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 t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-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.

Highlights

  • Independent component analysis (ICA) is a popular blind source separation technique and a powerful data-driven method (Dipasquale et al, 2015)

  • Malinen et al (2007) used independent component analysis (ICA) and general-linearmodel-based analysis (GLM) to analyze the functional magnetic resonance imaging (fMRI) data of 6 subjects, and the results showed that ICA was found to be a sensitive tool for studying brain responses to complex natural stimuli compared with GLM (Malinen et al, 2007)

  • According to the two-sample t-tests results. we found out abnormal resting state networks (RSNs) and brain regions in mild cognitive impairment (MCI) patients compared to healthy controls (HC) as shown in Figures 4, 5

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Summary

Introduction

Independent component analysis (ICA) is a popular blind source separation technique and a powerful data-driven method (Dipasquale et al, 2015). The advantage of ICA is that it does not require prior information when extracting brain maps and time courses from fMRI data (Svensén et al, 2002). Other analysis methods of fMRI data require prior information, and the prior information is generally artificial which may lead to the error of the result. For. Non-linear ICA Analysis of MCI example, the selection of regions of interest (ROIs) in seed correlation analysis method is artificially set (Koenig et al, 2009). As ICA does not require prior information, this data-driven method is widely used in the analysis of fMRI data (Robinson and Schöpf, 2013)

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