Premium
Mapping Structural Connectivity Using Diffusion MRI : Challenges and Opportunities
Author(s) -
Yeh ChunHung,
Jones Derek K.,
Liang Xiaoyun,
Descoteaux Maxime,
Connelly Alan
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27188
Subject(s) - connectome , tractography , diffusion mri , computer science , connectomics , human connectome project , graph theory , graph , artificial intelligence , data science , complex network , machine learning , neuroscience , functional connectivity , theoretical computer science , psychology , magnetic resonance imaging , mathematics , medicine , world wide web , combinatorics , radiology
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