Communicability distance reveals hidden patterns of Alzheimer disease

Abstract

<jats:title>Abstract</jats:title><jats:p>The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer disease. Using this distance we obtain the shortest communicability path lengths between different regions of brain networks from Alzheimer diseased (AD) patients and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only and 20% affects vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of Alzheimer disease. Finally, we show that in 76.9% damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.</jats:p>

Network communication models narrow the gap between the modular organization of structural and functional brain networks

“…Finally, our results corroborate previous reports on the utility of communicability to investigate a range of diverse neuroscience questions [39]. Examples include studies on the impact of stroke lesions [69], effects of neurodegeneration [70], simulations of neural gain fluctuations [71], and pharmacogenetic manipulation of brain regions [72]. More generally, we add to mounting empirical evidence challenging the notion that communication in brain networks occurs exclusively via topological shortest paths [30, 31, 73], an assumption built into many popular graph measures in network neuroscience (e.g., betweenness centrality or global efficiency).…”