Multi-modal evaluation of Alzheimer disease by using joint independent component analysis of functional MRI diffusion tensor imaging

Objective We utilized a joint independent component analysis (Joint ICA), a novel method that combined rs-fMRI and DTI information, to describe comprehensive characteristics of brain functional activities and microstructural changes in the continuum of AD. Methods We employed a Joint ICA to calculate ALFF maps of fMRI data and FA maps of DTI data and fuse them in healthy controls (n=68), SCD (n=35), amnesic MCI (n=47) and AD (n=31). Besides, we applied one way ANOVA to detect the significant differences of joint components among groups, while controlling the age, gender, education, head motion, volumes of gray matter, white matter and CSF. Partial correlation analysis was used to test the relationships between joint ICs and cognitive measures. Results The results showed that there was no inner-group difference in HC and SCD groups (F=14.16, P<0.05). Compared to HC, SCD and AD groups, the ALFF component of aMCI group showed higher values in the bilateral cerebellum, bilateral precuneus, bilateral angular gyrus, bilateral frontal gyrus, bilateral temporal areas, thalamus and left insula. And in these regions, the ALFF of AD group was lower than HC. For the FA component map, same differences were found in the corpus callosum and limbic system. Furthermore, positive partial correlation between the IC weights and Mini-Mental State Examination (MMSE) scores was also found (r=0.29, P<0.01). Conclusions Multi-modal evaluation of AD has been implemented by using Joint ICA analysis of fMRI-DTI, which would contribute to early prediction, diagnosis, and even effective intervention in AD. These findings could help to explain the underlying mechanism of the disease progression. Key words: Alzheimer disease; Magnetic resonance imaging; Diffusion tensor imaging; Joint independent component analysis