Communicability distance reveals hidden patterns of Alzheimer’s disease

Abstract

The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer’s disease. Using this distance, we obtain the shortest communicability path lengths between different regions of brain networks from patients with Alzheimer’s disease (AD) 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% affect vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of AD. Finally, we show that in 76.9% of 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.

Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

“…First, we provide evidence that network communication models—computed on structural connectivity inferred from tractography and MRI—reflect patterns of spatially-resolved, millisecond resolution, stimulus propagation measured across the whole brain. Our results recapitulate previous findings that structural connectivity shapes fast brain dynamics [10], and corroborate the use of network communication models to study neural information processing in healthy [54, 55] and clinical populations [20–24].…”

“…Network communication models provide tractable interpretations of the interplay between structural connectivity and inter-areal interactions, and can thus be used to form and test hypotheses about the relationship between brain structure and function. An emerging body of evidence indicates that these models can explain inter-individual variation in cognitive [18,19] and clinical [20][21][22][23][24] variables, as well as various aspects of functional and effective connectivity derived from blood-level-oxygen dependent (BOLD) fMRI time courses [16,[25][26][27][28][29][30][31][32][33][34].…”

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).…”

Editorial: Network Communication in the Brain

“…Three papers in this Focus Feature examine mechanisms that are novel or that are applied in a new context. Lella et al (2020) use a model of network communication inspired by infectious disease spreading to illuminate Alzheimer's disease (AD). They construct an analytical model of networkwide communication from structural imaging of healthy humans and those with AD.…”

“…Hao and Graham (2020) focus on collisions, which are ubiquitous in large-scale engineered communication systems. They compare numerical simulations of two routing protocols when collisions are considered: a standard random walk strategy and an "information spreading" scheme similar to the infectious disease model of Lella et al (2020). In simulations on two tracer-based connectomes of the macaque monkey cortex and one of the mouse whole brain, Hao and Graham (2020) show that information spreading actually achieves lower overall activity and greater sparseness of activity compared to a random walk model.…”