Comprehensive review on sexual dimorphism to improve scalp acupuncture in nervous system disease.

BACKGROUND: With the development of modern medicine, the Traditional Chinese Medicine (TCM) combined with western medicine began to be produced and applied. Scalp acupuncture (SA) as a Chinese medicine based on neurological theory, has a great advantage compared with TCM in the treatment of nervous system diseases. METHOD: In this paper, we analyze the physiological and pathological manifestations of sexual dimorphism (SD) to illustrate the necessity of SD treatment. In addition, we review the factors that can affect SD and analyze in physiological structure, function, and pathological neurons. Diseases (pathological basis, pathological manifestations, and incidence) and factors leading to gender differences, which to analyze the possibility of gender differences in SA. RESULT: Furthermore, we creatively a new insight of SD-SA and provide the complete SD treatment cases on the basis of the existing SA in different kinds of diseases including stroke, migraine, attention deficit hyperactivity disorder (ADHD), and depression. CONCLUSION: In summary, we believe that it is feasible to improve the clinical effectiveness of SA, which is able to promote the development of SA, and then provides an actionable evidence for the promotion of precision medicine in the future.

Classification of First-Episode Schizophrenia, Chronic Schizophrenia and Healthy Control Based on Brain Network of Mismatch Negativity by Graph Neural Network.

Mismatch negativity (MMN) has been consistently found deficit in schizophrenia, which was considered as a promising biomarker for assessing the impairments in pre-attentive auditory processing. However, the functional connectivity between brain regions based on MMN is not clear. This study provides an in-depth investigation in brain functional connectivity during MMN process among patients with first-episode schizophrenia (FESZ), chronic schizophrenia (CSZ) and healthy control (HC). Electroencephalography (EEG) data of 128 channels is recorded during frequency and duration MMN in 40 FESZ, 40 CSZ patients and 40 matched HC subjects. We reconstruct the cortical endogenous electrical activity from EEG recordings using exact low-resolution electromagnetic tomography and build functional brain networks based on source-level EEG data. Then, graph-theoretic features are extracted from the brain networks with the support vector machine (SVM) to classify FESZ, CSZ and HC groups, since the SVM has good generalization ability and robustness as a universally applicable nonlinear classifier. Furthermore, we introduce the graph neural network (GNN) model to directly learn for the network topology of brain network. Compared to HC, the damaged brain areas of CSZ are more extensive than FESZ, and the damaged area involved the auditory cortex. These results demonstrate the heterogeneity of the impacts of schizophrenia for different disease courses and the association between MMN and the auditory cortex. More importantly, the GNN classification results are significantly better than those of SVM, and hence the EEG-based GNN model of brain networks provides an effective method for discriminating among FESZ, CSZ and HC groups.