out cross-validation and cross-national transfer. The classifier achieved 0.73 leave-one-site-out accuracy on REST-meta-MDD and retained 0.78 sensitivity when transferred from the Chinese to the Japanese cohort, outperforming BrainIB and CI GNN under the same protocol. To mitigate site related confounds, we applied a standardized preprocessing pipeline and ComBat harmonization. Clustering consistently identified three MDD subtypes with distinct connectivity signatures involving the default mode network and cerebellum, the insula-cingulum temporal circuit, and frontostriatal circuitry. These findings provide a reproducible and biologically interpretable stratification of MDD. Prospective studies will be needed to link these subtypes to treatment response and other clinically meaningful outcomes.

Background: Depressive symptoms are common among people with diabetes and may impair self-management and quality of life. Objective: This study aimed to develop and externally validate an interpretable machine-learning model for depressive symptom screening in Chinese adults aged 50 years or older with diabetes and translate it into an online assessment tool. Methods: Participants with diabetes from Round 4 of the China Health and Retirement Longitudinal Study (n = 2,187) were randomly divided into training and internal validation/model-selection sets at a 7:3 ratio. An independent cohort from Xijing Hospital (n = 695) was reserved for external validation. Depressive symptoms were defined as a Center for Epidemiologic Studies Depression Scale-10 (CESD-10) score of 10 or higher. Least absolute shrinkage and selection operator regression was used for feature selection; nine machine-learning algorithms were compared in the development data, and SHapley Additive exPlanations analysis interpreted the final Extreme Gradient Boosting (XGBoost) model. Results: Sixteen predictors were retained. XGBoost achieved areas under the curve of 0.822, 0.832, and 0.812 in the training, internal validation, and external validation sets, respectively. Major contributors included sleep duration, self-perceived health status, cognitive function, activities of daily living, instrumental activities of daily living, and life satisfaction. An online assessment tool was developed. Conclusions: The XGBoost model showed consistent discrimination but non-ideal calibration and may support concurrent preliminary classification and triage of depressive symptoms in Chinese adults aged 50 years or older with diabetes. The calculator is not diagnostic, should not replace CESD-10 administration or professional assessment when clinically indicated, and requires recalibration and prospective multicenter validation before broader clinical use.

Major Depressive Disorder (MDD) is highly heterogeneous, limiting treatment efficacy. Despite efforts to delineate patient heterogeneity through subtyping, current approaches remain limited by noise, lack of clinical applicability, and insufficient external validation. Crucially, they focus on subtyping while neglecting staging information (e.g., illness duration). We developed BrainCVAE, a contrastive variational autoencoder, to disentangle MDD-specific neural features. Applying BrainCVAE to fALFF-derived resting-state fMRI from 1590 patients and 1308 controls identified two subtypes: Subtype 1 with hyperactivity in visual, attention, and default mode networks, and Subtype 2 with hypoactivity. Subtypes were validated in 1276 patients across independent centers. Subtype 1 showed superior responsiveness to pharmacological (SSRIs, SNRIs) and non-pharmacological (rTMS) interventions. Cross-sectional analyses revealed subtype-specific differences in DMN profiles across illness duration: Subtype 1 shifted from hyper- to hypoactivity, whereas Subtype 2 remained consistently hypoactive. In an independent dataset, illness duration correlated negatively with symptom reduction (r = -0.5565, 95% CI = (-0.8123, -0.1210), p = 0.0165). Datasets were ethically approved and registered on ClinicalTrials.gov: XJ_QG (NCT05577481, May 24, 2023), SAINT (NCT04653337, Oct 21, 2020), XJ_KG (NCT05544071, May 24, 2023). Integrating subtyping with illness staging bridges neurobiological heterogeneity and disease progression, providing a clinically actionable framework for precision treatment in MDD.

Auditory verbal hallucinations (AVH) represent one of the most debilitating symptoms in schizophrenia. The amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF), derived from resting-state fMRI, serve as robust metrics for intrinsic brain activity; however, the network-level architecture and biological substrates underlying AVH-related ALFF/fALFF alternations have not yet been systematically elucidated. In this study, we conducted a comprehensive systematic review and meta-analysis of ALFF/fALFF studies in schizophrenia patients with AVH, integrating neurochemical mapping and transcriptomic annotation to provide a multilevel mechanistic perspective. Across studies, AVH were consistently associated with increased intrinsic activity in auditory and language networks, reward and motivation circuits, and executive control regions, along with decreased activity within sensorimotor network, whereas alternations within default mode network regions were more heterogeneous. Meta-analysis further highlighted the involvement of thalamic-frontal circuitry in distinguishing AVH patients from non-AVH patients. Spatial correlation analysis demonstrated significant coupling between AVH-related functional changes and the normaltive distribution of key neurotransmitter systems, including the cannabinoid (CB1), dopaminergic (D2), noradrenergic (NAT), and metabotropic glutamate (mGluR5) . Gene enrichment analysis additionally revealed that implicated regions were transcriptionally characterized by biological pathways related to neurodevelopment, neural circuit formation, and regulation of neural excitability. By integrating these convergent results, we propose a systems-level model in which early genetic and neurodevelopmental vulnerabilities interacts with ongoing neurotransmitter dysregulation and large-scale network dysfunction, ultimately driving the emergence and persistence of AVH in schizophrenia. These findings underscore the importance of multidimensional biomarkers and may inform the development of precision interventions targeting hallucinations in schizophrenia.

Face photo-sketch recognition task plays a crucial role in forensic investigation, human visual perception, and facial biometrics applications. The substantial modality gap between photographs and sketches, compounded by the influence of the semantic gap, poses a formidable challenge to recognition tasks. This study aims to propose an effective electroencephalography (EEG)-based approach to bridge this gap. In this paper, we introduce a face photo-sketch recognition paradigm (FPSR), a rapid serial visual presentation (RSVP) paradigm for the matching of face sketches. Based on this paradigm, we further proposed a new EEG signal feature decoding method called multi-scale feature extraction and aggregation network (MFEA). This network extracts shallow features in three dimensions and reconstructs three dimensional abstract features. Subsequently, the shallow features are aggregated with the deeper features to enhance the retention of all effective EEG signal features. These combined features are then input into the spatial module for specific dimensionality reduction. Experiments were conducted on one public and one self-conducted EEG RSVP datasets to evaluate the performance of our proposed MFEA. The experimental results demonstrate that, compared to previous methods, our MFEA exhibits superior performance in the EEG classification task.

Objective Diabetes mellitus (DM) and cognitive impairment (CoI) are correlated, but the combined impact of depressive symptoms on CoI risk in older adults remains unclear. Methods This study included 1,200 U.S. adults aged ≥60 years (National Health and Nutrition Examination Survey, NHANES) and 1,500 Chinese adults (China Health and Retirement Longitudinal Study, CHARLS). DM was defined by laboratory measures or self-report; depressive symptoms were assessed via the Patient Health Questionnaire-9 (PHQ-9) (NHANES) and CES-D-10 (CHARLS); CoI was measured using the CERAD battery (NHANES) and situational memory/mental integrity scores (CHARLS). Multivariate logistic regression estimated associations of DM and depressive symptoms with CoI. Additive interaction was evaluated by the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S). Restricted cubic spline (RCS) analyses examined nonlinear DM–depressive symptoms interactions. Results Both DM and depressive symptoms were independently and significantly associated with CoI. After multivariate adjustment, diabetic patients exhibited significantly increased CoI risk; depressive symptoms similarly elevated CoI risk. Interaction analysis revealed an additive effect when DM co-occurred with depressive symptoms in CHARLS but not in NHANES. Mediation analyses further suggest bidirectional associations: in CHARLS, diabetes and depressive symptoms appear to mediate each other’s effects on CoI. Additionally, RCS analysis in NHANES indicated a nonlinear interaction between depressive symptom severity (PHQ-9) and DM on CoI (p<0.05). Conclusion The observed associations varied across cohorts, with a significant additive interaction between diabetes and depressive symptoms found only in the CHARLS cohort. Integrating metabolic and mental health screening and interventions may optimize cognitive outcomes in high-risk older adult populations.

Gut microbial, mainly bacterial dysbiosis, has been demonstrated in patients with schizophrenia (SCH). However, the signatures and differences of minority gut microbiota in SCH, such as archaea and fungi, have been poorly addressed. We obtained stool samples from 61 SCH patients and 69 healthy controls (HC), and analyzed the compositional and functional alterations of gut archaea, fungi, and bacteria using metagenomic shotgun sequencing (MSS). Additionally, we developed potential biomarkers to distinguish SCH from HC. SCH patients showed significantly lower archaeal α-diversity compared with that of HC. Whereas there were significant differences between SCH and HC in β-diversity at the species level of archaea, fungi and bacteria. Meanwhile, the functional differences between the two groups were concentrated in glucose, lipid and amino acid metabolic pathways. Furthermore, we established potential diagnostic archaeal (9 species, AUC = 0.73), fungal (8 species, AUC = 0.69), and bacterial (22 species, AUC = 0.74) microbiomes for differentiating SCH patients from HC. This study describes a more comprehensive understanding of abnormal gut microbiome in SCH and might provide candidate targets for the development of a microbe-based diagnosis for SCH. Chinese Clinical Trial Registry: ChiCTR2000032118, registration date: 2020/04/20.

ABSTRACT Purpose Growing evidence implicates gut microbiota dysbiosis in social anxiety disorder (SAD), yet direct causal evidence remains limited. This study investigated whether fecal microbiota transplantation (FMT) from individuals with SAD was associated with general anxiety‐like behaviors and accompanying gut microbial and predicted metabolic alterations in mice. Methods Fecal samples were collected from five patients diagnosed with SAD and five matched healthy controls and transplanted into antibiotic‐treated mice. Anxiety‐like behaviors were evaluated using the open‐field test (OFT) and elevated‐plus maze test (EPMT). Gut microbiota composition was assessed by 16S rRNA gene sequencing, microbial functional potential was inferred using PICRUSt2, and plasma tryptophan‐pathway metabolites were quantified. Results Mice receiving SAD microbiota (SAD group) showed general anxiety‐like behaviors, characterized by more time spent in the periphery of the OFT and fewer entries and less time in the open arms of the EPMT compared to mice receiving healthy control microbiota (control group). Although α‐diversity did not differ significantly, β‐diversity was distinct between groups. The SAD group showed enrichment of Bacteroidota/Bacteroidales‐related bacteria (e.g., Muribaculum), whereas the control group had higher abundance of butyrate producers (e.g., Butyricimonas). Functional prediction indicated lower predicted abundance of selected DNA‐repair and biosynthetic pathways in the SAD group. Plasma tryptophan levels were nominally lower in the SAD group. Conclusions These findings suggest that specific gut microbiota alterations and predicted functional pathway changes in individuals with SAD may be associated with anxiety‐related behavioral phenotypes, supporting the gut microbiome as a potential contributing factor to the pathophysiology of SAD.

To explore the effect of repetitive transcranial magnetic stimulation (rTMS) on influenza-induced sleep disorders, verify its adjuvant value, and reveal the “influenza-immune inflammation-abnormal brain function-sleep disorder” pathway, a retrospective cohort study enrolled 55 adults (18-40 years, no comorbidities) with acute influenza (2023-2025, First Affiliated Hospital of Air Force Medical University). They were divided into positive control group (PC, n = 20, conventional treatment) and rTMS group (n = 16, conventional + rTMS: 10 Hz, 1x/day, 20-30 mins, 3000 pulses, 100% motor threshold, 3 days; split into pre-treatment [BT-rTMS] and post-treatment [AT-rTMS] subgroups). Twenty healthy people served as negative controls group (NC). Assessments included polysomnography (PSG), Sleep Disorder-Visual Analog Scale (SD-VAS), functional near-infrared spectroscopy (fNIRS) for frontal oxygenated hemoglobin, and ELISA for 9 blood indicators. Two-sample Mendelian randomization (TSMR), ROC models, and t-tests (P < 0.05) were used. TSMR showed influenza correlated with insomnia (IVW:P = 0.047, 95% OR = 1.001-1.134) and 27 brain structural changes (frontal most affected, n = 10). Serum IgM (IVW:P = 0.048, 95% OR = 1.001-1.193) and neutrophils (IVW:P = 0.003, 95% OR = 1.018-1.089) mediated sleep disorders, linking to SD-VAS and frontal gray matter. Left inferior frontal gyrus and right orbital gyrus were key. BT-rTMS had abnormal sleep (duration/efficiency/stages) vs NC, with higher right orbitofrontal cortex (R-OFC) and left ventrolateral prefrontal cortex (L-VLPFC) oxygenated hemoglobin (P < 0.05). AT-rTMS improved sleep vs PC, with lower R-OFC/L-VLPFC oxygenated hemoglobin (P < 0.05). A 7-indicator ROC model had AUC = 0.8571. rTMS improves influenza-related sleep disorders short-term by inhibiting frontal overactivation (R-OFC, L-VLPFC) and regulating immunity (IgM,neutrophils), offering a safe, rapid new approach for acute influenza sleep management.

Major depressive disorder (MDD) imposes significant global health burdens, yet its underlying neural mechanisms remain elusive. Traditional static functional metrics inadequately capture the brain’s dynamic nature, motivating the exploration of dynamic functional metrics to understand both the temporal and spatial reconfigurations of brain networks in MDD. Leveraging the Depression Imaging Research Consortium (DIRECT) dataset, this study conducted vertex-wise dynamic analyses in a large cohort of MDD patients (n = 1660) and healthy controls (n = 1341). We identified significant alterations in temporal stability across the brain, with MDD patients exhibiting increased stability in higher-order association areas (e.g., frontoparietal and default mode networks) and decreased stability in primary sensory-motor regions. Among the regions showing altered temporal stability, brain-symptom relationships were further explored. We identified a set of brain regions including the superior frontal gyrus, postcentral gyrus and superior insular sulcus, which were potentially involved in the common abnormal dFC network and associated with insomnia, feelings of guilt, and insight symptoms in MDD. By incorporating advanced vertex-wise dynamic functional analyses and a large sample size, this study provides insights into the neural mechanisms of MDD, emphasizing the value of dynamic approaches for identifying biomarkers. Future longitudinal and task-based studies are promising to elucidate causal relationships and refine personalized therapeutic interventions targeting specific dynamic dysfunctions in MDD.

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.

Major depressive disorder is projected to become the leading contributor to mental illness by 2030. While resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a non-invasive solution for depression detection, two significant challenges remain. First, due to medical data privacy regulations and the high costs associated with acquiring the necessary equipment, individual medical institutions struggle to obtain sufficient annotated data. Second, domain shifts, caused by discrepancies in scanner parameters and acquisition protocols across multi-center datasets, significantly hinder model generalization. To address these challenges, we propose a federated domain adaptation (FDA) method that integrates co-activation patterns and a multimodal Mamba network, termed FDA-CAPMA, for fMRI-based depression detection. Specifically, a federated learning architecture ensures both physical data isolation and patient privacy through parameter aggregation. A state-space model-based Mamba network captures cross-modal correlations between fMRI time-series features and non-imaging features. Additionally, a local maximum mean discrepancy (LMMD) module aligns source and target domain distributions in both feature and prediction spaces. Extensive experiments on the largest multi-center depression dataset (Rest-meta-MDD, 1813 participants) and ABIDE dataset, our method achieves an accuracy of 67.16%, and 65.72%, respectively. This work establishes a new paradigm for privacy-preserving depression recognition. Code will be available at: https://github.com/helang818/FDA-CAPMA/.

Cognitive impairment, characterized by impaired attention function, is widely recognized as one of the core symptoms of schizophrenia, significantly impacting the quality of life for individuals with this disorder. The objective of this cross-sectional study is to investigate the characteristics of attentional behavior and related brain structure and gene expression of schizophrenia, aiming to provide new trans-scale evidence. A total of 137 participants were enrolled in this study, including 65 patients with schizophrenia and 72 healthy controls. All participants underwent magnetic resonance imaging scans and completed cognitive tests. The results revealed that patients required more time (all p < 0.05) to complete key responses in attention-related tasks compared to the control group. Moreover, significant structural brain alterations were observed in patients with schizophrenia, including enlarged bilateral pallidum, smaller right hippocampus, and reduced cortical thickness in relevant brain regions (all p < 0.05). Further analysis demonstrated a correlation between alertness during Attention Network Test performance and pallidum volume among patients (r = 0.42, p = 4.30 × 10-4), and this correlation was associated with a specific pattern of gene expression in the patients' brain but not observed among controls. Our study reveals that decreased attentional ability in schizophrenia is accompanied by abnormal changes in pallidum along with specific transcriptional profile among patients with schizophrenia; these findings hold clinical significance for precise diagnosis and treatment strategies targeting schizophrenia.

Antidepressant efficacy for major depressive disorder (MDD) remains limited, with the neural mechanisms underlying treatment response poorly understood. The default mode network (DMN), particularly the connectivity between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), has been implicated in MDD pathophysiology and may be linked to treatment outcomes. However, its potential as a biomarker for antidepressant response has not been validated. Here, we investigate the relationship between DMN connectivity and antidepressant treatment response in MDD. Resting-state fMRI data from four large MDD cohorts (n = 4271) were analyzed using Granger causality to examine directional effective connectivity (EC) within the DMN. Linear mixed-effects models compared EC between recurrent MDD patients, first-episode drug-naïve patients, and healthy controls. We also examined associations between EC, medication use, illness duration, depressive symptoms, and treatment outcomes. Additionally, Support Vector Machine (SVM) classifiers and support vector regression (SVR) were trained using EC from mPFC to PCC to predict treatment response. Our results revealed that recurrent MDD patients exhibited significantly reduced EC from mPFC to PCC compared to healthy controls and first-episode patients, with this reduction correlating with antidepressant medication use and illness duration. Importantly, DMN connectivity was associated with treatment improvement rather than core depressive symptoms, including suicide, anhedonia, or emotional blunting. Crucially, EC from mPFC to PCC predicted antidepressant treatment response, and SVM classifiers demonstrated high predictive accuracy for therapeutic outcomes. In conclusion, reduced EC from mPFC to PCC may serve as a biomarker for antidepressant treatment response in MDD, offering insights into MDD neurobiology and supporting the clinical potential of DMN connectivity measures for guiding treatment decisions. The SAINT, Xijing_QG, and Xijing_KG datasets were approved by the Ethics Committee of the First Affiliated Hospital, Fourth Military Medical University (approval numbers: KY20202066-F-1, XJLL-KY20222111, and KY20222165-F-1, respectively) and registered with clinicaltrials.gov (identifiers: NCT 04653337, NCT 05577481, and NCT 05544071, respectively).

Imaging-based automatic diagnosis of major depressive disorder (MDD) has received widespread attention in precision medicine. Increasing evidence suggests that the pathophysiology of MDD is associated with the abnormality in brain connectome, which could be an effective biomarker for classification. However, previous studies suffered from small number of samples and large multi-site imaging divergences, as well as irregular graph architectures of the connectome, which challenges the diagnostic classification of MDD. Here, we propose a novel graph convolution network with sparse pooling (GCNSP) to learn the hierarchical features of the connectome graph to improve MDD classification. We applied the model to a multi-site functional MRI sample (33 sites with 3335 subjects, the largest functional imaging dataset of MDD to date), and perform transfer learning classification for each site using the pre-trained GCNSP on remaining sites to fit cross-site divergences, achieving an average accuracy of 70.14%. Moreover, hierarchical dysfunction of default mode network (DMN) is detected by the GCNSP in the patients. The interaction between DMN and frontoparietal network exhibit high discriminative power between patients and controls. Accordingly, this study may provide an effective pipeline for multi-site diagnostic classification and improve our understanding of hierarchical clues of brain network dysfunction in neuropsychiatric disorders.

Major depressive disorder (MDD) is a leading cause of disability worldwide, and current antidepressants are limited by delayed onset and incomplete efficacy. Esketamine produces antidepressant effects more rapidly than conventional treatments, but its underlying mechanisms remain unclear. We examined whether cannabinoid type 1 receptor (CB1R) signaling is associated with hippocampal mitochondrial biogenesis-related changes following esketamine treatment in a chronic variable stress (CVS) mouse model.Mice underwent 21 days of CVS and received a single intraperitoneal injection of esketamine (15 mg/kg) 24 h before behavioral testing. Hippocampal CB1R, nuclear respiratory factor 1 (NRF1), mitochondrial transcription factor A (TFAM), and total cytochrome C expression were assessed by Western blotting. Additional groups received the CB1R antagonists AM251 or SR141716A before esketamine administration.CVS induced depressive-like behavioral changes in the forced swim, tail suspension, and novelty-suppressed feeding tests. These effects were accompanied by reduced hippocampal CB1R, NRF1, and TFAM expression and altered total cytochrome c expression. Esketamine reversed the behavioral abnormalities and normalized these molecular changes. Pretreatment with either CB1R antagonist prevented both the behavioral and molecular effects of esketamine.These findings indicate that the antidepressant-like effects of esketamine observed 24 h after administration are associated with CB1R-dependent changes in hippocampal mitochondrial biogenesis-related protein expression. Further studies using direct mitochondrial functional assays and loss-of-function approaches are needed to establish causality.

Objective Although previous studies have reported structural brain alterations in major depressive disorder (MDD) patients at risk of suicide, no wide consensus has been reached. This study aimed to elucidate structural brain differences between MDD patients with and without suicide risk using data from the DIRECT Consortium, advancing our understanding of the neurophysiological mechanisms underlying suicide risk in MDD patients. Methods A total of 203 healthy controls (HCs), 208 MDD patients without suicide risk (MDD-NSR), and 376 MDD patients with suicide risk (MDD-SR) were included. T1-weighted MRI data were processed using DPABISurf to quantify cortical surface area, thickness, and cortical gray matter (CGM) volume. Results The MDD-SR and MDD-NSR groups demonstrated reduced surface area in the right orbitofrontal cortex (OFC), and reduced CGM volume in the left ACC and the right OFC compared with the HC group. The left ACC CGM volume decreased in a gradient, with the MDD-SR group showing significantly lower values than both the MDD-NSR and HC groups. While the SVM model showed limited differentiation between MDD-SR and MDD-NSR, the left ACC CGM volume was identified as the most critical feature in SHAP analysis. Conclusion MDD patients with suicide risk exhibited alterations in cortical surface area and CGM volume in the frontal lobe. The CGM volume reduction in the left ACC may serve as a potential biomarker for predicting suicide risk in MDD patients.

We propose TB-GCAN, a tri-branch cross-attention graph neural network for schizophrenia classification using multimodal MRI, including sMRI, fMRI, and DTI. Built on a multi-site dataset of 1191 samples from seven scanning sites, the model exploits atlas-defined one-to-one anatomical correspondence across modalities to enable node-level cross-attention during intermediate representation learning. In 7-site leave-one-site-out evaluation, TB-GCAN achieved 84.63% accuracy and outperformed GAT, GCN, CNN, SVM, and MMGNN in the tri-modal setting. Attention-based region ranking highlighted biologically plausible schizophrenia-related regions, and downstream analyses linked the learned imaging representations to PANSS dimensions and transcriptional programs. Unlike generic multimodal GNNs that learn cross-modal relations from data, TB-GCAN directly leverages atlas-aligned regional correspondence to perform anatomically constrained node-level interaction. These findings indicate that anatomically grounded node-level multimodal fusion can improve classification performance while preserving neurobiological interpretability, thereby providing a principled framework for multimodal schizophrenia classification and biomarker discovery.