1 Department of Biomedical Engineering, Marquette University , Milwaukee, Wisconsin.
2 Department of Neurosurgery, Medical College of Wisconsin , Milwaukee, Wisconsin.
Brain Connect. 2017 Sep;7(7):413-423. doi: 10.1089/brain.2016.0468. Epub 2017 Aug 30.
Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.
基于图论的网络分析将大脑描绘为一个复杂的网络，允许检查整体大脑连接模式并计算可量化的网络指标。迄今为止，大规模网络分析尚未应用于完全性脊髓损伤 (SCI) 患者的静息态功能网络。为了描述整个大脑到组成节点的模块化重组，并比较 SCI 和对照组的网络指标，对 15 名慢性完全性颈 SCI 患者和 15 名神经功能正常的对照组进行了扫描。对数据进行预处理，然后将大脑分割成 116 个感兴趣区域 (ROI)。对每个 ROI 对进行相关分析，构建连接矩阵，并将 ROI 分类为不同的模块。随后，在递增的成本阈值下计算局部效率 (LE) 和全局效率 (GE) 网络指标。模块化算法的应用将 SCI 和对照组的全脑静息态功能网络组织成九个和七个模块。各个模块在组成节点的数量和组成上在组间存在差异。在多个成本水平下，SCI 患者的 LE 表现出统计学上的显著降低。两组间的 GE 没有显著差异。两组中模块化结构的展示突出了大规模网络分析在研究复杂大脑网络中的适用性。比较组间的模块揭示了组成节点的数量和成员的差异，表明由于神经可塑性导致模块化重组。
