Communicability disruption in Alzheimer’s disease connectivity networks

In real-world networks, information from source to destination does not only flow along the shortest path connecting them, but can flow along any alternative route. Communicability is a network metric that accounts for this issue and, especially in diffusion-like processes, provides a reliable measure of the ease of communication between node pairs. Accordingly, communicability appears to be promising for highlighting the disruption of connectivity among brain regions, caused by the white matter degeneration due to Alzheimer's disease (AD). Such a degeneration can be captured by digital imaging techniques, in particular diffusion tensor imaging (DTI), which allow to build the brain connectivity network through tractography algorithms and studying its complexity through graph theory. In this study, a cohort of 122 DTI scans, composed by 52 healthy control (HC) subjects, 40 AD patients and 30 mild cognitive impairment (MCI) converter subjects, from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, has been employed to study the suitability of communicability to serve as discriminant factor for AD. We developed a two-fold investigation. On one hand, a statistical analysis has been carried out to ascertain the information content provided by communicability to detect the brain regions mostly affected by the disease: node pairs with statistical significant different communicability have been found, corresponding to some well-known AD-related brain regions. On the other hand, heterogeneous groups of network features (which include/not include communicability) were input to a support vector machine, to assess the impact of communicability on the classification performances in the HC/AD and the HC/AD/MCI discrimination. The best performances, i.e., AUC = 0.82 in the HC/AD case and multiclass AUC = 0.77 in the HC/AD/MCI task, were obtained by using the values of communicability, outperforming the performance obtained with the other network metrics. In summary, this article suggests that communicability can be promising for an automatized AD diagnosis.