The Mind Research Network Albuquerque, NM, USA.
Front Syst Neurosci. 2012 Jan 10;5:103. doi: 10.3389/fnsys.2011.00103. eCollection 2011.
Neuroimaging studies have shown that functional brain networks composed from select regions of interest have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness.
神经影像学研究表明，由特定感兴趣区域组成的功能性脑网络具有模块化的社区结构。然而，由空间独立的脑区组成的纯粹基于数据的功能网络连接（FNC）的组织尚不清楚。本研究旨在探索健康对照组（HCs）和精神分裂症患者（SZs）的 FNC 的模块化组织。通过组独立成分分析（ICA）将 HCs 和 SZs 的静息态功能磁共振成像数据分解为独立成分（ICs）。然后，基于 ICA 时间序列之间的相关性构建加权脑网络（其中节点是脑区）。计算网络的聚类系数和连接强度。使用动态分支切割算法识别 HCs 和 SZs 中 FNC 的模块。结果表明，SZs 中的模块数量较少，连接强度和聚类系数更强。此外，HCs 和 SZs 具有一些不同的枢纽。我们的发现表明精神分裂症中 FNC 的模块化结构发生改变，并为这种精神疾病中内在脑网络的异常拓扑组织提供了见解。
