Graph theory and network modelling have been previously applied to characterize motor network structural topology in multiple sclerosis (MS). However, between-group differences disclosed by graph analysis might be primarily driven by discrepancy in density, which is likely to be reduced in pathologic conditions as a consequence of macroscopic damage and fibre loss that may result in less streamlines properly traced. In this work, we employed the convex optimization modelling for microstructure informed tractography (COMMIT) framework, which, given a tractogram, estimates the actual contribution (or weight) of each streamline in order to optimally explain the diffusion magnetic resonance imaging signal, filtering out those that are implausible or not necessary. Then, we analysed the topology of this 'COMMIT-weighted sensory-motor network' in MS accounting for network density. By comparing with standard connectivity analysis, we also tested if abnormalities in network topology are still identifiable when focusing on more 'quantitative' network properties. We found that topology differences identified with standard tractography in MS seem to be mainly driven by density, which, in turn, is strongly influenced by the presence of lesions. We were able to identify a significant difference in density but also in network global and local properties when accounting for density discrepancy. Therefore, we believe that COMMIT may help characterize the structural organization in pathological conditions, allowing a fair comparison of connectomes which considers discrepancies in network density. Moreover, discrepancy-corrected network properties are clinically meaningful and may help guide prognosis assessment and treatment choice.
Keywords: COMMIT; diffusion MRI; graph theory; motor network; multiple sclerosis; structural connectivity; tractography.
© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.