Clinical Research Papers:

Increased local connectivity of brain functional networks during facial processing in schizophrenia: evidence from EEG data

Tianyi Yan _, Wenhui Wang, Tiantian Liu, Duanduan Chen, Changming Wang, Yulong Li, Xudong Ma, Xiaoying Tang, Jinglong Wu, Yiming Deng and Lun Zhao

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Oncotarget. 2017; 8:107312-107322. https://doi.org/10.18632/oncotarget.20598

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Tianyi Yan1,2, Wenhui Wang1,2, Tiantian Liu1,2, Duanduan Chen1, Changming Wang3, Yulong Li4, Xudong Ma5, Xiaoying Tang1, Jinglong Wu2, Yiming Deng6,7 and Lun Zhao8

1School of Life Science, Beijing Institute of Technology, Beijing, China

2Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China

3Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China

4Beijing National Day School, Beijing, China

5Guang Zhou Clifford School, Guang Dong, China

6Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China

7China National Clinical Research Center for Neurological Diseases, Beijing, China

8School of Education, Beijing Normal University Zhuhai, Zhuhai, China

Correspondence to:

Tianyi Yan, email: [email protected]

Yiming Deng, email: [email protected]

Keywords: schizophrenia, facial processing, dynamic brain network, phase synchrony, graph theory

Received: April 09, 2017     Accepted: June 12, 2017     Published: September 01, 2017


Schizophrenia is often considered to be a disconnection syndrome. The abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. The present study investigated event-related functional connectivity networks to compare facial processing in individuals with and without schizophrenia. Faces and tables were presented to participants, and event-related phase synchrony, represented by the phase lag index (PLI), was calculated. In addition, cortical oscillatory dynamics may be useful for understanding the neural mechanisms underlying the disparate cognitive and functional impairments in schizophrenic patients. Therefore, the dynamic graph theoretical networks related to facial processing were compared between individuals with and without schizophrenia. Our results showed that event-related phase synchrony was significantly reduced in distributed networks, and normalized clustering coefficients were significantly increased in schizophrenic patients relative to those of the controls. The present data suggest that schizophrenic patients have specific alterations, indicated by increased local connectivity in gamma oscillations during facial processing.

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