Volume 128, Number 5, December 2019
|Number of page(s)||7|
|Published online||04 February 2020|
Functional alteration of brain network in schizophrenia: An fMRI study based on mutual information
1 School of Electrical and Information Engineering, Tianjin University - Tianjin 300072, China
2 Tencent Youtu Lab, Malata Building, 9998 Shennan Avenue - Shenzhen 518057, China
3 First Teaching Hospital of Tianjin University of Traditional Chinese Medicine - Tianjin 300193, China
4 Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University - Shanghai 200433, China
Received: 20 November 2019
Accepted: 19 December 2019
Schizophrenia is a severe psychiatric disorder with complex neural mechanisms. Previous functional magnetic resonance imaging (fMRI) studies of schizophrenia often focused on single brain regions or selected networks. In recent years, neurophysiological alterations of schizophrenia are thought to be related with connectivity between distinct brain functional regions. The present study attempted to explore the alterations of resting-state functional connectivity to understand the neural mechanisms for adults with schizophrenia comprehensively. Here we perform a whole brain data-driven functional network analysis using resting-state fMRI data of 128 schizophrenia patients and 103 matched healthy controls. A whole brain large-scale graph theory based network is constructed using the mutual information method instead of the traditional correlation method. Significant nodal differences of network measures are found at several regions including the prefrontal cortex, hippocampus, temporal gyrus between patients and controls. Furthermore, we construct a pathological subnetwork and find intra-subnetwork edge strength differences located at the bilateral orbital gyrus and right hippocampus and the connection within temporal gyrus. Motif analysis reveal the network topological reorganization in schizophrenia happens mainly at the frontal, anterior cingulate gyrus and parahippocampal gyrus. These findings may help understand the neural basis more comprehensively and serve as a potential diagnosis marker in schizophrenia clinical application.
PACS: 05.45.Tp – Time series analysis
© EPLA, 2020
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