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Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue,

Koji Kamagata¹, Andrew Zalesky², Taku Hatano³

¹Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Parkville, VIC, Australia.

Neuroimage. Clinical

|December 5, 2017

Summary

This study reveals that advanced MRI tractography, specifically probabilistic multi-shell, multi-tissue constrained spherical deconvolution (MSMT-CSD), detects significant white matter connectivity disruptions in Parkinson's disease (PD) patients. This method offers superior diagnostic accuracy for evaluating connectome pathology in PD.

Keywords:

CSD, constrained spherical deconvolution CSF, cerebrospinal fluid Connectome DW-MRI, diffusion-weighted magnetic resonance imaging Diffusion MRI Diffusion tensor imaging GM, gray matter Lewy bodies MSMT-CSD, multi-shell, multi-tissue CSD Neurodegenerative disorders PD, Parkinson's disease SVM, support vector machine Support vector machine UPDRS, Unified Idiopathic Parkinson's Disease Rating Scale WM, white matter fODF, fiber orientation distribution function

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Area of Science:

Neuroimaging
Neuroscience
Medical Physics

Background:

Parkinson's disease (PD) is a progressive neurodegenerative disorder impacting the central nervous system.
Understanding white matter connectivity disruptions is crucial for diagnosing and managing PD.
Previous tractography methods have limitations in precisely mapping the structural connectome.

Purpose of the Study:

To evaluate the structural connectome of PD patients using advanced diffusion-weighted MRI tractography.
To compare the efficacy of multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) with single-shell, single-tissue (SSST) CSD methods.
To assess the diagnostic accuracy of different tractography methods in identifying PD-related connectome alterations.

Main Methods:

Probabilistic MSMT-CSD tractography was applied to 21 PD patients and 21 age/gender-matched controls.
Deterministic and probabilistic SSST-CSD were used for comparative analysis.
A support vector machine classifier was trained on graph metrics to predict diagnosis.

Main Results:

Probabilistic MSMT-CSD detected significant reductions in global strength, efficiency, clustering, and small-worldness, alongside increased path length in PD patients.
SSST-CSD methods showed less sensitivity, only detecting differences in global strength and small-worldness.
MSMT-CSD identified localized disruptions in motor, associative, limbic, basal ganglia, and thalamic areas and demonstrated superior diagnostic accuracy.

Conclusions:

Probabilistic MSMT-CSD provides a more precise and comprehensive assessment of white matter connectome pathology in Parkinson's disease.
This advanced method reveals widespread disruptions, including within the cortico-basal ganglia-thalamocortical network, offering potential for improved diagnostic evaluation.
Connectome analysis using probabilistic MSMT-CSD is a valuable tool for quantifying white matter connectivity disruptions in PD.