Abnormality of Functional Connections in the Resting State Brains of Schizophrenics

Yan Zhu; Geng Zhu; Bin Li; Yueqi Yang; Xiaohan Zheng; Qi Xu; Xiaoou Li

doi:10.3389/fnhum.2022.799881

Abnormality of Functional Connections in the Resting State Brains of Schizophrenics

Front Hum Neurosci. 2022 Mar 10:16:799881. doi: 10.3389/fnhum.2022.799881. eCollection 2022.

Authors

Yan Zhu^{1

2}, Geng Zhu¹, Bin Li³, Yueqi Yang^{1

2}, Xiaohan Zheng^{1

2}, Qi Xu^{1

2}, Xiaoou Li^{1

2}

Affiliations

¹ College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China.
² College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China.
³ Shanghai Yangpu District Mental Health Center, Shanghai, China.

Abstract

To explore the change of brain connectivity in schizophrenics (SCZ), the resting-state EEG source functional connections of SCZ and healthy control (HC) were investigated in this paper. Different band single-layer networks, multilayer networks, and improved multilayer networks were constructed and their topological attributes were extracted. The topological attributes of SCZ and HC were automatically distinguished using ensemble learning methods called Ensemble Learning based on Trees and Soft voting method, and the effectiveness of different network construction methods was compared based on the classification accuracy. The results showed that the classification accuracy was 89.38% for α band network, 82.5% for multilayer network, and 86.88% for improved multilayer network. Comparing patients with SCZ to those with Alzheimer's disease (AD), the classification accuracy of improved multilayer network was the highest, which was 88.12%. The power spectrum in the α band of SCZ was significantly lower than HC, whereas there was no significant difference between SCZ and AD. This indicated that the improved multilayer network can effectively distinguish SCZ and other groups not only when their power spectrum was significantly different. The results also suggested that the improved multilayer topological attributes were regarded as biological markers in the clinical diagnosis of patients with schizophrenia and even other mental disorders.

Keywords: EEG; functional brain network; machine learning; resting state; schizophrenia.