A multi-compartment model for pathological connectomes

Brain connectivity analysis is pivotal to understanding mechanisms underpinning neurological diseases. However, current methodologies for quantitatively mapping the connectivity in vivo face challenges when focal lesions are present and can introduce strong biases in the estimates. We present a novel approach to address these challenges by introducing a multi-compartment description of the connectome, which explicitly incorporates lesion information during the estimation process. We extended the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework to integrate an additional tissue compartment in voxels affected by pathology, allowing us to infer accurately the contributions of streamlines passing through lesions and to provide unbiased connectivity estimates. We evaluated the effectiveness of our approach on data from healthy subjects of the Human Connectome Project, in which we artificially introduced focal lesions to simulate pathology with varying levels of axonal damage. We also tested the performances obtained when comparing healthy subjects with patients affected by multiple sclerosis. Results demonstrate that our approach significantly enhances sensitivity to pathological changes even at low degeneracy levels compared with state-of-the-art techniques, thus representing a significant step forward to advance our understanding of neurodegenerative diseases.