Unifying inference on brain network variations in neurological diseases: The Alzheimer's case

TL;DR

This paper introduces a novel probabilistic framework for analyzing brain network variations associated with neurological diseases, enabling more comprehensive insights than traditional edge-by-edge methods.

Contribution

It proposes a unifying methodology using dependent mixtures of low-rank factorizations to model brain network differences related to neurological diseases.

Findings

Enhanced ability to distinguish disease-related network patterns

Flexible modeling of network variations across patient groups

Improved biological interpretability of brain network data

Abstract

There is growing interest in understanding how the structural interconnections among brain regions change with the occurrence of neurological diseases. Diffusion weighted MRI imaging has allowed researchers to non-invasively estimate a network of structural cortical connections made by white matter tracts, but current statistical methods for relating such networks to the presence or absence of a disease cannot exploit this rich network information. Standard practice considers each edge independently or summarizes the network with a few simple features. We enable dramatic gains in biological insight via a novel unifying methodology for inference on brain network variations associated to the occurrence of neurological diseases. The key of this approach is to define a probabilistic generative mechanism directly on the space of network configurations via dependent mixtures of low-rank factorizations, which efficiently exploit network information and allow the probability mass function for the brain network-valued random variable to vary flexibly across the group of patients characterized by a specific neurological disease and the one comprising age-matched cognitively healthy individuals.