Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study

Sungkean Kim; Yong-Wook Kim; Miseon Shim; Min Jin Jin; Chang-Hwan Im; Seung-Hwan Lee

doi:10.3389/fpsyt.2020.00661

Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study

Front Psychiatry. 2020 Jul 17:11:661. doi: 10.3389/fpsyt.2020.00661. eCollection 2020.

Authors

Sungkean Kim¹, Yong-Wook Kim^{2

3}, Miseon Shim⁴, Min Jin Jin⁵, Chang-Hwan Im³, Seung-Hwan Lee^{2

6}

Affiliations

¹ J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.
² Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea.
³ Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
⁴ Institute of Industrial Technology, Korea University, Sejong, South Korea.
⁵ Department of Psychiatry, Wonkwang University Hospital, Iksan, South Korea.
⁶ Department of Psychiatry, Inje University Ilsan Paik Hospital, Ilsan, South Korea.

Abstract

Background: Pathologies of schizophrenia and bipolar disorder have been poorly understood. Brain network analysis could help understand brain mechanisms of schizophrenia and bipolar disorder. This study investigates the source-level brain cortical networks using resting-state electroencephalography (EEG) in patients with schizophrenia and bipolar disorder.

Methods: Resting-state EEG was measured in 38 patients with schizophrenia, 34 patients with bipolar disorder type I, and 30 healthy controls. Graph theory based source-level weighted functional networks were evaluated: strength, clustering coefficient (CC), path length (PL), and efficiency in six frequency bands.

Results: At the global level, patients with schizophrenia or bipolar disorder showed higher strength, CC, and efficiency, and lower PL in the theta band, compared to healthy controls. At the nodal level, patients with schizophrenia or bipolar disorder showed higher CCs, mostly in the frontal lobe for the theta band. Particularly, patients with schizophrenia showed higher nodal CCs in the left inferior frontal cortex and the left ascending ramus of the lateral sulcus compared to patients with bipolar disorder. In addition, the nodal-level theta band CC of the superior frontal gyrus and sulcus (cognition-related region) correlated with positive symptoms and social and occupational functioning scale (SOFAS) scores in the schizophrenia group, while that of the middle frontal gyrus (emotion-related region) correlated with SOFAS scores in the bipolar disorder group.

Conclusions: Altered cortical networks were revealed and these alterations were significantly correlated with core pathological symptoms of schizophrenia and bipolar disorder. These source-level cortical network indices could be promising biomarkers to evaluate patients with schizophrenia and bipolar disorder.

Keywords: cortical functional network; graph theory; ipolar disorder; resting-state EEG; schizophrenia.