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dc.contributor.authorYeh, C-H
dc.contributor.authorJones, DK
dc.contributor.authorLiang, X
dc.contributor.authorDescoteaux, M
dc.contributor.authorConnelly, A
dc.date.accessioned2020-11-27T00:23:08Z
dc.date.available2020-11-27T00:23:08Z
dc.date.issued2020-06-17
dc.identifier.citationYeh, C. -H., Jones, D. K., Liang, X., Descoteaux, M. & Connelly, A. (2020). Mapping Structural Connectivity Using DiffusionMRI: Challenges and Opportunities. JOURNAL OF MAGNETIC RESONANCE IMAGING, 53 (6), pp.1666-1682. https://doi.org/10.1002/jmri.27188.
dc.identifier.issn1053-1807
dc.identifier.urihttp://hdl.handle.net/11343/252444
dc.description.abstractDiffusion 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.
dc.languageEnglish
dc.publisherWILEY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleMapping Structural Connectivity Using DiffusionMRI: Challenges and Opportunities
dc.typeJournal Article
dc.identifier.doi10.1002/jmri.27188
melbourne.affiliation.departmentFlorey Department of Neuroscience and Mental Health
melbourne.source.titleJournal of Magnetic Resonance Imaging
melbourne.source.volume53
melbourne.source.issue6
melbourne.source.pages1666-1682
dc.rights.licensecc-by
melbourne.elementsid1453394
melbourne.contributor.authorLiang, Xiaoyun
melbourne.contributor.authorConnelly, Alan
dc.identifier.eissn1522-2586
melbourne.accessrightsOpen Access


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