AUTHOR=Bi Xia-an , Sun Qi , Zhao Junxia , Xu Qian , Wang Liqin TITLE=Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment JOURNAL=Frontiers in Neuroscience VOLUME=12 YEAR=2018 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00413 DOI=10.3389/fnins.2018.00413 ISSN=1662-453X ABSTRACT=
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