Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia. (15th January 2021)

• Record Type: Journal Article
• Authors: Yao, Rong; Xue, Jiayue; Yang, Pengfei; Wang, Qianshan; Gao, Peng; Yang, Xiaofeng; Deng, Hongxia; Tan, Shuping; Li, Haifang
• Is Part Of: Neuroscience. Volume 453(2021)
• Page Start: 187
• Page End: 205
• Publication Date: 2021-01-15
• Subjects: EEG -- brain functional networks -- microstates -- working memory tasks -- schizophrenia; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2020.11.007
• Languages: English
• ISSNs: 0306-4522

Abstract

Highlights: MTS can well detect the brain network abnormalities in patients with schizophrenia. MTS can significantly decrease time complexity of construct brain networks. Microstates were more sensitive in theta band during working memory tasks. Abstract: Electroencephalograph (EEG) signals and graph theory measures have been widely used to characterize the brain functional networks of healthy individuals and patients by calculating the correlations between different electrodes over an entire time series. Although EEG signals have a high temporal resolution and can provide relatively stable results, the process of constructing and analyzing brain functional networks is inevitably complicated by high time complexity. Our goal in this research was to distinguish the brain function networks of schizophrenia patients from those of healthy participants during working memory tasks. Consequently, we utilized a method involving microstates, which are each characterized by a unique topography of electric potentials over an entire channel array, to reduce the dimension of the EEG signals during working memory tasks and then compared and analyzed the brain functional networks using the microstates time series (MTS) and original time series (OTS) of the schizophrenia patients and healthy individuals. We found that the right frontal and parietal-occipital regions neurons of the schizophrenia patients were less active than those of the healthy participants during working memory tasks. Notably, compared with OTS, the time needed to construct the brain functional networks was significantly reduced by using MTS. In conclusion, our results show that, like OTS, MTS can well distinguish the brain functional network of schizophrenia patients from those of healthy individuals during working memory tasks while greatly decreasing time complexity. MTS can thus provide a method for characterizing the original time series for the construction and analysis of EEG brain functional networks.