Li Siyi, Hu Na, Zhang Wenjing, Tao Bo, Dai Jing, Gong Yao, Tan Youguo, Cai Duanfang, Lui Su
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Front Psychiatry. 2019 Jul 12;10:482. doi: 10.3389/fpsyt.2019.00482. eCollection 2019.
Seed-based studies on resting-state functional connectivity (rsFC) in schizophrenia have shown disrupted connectivity involving a number of brain networks; however, the results have been controversial. We conducted a meta-analysis based on independent component analysis (ICA) brain templates to evaluate dysconnectivity within resting-state brain networks in patients with schizophrenia. Seventy-six rsFC studies from 70 publications with 2,588 schizophrenia patients and 2,567 healthy controls (HCs) were included in the present meta-analysis. The locations and activation effects of significant intergroup comparisons were extracted and classified based on the ICA templates. Then, multilevel kernel density analysis was used to integrate the results and control bias. Compared with HCs, significant hypoconnectivities were observed between the seed regions and the areas in the auditory network (left insula), core network (right superior temporal cortex), default mode network (right medial prefrontal cortex, and left precuneus and anterior cingulate cortices), self-referential network (right superior temporal cortex), and somatomotor network (right precentral gyrus) in schizophrenia patients. No hyperconnectivity between the seed regions and any other areas within the networks was detected in patients, compared with the connectivity in HCs. Decreased rsFC within the self-referential network and default mode network might play fundamental roles in the malfunction of information processing, while the core network might act as a dysfunctional hub of regulation. Our meta-analysis is consistent with diffuse hypoconnectivities as a dysregulated brain network model of schizophrenia.
基于种子点的精神分裂症静息态功能连接(rsFC)研究表明,涉及多个脑网络的连接性遭到破坏;然而,结果一直存在争议。我们基于独立成分分析(ICA)脑模板进行了一项荟萃分析,以评估精神分裂症患者静息态脑网络内的连接障碍。本荟萃分析纳入了来自70篇出版物的76项rsFC研究,共2588例精神分裂症患者和2567例健康对照(HCs)。基于ICA模板提取并分类显著组间比较的位置和激活效应。然后,使用多级核密度分析来整合结果并控制偏差。与HCs相比,在精神分裂症患者中观察到种子区域与听觉网络(左侧岛叶)、核心网络(右侧颞上回)、默认模式网络(右侧内侧前额叶皮层、左侧楔前叶和前扣带回皮层)、自我参照网络(右侧颞上回)和躯体运动网络(右侧中央前回)中的区域之间存在显著的低连接性。与HCs的连接性相比,未在患者中检测到种子区域与网络内任何其他区域之间的高连接性。自我参照网络和默认模式网络内rsFC的降低可能在信息处理功能障碍中起重要作用,而核心网络可能作为调节功能失调的枢纽。我们的荟萃分析与弥漫性低连接性作为精神分裂症的脑网络失调模型一致。