Prediction of Multiple Sclerosis Patient Disability from Structural Connectivity using Convolutional Neural Networks

Prediction of disability progression in multiple sclerosis patients is a critical component of their management. In particular, one challenge is to identify and characterize a patient profile who may benefit of efficient treatments. However, it is not yet clear whether a particular relation exists between the brain structure and the disability status.This work aims at producing a fully automatic model for the expanded disability status score estimation, given the brain structural connectivity representation of a multiple sclerosis patient. The task is addressed by first extracting the connectivity graph, obtained by combining brain grey matter parcellation and tractography extracted from Diffusion and T1-weighted Magnetic Resonance (MR) images, and then processing it via a convolutional neural network (CNN) in order to compute the predicted score. Experiments show that the herein proposed approach achieves promising results, thus resulting as an important step forward on the road to better predict the evolution of the disease.