Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution
AuthorKamagata, K; Zalesky, A; Hatano, T; Di Biase, MA; El Samad, O; Saiki, S; Shimoji, K; Kumamaru, KK; Kamiya, K; Hori, M; ...
Source TitleNeuroImage: Clinical
PublisherELSEVIER SCI LTD
University of Melbourne Author/sPantelis, Christos; Zalesky, Andrew; Kamagata, Koji; Di Biase, Maria
Document TypeJournal Article
CitationsKamagata, K., Zalesky, A., Hatano, T., Di Biase, M. A., El Samad, O., Saiki, S., Shimoji, K., Kumamaru, K. K., Kamiya, K., Hori, M., Hattori, N., Aoki, S. & Pantelis, C. (2018). Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution. NEUROIMAGE-CLINICAL, 17, pp.518-529. https://doi.org/10.1016/j.nicl.2017.11.007.
Access StatusOpen Access
Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography and a multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) method to increase the precision of tractography at tissue interfaces. The connectome was mapped with probabilistic MSMT-CSD in 21 patients with PD and in 21 age- and gender-matched controls. Mapping was also performed by deterministic single-shell, single tissue (SSST)-CSD tracking and probabilistic SSST-CSD tracking for comparison. A support vector machine was trained to predict diagnosis based on a linear combination of graph metrics. We showed that probabilistic MSMT-CSD could detect significantly reduced global strength, efficiency, clustering, and small-worldness, and increased global path length in patients with PD relative to healthy controls; by contrast, probabilistic SSST-CSD only detected the difference in global strength and small-worldness. In patients with PD, probabilistic MSMT-CSD also detected a significant reduction in local efficiency and detected clustering in the motor, frontal temporoparietal associative, limbic, basal ganglia, and thalamic areas. The network-based statistic identified a subnetwork of reduced connectivity by MSMT-CSD and probabilistic SSST-CSD in patients with PD, involving key components of the cortico-basal ganglia-thalamocortical network. Finally, probabilistic MSMT-CSD had superior diagnostic accuracy compared with conventional probabilistic SSST-CSD and deterministic SSST-CSD tracking. In conclusion, probabilistic MSMT-CSD detected a greater extent of connectome pathology in patients with PD, including those with cortico-basal ganglia-thalamocortical network disruptions. Connectome analysis based on probabilistic MSMT-CSD may be useful when evaluating the extent of white matter connectivity disruptions in PD.
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