Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics

Journal article


Yeh, Chun-Hung, Smith, Robert E., Liang, Xiaoyun, Calamante, Fernando and Connelly, Alan. (2016) Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics. NeuroImage. 142, pp. 150 - 162. https://doi.org/10.1016/j.neuroimage.2016.05.047
AuthorsYeh, Chun-Hung, Smith, Robert E., Liang, Xiaoyun, Calamante, Fernando and Connelly, Alan
Abstract

Diffusion MRI streamlines tractography has become a major technique for inferring structural networks through reconstruction of brain connectome. However, quantification of structural connectivity based on the number of streamlines interconnecting brain grey matter regions is known to be problematic in a number of aspects, such as the ill-posed nature of streamlines terminations and the non-quantitative nature of streamline counts. This study investigates the effects of state-of-the-art connectome construction methods on the subsequent analyses of structural brain networks using graph theoretical approaches. Our results demonstrate that the characteristics of structural connectivity, including connectome variability, global network metrics, small-world attributes and network hubs, alter significantly following the improvement in biological accuracy of streamlines tractograms provided by anatomically-constrained tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT). Importantly, the commonly-used correction for connection density based on scaling the contribution of each streamline to the connectome by its inverse length is shown to provide incomplete correction, highlighting the necessity for the use of advanced tractogram reconstruction techniques in structural connectomics research.

KeywordsDiffusion MRI; Fibre-tracking; Tractography; Structural connectome; Network metrics
Year2016
JournalNeuroImage
Journal citation142, pp. 150 - 162
PublisherElsevier BV
ISSN1053-8119
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neuroimage.2016.05.047
Scopus EID2-s2.0-84994032219
Page range150 - 162
Research GroupMary MacKillop Institute for Health Research
Place of publicationUnited States of America
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