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.

Toludesvenlafaxine hydrochloride sustained‐release tablets, as China’s first independently developed chemical Class 1 innovative drug with independent intellectual property rights for the treatment of depression and a new molecular entity, represent a novel triple reuptake inhibitor (TRI) with specific target selectivity for serotonin (5‐HT), norepinephrine (NE), and dopamine (DA). This single‐arm, multicenter clinical study aimed to evaluate the efficacy and safety of toludesvenlafaxine in alleviating anhedonia symptoms in patients with major depressive disorder (MDD). A total of 123 patients aged 18–65 years were enrolled between April 2023 and April 2024 and received an 8‐week treatment with toludesvenlafaxine sustained‐release tablets (80–160 mg/day). The primary efficacy endpoint was the change in the total score of the Dimensional Anhedonia Rating Scale (DARS) at weeks 2, 4, and 8. Significant improvements in DARS scores were observed, with mean changes from baseline of 8.4 (95% CI [6.4, 10.4], p < 0.0001), 14.1 (95% CI [12.0, 16.2], p < 0.0001), and 20.4 (95% CI [18.0, 22.9], p < 0.0001), respectively. Additionally, after 8 weeks of treatment, plasma levels of neurotrophic factors, including mature brain‐derived neurotrophic factor (mBDNF) ( t = 28.78, p < 0.0001), pro‐BDNF ( t = 27.71, p < 0.0001), and vascular endothelial growth factor (VEGF) ( t = 31.07, p < 0.0001), were significantly increased, and the plasma level of IGF‐1 was not significantly changed ( t = 0.35, p = 0.7269). No association was found between the percentage of changes in neurotrophic factors and the percentage of symptom improvements. Toludesvenlafaxine was generally well‐tolerated, with treatment‐emergent adverse events (AEs) (TEAEs) reported in 83.7% of participants and treatment‐related AEs (TRAEs) in 76.4%. These findings indicate that toludesvenlafaxine hydrochloride sustained‐release tablets are safe, well‐tolerated, and effective in alleviating anhedonia symptoms in patients with depression. Trial Registration: http://www.chictr.org.cn (No.: ChiCTR2300070331).

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.

Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. Firstly, we demonstrated the robust fitting of the model within our multi-site dataset. Subsequently, by employing representational similarity analysis and a deep learning classifier, we confirmed the specificity and disorder-related information capturing ability of SCZ-specific features. Moreover, analysis of the attractor characteristics of the neurodynamic system revealed significant differences in attractor space patterns between SCZ-specific states and shared states. Finally, we utilized Partial Least Squares (PLS) regression to examine the multivariate mapping relationship between SCZ-specific features and symptoms, identifying two sets of correlated modes implicating unique molecular mechanisms: one mode corresponding to negative and general symptoms, and another mode corresponding to positive symptoms. Our results provide valuable insights into disorder-specific neurodynamic features and states associated with SCZ, laying the foundation for understanding the intricate pathophysiology of this disorder.

To elucidate the brain-wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information-resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time-series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally-specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole-brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean-field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.

Background and Hypothesis Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification.Study Design Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions.Study Results Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research.Conclusions Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Importance: The global increase in urbanization has coincided with a rise in depression prevalence. However, the effect of urbanization on depression remains controversial, especially for the elderly. Objective: To clarify how urbanization impacts depression in the elderly from a network perspective. Design, setting, and participants: This sectional cohort study used data from China Health and Retirement Longitudinal Study (CHARLS). Main outcomes and measures: The occurrence of depressive symptoms in urban and rural elderly residents. Network metrics of depressive symptoms. Results: Of the 13,993 participants, lower incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 24.7 %-27.8 %) than in rural (40.4 %, 95 % CI, 39.5 %-41.3 %, P < 0.0001) residents. However, higher incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 25.2 %-28.4 %) than in rural (17.5 %, 95 % CI, 16.1 %-18.9 %, P < 0.0001) residents in a subset of 2898 pairs of participants after PSM. No difference in the network structure and metrics between urban and rural residents before (M = 0.071, p = 0.054, S = 0.037, p = 0.80) and after (M = 0.085, p = 0.133, S = 0.086, p = 0.47) PSM was detected. The networks structure revealed that negative affect was strongly connected to somatic symptoms and that the two anhedonic symptoms were also strongly connected. Conclusions: The current study indicated the homogeneity of the ultimate nature of depression between rural and urban residents from the network perspective, supporting the viewpoint that urbanization might not impose influence on depression. Further researches delving deeper into the complexity of the issue may provide new insights into our understanding of depression in an urban environment among the elderly.

Background It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. Methods This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. Results At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). Conclusions Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. Trial registration This study was registered on Clinicaltrials.gov (NCT03451734).

Schizophrenia (SCZ) is a highly heterogeneous disorder with diverse clinical manifestations and macro- and microscale biological variations, usually observed at dissociable levels. Here we propose a cross-scale, circuit-based framework to connect heterogeneous clinical symptoms, large-scale brain circuit dysfunctions, and genetic, molecular and cellular abnormalities in SCZ. Using connectomic and predictive models on three independent neuroimaging datasets ( n = 1,199, including patients with SCZ and healthy controls), we first identified two macroscale dysconnectivity dimensions for corticocortical and corticostriatal circuits, each associated with specific clinical symptoms. We then associated macroscale dysconnectivity with disrupted cellular circuits using extended imaging transcriptomic and genetic analyses on multiomics data. Our findings suggest a two-dimensional cross-scale heterogeneity model of SCZ, which reveals how distinct genetic disruptions affect specific cellular-level deficits, resulting in system-level brain circuit dysconnectivity responsible for the heterogeneous symptoms in SCZ. These findings significantly improve our understanding of cross-scale heterogeneity in SCZ, advancing its pathophysiology and treatment development.

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

BackgroundPrevious analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.AimsTo identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.MethodWe used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.ResultsWe found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.ConclusionsThese results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

White matter tracts alterations have been reported in schizophrenia (SZ), but whether such abnormalities are associated with the effects of the disorder itself and/or genetic vulnerability remains unclear. Moreover, the specific patterns of different parts of these altered tracts have been less well studied. Thus, diffusion-weighted images were acquired from 38 healthy controls (HCs), 48 schizophrenia patients, and 33 unaffected first-degree relatives of SZs (FDRs). Diffusion properties of the 25 major tracts automatically extracted with probabilistic tractography were calculated and compared among groups. Regarding the peripheral regions of the tracts, significantly higher diffusivity values in the left superior longitudinal fasciculus (SLF) and the left anterior thalamic radiation (ATR) were observed in SZs than in HCs and unaffected FDRs. However, there were no significant differences between HCs and FDRs in these two tracts. While no main effects of group with respect to the core regions of the 25 tracts survived multiple comparisons correction, FDRs had significantly higher diffusivity values in the left medial lemniscus and lower diffusivity values in the middle cerebellar peduncle than HCs and SZs. These findings enhance the understanding of the abnormal patterns in the peripheral and core regions of the tracts in SZs and those at high genetic risk for schizophrenia. Our results suggest that alterations in the peripheral regions of the left SLF and ATR are features of established illness rather than genetic predisposition, which may serve as critical neural substrates for the psychopathology of schizophrenia.

焦虑是一种情绪,我们每个人都会有.当我们对某种即将发生的事情怀有负面期待或恐惧,担心有不好的事情有可能发生,从而回避、控制和对抗时,焦虑就产生了.比方说:最后期限可能要到了,但是你的工作还没有完成;或者马上要考试了,但是你还没有准备好;又或者面对超额的信用卡账单,你担心自己可能还不上……这些情况可能会让你感受到焦虑.

OBJECTIVE:The microbiota-gut-brain axis is a key pathway perturbed by prolonged stressors to produce brain and behavioral disorders. Frontline healthcare workers (FHWs) fighting against COVID-19 typically experience stressful event sequences and manifest some mental symptoms; however, the role of gut microbiota in such stress-induced mental problems remains unclear. We investigated the association between the psychological stress of FHW and gut microbiota.METHODS:We used full-length 16S rRNA gene sequencing to characterize the longitudinal changes in gut microbiota and investigated the impact of microbial changes on FHWs' mental status.RESULTS:Stressful events induced significant depression, anxiety, and stress in FHWs and disrupted the gut microbiome; gut dysbiosis persisted for at least half a year. Different microbes followed discrete trajectories during the half-year of follow-up. Microbes associated with mental health were mainly Faecalibacterium spp. and [Eubacterium] eligens group spp. with anti-inflammatory effects. Of note, the prediction model indicated that low abundance of [Eubacterium] hallii group uncultured bacterium and high abundance of Bacteroides eggerthii at Day 0 (immediately after the two-month frontline work) were significant determinants of the reappearance of post-traumatic stress symptoms in FHWs.LIMITATIONS:The lack of metabolomic evidence and animal experiments result in the unclear mechanism of gut dysbiosis-related stress symptoms.CONCLUSION:The stressful event sequences of fighting against COVID-19 induce characteristic longitudinal changes in gut microbiota, which underlies dynamic mental state changes.

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

目的 通过对一项大型全国研究的数据分析,探究老年人听觉和视觉障碍与其抑郁症状的相关性.方法 纳入6141例年龄≥60岁的老年人为研究对象,根据是否合并听觉和视觉障碍分为:无障碍组(n=5519)、视觉障碍组(n=195)、听觉障碍组(n=335)和双重障碍组(n=72).比较一般资料和10条目流调中心用抑郁量表(CESD-10)评分的差异,并分析与CESD-10评分有关的因素.结果 四组老年人的年龄、性别构成、受教育年限、婚姻状态和CESD-10评分存在差异(P<0.05).与无障碍组相比,视觉障碍组、听觉障碍组和双重障碍组的CESD-10评分较高;与听觉障碍组相比,视觉障碍组和双重障碍组的CESD-10评分更高(均P<0.05).受教育年限低、合并听觉障碍、合并视觉障碍、性别为女性、婚姻状态不佳和年龄低与CESD-10评分升高有关(P<0.05).结论 老年人抑郁症状与其合并听觉和视觉障碍有关,视觉障碍对抑郁症状的影响更明显.

Object:This study aims to compare the efficacy, acceptability and tolerability of cognitive behavior therapy (CBT), pharmacotherapy, and their combination therapy for acute panic disorder via network Meta-analysis.Methods:Electronic databases including PubMed, Web of Science, Embase, Cochrane Library, CNKI, and Wanfang Data were searched for relevant randomized controlled clinical trials from their inception up to March 24, 2021. The outcomes were efficacy (remission rate and response rate), acceptability and tolerability. Network Meta-analysis was adopted with random effects, and estimated relative risk (RR) for all effects of CBT, CBT plus medications, selective serotonin-reuptake inhibitors(SSRIs), serotonin-noradrenaline-reuptake inhibitors(SNRIs), tricyclic antidepressants(TCAs), monoamine oxidase inhibitors(MAOI), benzodiazepines(BZD), noradrenergic-reuptake inhibitors(NRI), and others. This study used gemtc package in R for network analysis, Review Manager for quality assessment, and GRADEPro for evidence assessment, respectively.Results:Identified citations (7 173) included 72 trials comprising 12 293 patients. The treatment measures with higher remission rate and response rate than placebo included CBT+drug( RR:2.4, 95% CI: 1.7-3.5), CBT( RR:1.8, 95% CI:1.4-2.3), SNRI( RR:1.8, 95% CI:1.5-2.2), BZD( RR:1.6, 95% CI:1.4-1.9), SSRI( RR:1.5, 95% CI:1.4-1.7), TCA( RR:1.5, 95% CI: 1.4-1.8)(remission rate in brackets); Among them, CBT combined with medications was better than SSRI, SNRI and TCA. The acceptability of BZD was better than placebo but its tolerability was inferior. Besides, the tolerability of SSRI and TCA was inferior to placebo. Conclusions:CBT combined with medications is more effective than BZD alone and the antidepressants alone. CBT combined with medications may selected the first-line treatment for panic disorder.

Evidence suggests that complex interactions between the immune system and brain have important etiological and therapeutic implications in schizophrenia. However, the detailed cellular and molecular basis of immune dysfunction in schizophrenia remains poorly characterized. To better understand the immune changes and molecular pathways, we systemically compared the cytokine responses of peripheral blood mononuclear cells (PBMCs) derived from patients with schizophrenia and controls against bacterial, fungal, and purified microbial ligands, and identified aberrant cytokine response patterns to various pathogens, as well as reduced cytokine production after stimulation with muramyl dipeptide (MDP) in schizophrenia. Subsequently, we performed single-cell RNA sequencing on unstimulated and stimulated PBMCs from patients and controls and revealed widespread suppression of antiviral and inflammatory programs as well as impaired chemokine/cytokine–receptor interaction networks in various immune cell subpopulations of schizophrenic patients after MDP stimulation. Moreover, serum MDP levels were elevated in these patients and correlated with the course of the disease, suggesting increased bacterial translocation along with disease progression. In vitro assays revealed that MDP pretreatment altered the functional response of normal PBMCs to its re-stimulation, which partially recapitulated the impaired immune function in schizophrenia. In conclusion, we delineated the molecular and cellular landscape of impaired immune function in schizophrenia, and proposed a mutual interplay between innate immune impairment, reduced pathogen clearance, increased MDP translocation along schizophrenia development, and blunted innate immune response. These findings provide new insights into the pathogenic mechanisms that drive systemic immune activation, neuroinflammation, and brain abnormalities in schizophrenia.

BackgroundSchizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.AimsTo investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.MethodGenomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.ResultsThe imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.ConclusionsThese findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.