Resolving heterogeneity in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder through individualized structural covariance network analysis.

Disruptions in large-scale brain connectivity are hypothesized to contribute to psychiatric disorders, including schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. However, high inter-individual variation among patients with psychiatric disorders hinders achievement of unified findings. To this end, we adopted a newly proposed method to resolve heterogeneity of differential structural covariance network in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. This method could infer individualized structural covariance aberrance by assessing the deviation from healthy controls. T1-weighted anatomical images of 114 patients with psychiatric disorders (schizophrenia: n = 37; bipolar I disorder: n = 37; attention-deficit/hyperactivity disorder: n = 37) and 110 healthy controls were analyzed to obtain individualized differential structural covariance network. Patients exhibited tremendous heterogeneity in profiles of individualized differential structural covariance network. Despite notable heterogeneity, patients with the same disorder shared altered edges at network level. Moreover, individualized differential structural covariance network uncovered two distinct psychiatric subtypes with opposite differences in structural covariance edges, that were otherwise obscured when patients were merged, compared with healthy controls. These results provide new insights into heterogeneity and have implications for the nosology in psychiatric disorders.

Individualized gray matter morphological abnormalities uncover two robust transdiagnostic biotypes.

Psychiatric disorders exhibit a shared neuropathology, yet the diverse presentations among patients necessitate the identification of transdiagnostic subtypes to enhance diagnostic and treatment strategies. This study aims to unveil potential transdiagnostic subtypes based on personalized gray matter morphological abnormalities. A total of 496 patients with psychiatric disorders and 255 healthy controls (HCs) from three distinct datasets (one for discovery and two for validation) were enrolled. Individualized gray matter morphological abnormalities were determined using normative modeling to identify transdiagnostic subtypes. In the discovery dataset, two transdiagnostic subtypes with contrasting patterns of structural abnormalities compared to HCs were identified. Reproducibility and generalizability analyses demonstrated that these subtypes could be generalized to new patients and even to new disorders in the validation datasets. These subtypes were characterized by distinct disease epicenters. The gray matter abnormal pattern in subtype 1 was mainly linked to excitatory receptors, whereas subtype 2 showed a predominant association with inhibitory receptors. Furthermore, we observed that the gray matter abnormal pattern in subtype 2 was correlated with transcriptional profiles of inflammation-related genes, while subtype 1 did not show this association. Our findings reveal two robust transdiagnostic biotypes, offering novel insights into psychiatric nosology.

Uncovering the effect and mechanism of Jiawei Xiaoyao Wan in treating breast cancer complicated with depression based on network pharmacology and experimental analysis.

BACKGROUND: Depression is a clinically common co-morbidity in breast cancer cases that brings negative outcomes on quality of life and potentially survival. Jiawei Xiaoyao Wan (JXW) is widely used in treating breast cancer and depressive disorder, but its potential pharmacological mechanisms remain elusive. PURPOSE: We aimed to explore the dual therapeutic effects and mechanisms of JXW acting on breast cancer complicated with depression (BCCD) by network pharmacology and in vivo experimental verification. METHODS: The chemical constituents of JXW were characterized using liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-Q-TOF/MS). The targets related to constituents of JXW were predicted by the TCMSP and Swiss Target Prediction databases, and targets of breast cancer and depression were screened by the GeneCards and OMIM databases. Gene Ontology annotation and KEGG enrichment analysis were performed with the DAVID database. Ultimately, a BCCD mouse model induced by chronic restraint stress (CRS) was used to explore therapeutic effects and mechanisms of JXW against BCCD. The efficacy of JXW in the treatment of BCCD was evaluated based on behavioral tests, tumor volume and weight, and pathological examination. Additionally, the underlying mechanisms were explored by measuring the levels of neurotransmitter and inflammatory factors, as well as detecting the expression of genes or proteins associated with candidate targets and the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway through RT-PCR, western blotting, and immunohistochemistry. RESULTS: Totals of 108 components were identified in JXW using LC-Q-TOF/MS. By network pharmacology analysis, 714 compound targets of JXW, 2114 breast cancer targets, 1122 depression targets, and 98 overlapping proteins were obtained. PPI network and KEGG analysis implied that TP53, ESR1, VEGFA, AKT1, IL6, TNF, EGFR and the JAK/STAT pathway might be the potential targets of JXW in treating BCCD. In vivo experiments indicated that JXW significantly ameliorated depressive symptoms and tumor progression in BCCD mice. Further mechanistic studies showed that JXW could reduce the levels of inflammatory factors, increase 5-HT level, and regulate mRNA expression levels of TP53, VEGFA, AKT1, IL6, TNF, and EGFR targets. Moreover, the expression levels of proteins related to the JAK2/STAT3 signaling pathway in BCCD mice were effectively regulated by JXW. CONCLUSION: JXW exerts dual therapeutic effects in a BCCD mouse via multiple targets. The underlying mechanisms might be associated with regulating the levels of neurotransmitter and inflammatory factors; more importantly, the JAK2/STAT3 pathway plays a significant role in this process.