Bi Xia-An, Sun Qi, Zhao Junxia, Xu Qian, Wang Liqin
College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Front Neurosci. 2018 Jun 19;12:413. doi: 10.3389/fnins.2018.00413. eCollection 2018.
Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample -tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample -tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients.
与线性独立成分分析（ICA）相比，非线性ICA更适合于混合成分的分解。现有的利用线性ICA对功能磁共振成像（fMRI）数据的研究假设，由大脑活动引起的大脑混合信号是通过源信号的线性组合形成的。但源信号的非线性组合应用更适合大脑的混合信号。因此，我们使用后非线性ICA研究了32名健康对照者（HC）和38名轻度认知障碍（MCI）患者静息态网络（RSN）的统计差异。后非线性ICA是非线性ICA方法之一。首先，对所有受试者的fMRI数据进行预处理。第二步是提取所有受试者fMRI数据的独立成分（IC）。第三步，计算IC与RSN模板之间的相关系数，并选择空间相关系数最大的IC。这些IC代表相应的RSN。在找出MCI组和HC组的八个RSN后，进行单样本检验。最后，为了比较MCI组和HC组RSN的差异，进行双样本检验。我们发现MCI患者RSN的功能连接性（FC）异常。与HC相比，MCI患者在默认模式网络（DMN）、中央执行网络（CEN）、背侧注意网络（DAN）、躯体运动网络（SMN）、视觉网络（VN）中的FC增加和减少，MCI患者在听觉网络（AN）、自我参照网络（SRN）中表现出特异性的FC降低。核心网络（CN）的FC没有显示出显著的组间差异。结果表明，MCI患者RSN中的异常FC具有选择性。
