Association of Reduced Local Activities in the Default Mode and Sensorimotor Networks with Clinical Characteristics in First-diagnosed Episode of Schizophrenia.

Abstract

The neurodevelopmental hypothesis states that schizophrenia is a brain disease. Exploring abnormal brain activities can improve understanding of the neural pathologic mechanism of clinical characteristics and determine subjective biomarkers to differentiate patients with schizophrenia from healthy controls. We collected clinical characteristics (i.e., demographics, positive and negative syndrome scale (PANSS) scores, and cognitive scores) and magnetic resonance imaging (MRI) data from 57 first-diagnosed drug-naïve patients with schizophrenia and 50 healthy controls. The fractional amplitude of low-frequency fluctuation (fALFF) was used to detect local activities. Partial correlation analysis was applied to estimate the relationship between abnormal regions and clinical characteristics. The support vector machine (SVM) analysis was used to calculate the accuracy of classification in abnormal regions. In our study, the fALFF values in the right postcentral gyrus, left precentral gyrus/postcentral gyrus, left postcentral gyrus/superior parietal lobule, bilateral supplementarymotor area, bilateral paracentral lobule, and bilateral precuneus were decreased in patients with schizophrenia and associated with clinical characteristics. However, the related patterns of cognition of patients were different from those of healthy controls. Additionally, the combination of fALFF values in the bilateral paracentral lobule and right postcentral gyrus might distinguish patients with schizophrenia from healthy controls with high accuracy (98.13%), specificity (98.00%), and sensitivity (98.25%). Our study suggests that reduced local activities in the default mode network and sensorimotor network may be regarded as neural underpinnings of clinical characteristics and may discriminate patients with schizophrenia from healthy controls.

• Authors: Shuzhan Gao, Yidan Ming, Sulin Ni, Zhiyao Kong, Jiayin Wang, Yuan Gu, Shuiping Lu, Tian Chen, Mingjun Kong, Jing Sun, Xijia Xu
• Journal: Neuroscience (Neuroscience) Vol. 495 Pg. 47-57 (07 15 2022) ISSN: 1873-7544 [Electronic] United States
• PMID: 35605906 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2022 IBRO. Published by Elsevier Ltd. All rights reserved.

Topics

• Brain (diagnostic imaging, pathology)
• Brain Mapping (methods)
• Humans
• Magnetic Resonance Imaging (methods)
• Schizophrenia (diagnostic imaging, pathology)
• Support Vector Machine