Kim Sungkean, Kim Yong-Wook, Shim Miseon, Jin Min Jin, Im Chang-Hwan, Lee Seung-Hwan
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.
Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea.
Front Psychiatry. 2020 Jul 17;11:661. doi: 10.3389/fpsyt.2020.00661. eCollection 2020.
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.
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.
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.
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.
精神分裂症和双相情感障碍的病理机制一直未被充分理解。脑网络分析有助于理解精神分裂症和双相情感障碍的脑机制。本研究使用静息态脑电图（EEG）对精神分裂症和双相情感障碍患者的源水平脑皮质网络进行研究。
对38例精神分裂症患者、34例I型双相情感障碍患者和30名健康对照者进行静息态EEG测量。评估基于图论的源水平加权功能网络：六个频段的强度、聚类系数（CC）、路径长度（PL）和效率。
在全局水平上，与健康对照相比，精神分裂症或双相情感障碍患者在θ频段表现出更高的强度、CC和效率，以及更低的PL。在节点水平上，精神分裂症或双相情感障碍患者表现出更高的CC，主要在θ频段的额叶。特别是，与双相情感障碍患者相比，精神分裂症患者在左下额叶皮质和外侧沟左升支表现出更高的节点CC。此外，额上回和沟（认知相关区域）的节点水平θ频段CC与精神分裂症组的阳性症状及社会和职业功能量表（SOFAS）评分相关，而额中回（情感相关区域）的节点水平θ频段CC与双相情感障碍组的SOFAS评分相关。
揭示了皮质网络的改变，这些改变与精神分裂症和双相情感障碍的核心病理症状显著相关。这些源水平皮质网络指标可能是评估精神分裂症和双相情感障碍患者的有前景的生物标志物。
