Abstract
Alzheimer’s disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, this question is addressed at the group-level by looking to differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer’s Disease Neuroimaging Initiative (ADNI), 4 different groups were defined (all of them matched by age, sex and education level): (G1(N1 = 36, healthy control participants, HC), G2(N2 = 36, early mild cognitive impairment, EMCI), (G3(N3 = 36, late mild cognitive impairment, LMCI) and (G4(N4 = 36, AD). We built diffusion-tensor brain networks and performed group comparison across 3 disease stages: stage I (HC vs EMCI), stage II (HC vs LMCI) and stage III (HC vs AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle functional network data, but here was applied to diffusion-tensor data. The results were three-fold: First, no significant differences were found in stage I. Second, in stage II, statistically significant differences were found in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting stronger to the same memory subnetwork appearing in stage II plus to other subnetworks, including the default mode network, the medial visual network, frontoparietal regions, and subnetworks encompassing mainly subcortical structures (including part of the hippocampus, amygdala and putamen). The novelty of the approach lies in the fact that group differences were approached across severity progression. A better possibility would have been to analyze well time-resolved longitudinal data, building diffusion-tensor networks belonging to the same patient across all disease stages (from control to AD), but such data (to the best of our knowledge) are not available yet.