Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities

• Published in: Journal of magnetic resonance imaging Vol. 53; no. 6; pp. 1666 - 1682
• Main Authors: Yeh, Chun‐Hung, Jones, Derek K., Liang, Xiaoyun, Descoteaux, Maxime, Connelly, Alan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2021; Wiley Subscription Services, Inc
• Subjects: Brain; Brain - diagnostic imaging; Connectome; connectomics; Diffusion Magnetic Resonance Imaging; diffusion MRI; Diffusion Tensor Imaging; graph theoretical analysis; Graph theory; Image Processing, Computer-Assisted; Magnetic resonance imaging; Mathematical models; network metrics; Neural networks; Neural Pathways - diagnostic imaging; structural connectome; tractography; network metrics; diffusion MRI; connectomics; tractography; graph theoretical analysis; structural connectome
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.27188

Diffusion MRI‐based tractography is the most commonly‐used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory—a powerful mathematical approach for modeling complex network systems—for analyzing tractography‐based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1