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Title: Connectomes from streamlines tractography: Assigning streamlines to brain parcellations is not trivial but highly consequential.
Austin Authors: Yeh, Chun-Hung;Smith, Robert E;Dhollander, Thijs;Calamante, Fernando;Connelly, Alan
Affiliation: The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Issue Date: 11-May-2019
Date: 2019-05-11
Publication information: NeuroImage 2019; 199: 160-171
Abstract: When using diffusion MRI streamlines tractograms to construct structural connectomes, ideally, each streamline should connect exactly 2 regions-of-interest (i.e. network nodes) as defined by a given brain parcellation scheme. However, the ill-posed nature of termination criteria in many tractography algorithms can cause streamlines apparently being associated with zero, one, or more than two grey matter (GM) nodes; streamlines that terminate in white matter or cerebrospinal fluid may even end up being assigned to nodes if the definitions of these nodes are not strictly constrained to genuine GM areas, resulting in a misleading connectome in non-trivial ways. Based on both in-house MRI data and state-of-the-art data provided by the Human Connectome Project, this study investigates the actual influence of streamline-to-node assignment methods, and their interactions with fibre-tracking terminations and brain parcellations, on the construction of pairwise regional connectivity and subsequent connectomic measures. Our results show that the frequency of generating successful pairwise connectivity is heavily affected by the convoluted interactions between the applied strategies for connectome construction, and that minor changes in the mechanism can cause significant variations in the within- and between-module connectivity strengths as well as in the commonly-used graph theory metrics. Our data suggest that these fundamental processes should not be overlooked in structural connectomics research, and that improved data quality is not in itself sufficient to solve the underlying problems associated with assigning streamlines to brain nodes. We demonstrate that the application of advanced fibre-tracking techniques that are designed to correct for inaccuracies of track terminations with respect to anatomical information at the fibre-tracking stage are advantageous to the subsequent connectome construction process, in which pairs of parcellation nodes can be more robustly identified from streamline terminations via a suitable assignment mechanism.
DOI: 10.1016/j.neuroimage.2019.05.005
ORCID: 0000-0002-7550-3142
Journal: NeuroImage
PubMed URL: 31082471
Type: Journal Article
Subjects: Brain parcellation
Diffusion MRI
Network metrics
Structural connectome
Appears in Collections:Journal articles

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