A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites.

The default mode network (DMN) is suggested to play a pivotal role in schizophrenia; however, the dissociation pattern of functional connectivity of DMN subsystems remains uncharacterized in this disease. In this study, resting-state fMRI data were acquired from 55 schizophrenic patients and 53 matched healthy controls. DMN connectivity was estimated from time courses of independent components. The lateral DMN exhibited decreased connectivity with the unimodal sensorimotor cortex but increased connectivity with the heteromodal association areas in schizophrenics. The increased connectivity between the lateral DMN and right control network was significantly correlated with negative and anergia factor scores in the schizophrenic patients. The anterior and posterior DMNs exhibited increased and decreased connectivity with the right control and lateral visual networks, respectively, in schizophrenics. The altered DMN connectivity may underlie the hallucinations, delusions, thought disturbances and negative symptoms involved in schizophrenia. Furthermore, DMN connectivity patterns could be used to differentiate patients from controls with 76.9% accuracy. These findings may shed new light on the distinct role of DMN subsystems in schizophrenia, thereby furthering our understanding of the pathophysiology of schizophrenia. Elucidating key disease-related DMN subsystems is critical for identifying treatment targets and aiding in the clinical diagnosis and development of treatment strategies.

Depressive disorder is one of the most common mental health problems currently. However, the mechanism-based treatments for this disorder remain elusive. Repetitive transcranial magnetic stimulation (rTMS), a non-invasive procedure that could stimulate electrical activity by a pulsed magnetic field in the brain, is considered to be an effective treatment for depression. Here, we review the main findings from both clinical and basic research on rTMS for depression, including its antidepressant efficacy, basic principles, as well as its ability to regulate neural circuits, neurotransmitters and brain networks, neurogenesis in hippocampus, and synaptic, and molecular pathways.

Gastrodin, a main constituent of a Chinese herbal medicine, has been shown to be effective in treating various mood disorders. The purpose of the present study was to determine whether gastrodin could ameliorate stress-associated behavior in a rat model of enhanced single prolonged stress (ESPS)-induced posttraumatic stress disorder (PTSD). Following ESPS, rats were administered orally with gastrodin (50, 100, or 200 mg/kg daily) or vehicle for 2 weeks. Animals were then tested in the open field and elevated plus-maze, and the levels of IL-6 and IL-1β, the expression of iNOS, p38 and phospho-p38 (p-p38) in hippocampus were also tested. ESPS exposure resulted in pronounced anxiety-like behavior, elevated IL-6 and IL-1β levels, and the higher expression of iNOS and p-p38 in hippocampus. However, repeated treatment with gastrodin, particularly at higher doses, reversed the aforementioned changes, including anxiety-like behavior, levels of IL-6 and IL-1β, and the expression of iNOS and the p38 MAPK phosphorylation. These results indicate that gastrodin possesses anxiolytic effect and may be an effective herbal preparation for the treatment of PTSD.