Multimodal neuroimaging in Alzheimer's disease: Contributions of multi-voxel pattern analysis to the analysis of DTI and resting-state MRI

Previous findings suggest that temporal coherence between Blood-Oxygen-Level Dependent (BOLD) activation in certain areas is specifically related to the micro-structural organization of fascicles, i.e., the more organized the fibers, the more intense is the communication between areas. This assumption was considered in the analysis of functional and effective connectivity in patients with AD. Support Vector Machines for pattern classification (PRoNTo Toolbox-UCL) were applied to verify the usefulness of Granger-causality effective connectivity maps in correctly classifying patients and controls. Nineteen patients and eighteen healthy controls were recruited for the study and were scanned using DTI and resting state functional connectivity MRI (rs fc-MRI). Analysis of covariance with age as a confounding factor was applied to DTI data to identify areas related to disease progression. Granger mapping was used to identify brain areas related to differences of effective connectivity between groups. Maps were then input to feature extraction procedures. Models were specified with second-level masks and, after training, classifiers were validated by a leave-one-subject-out schedule. The main difference area between groups was found in the white matter below BA6, in the right hemisphere. Weight vector maps showed differences in areas related to attentional processing and auditory stimulus integration. Results point to an association between normal ageing and differences in effective connectivity related to AD. Our results show that degeneration of fibers is complementary to the degeneration of cortical cells, in accordance with the notion that AD is a network disease.