A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles

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Abstract

In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.

Highlights

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A diffusion MRI protocol to recover robust disease-specific biomarkers is proposed.
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A test-retest reproducibility assessment is applied to 50 dissected WM fascicles.
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Statistical analysis of tractometry results is performed on these robust fascicles.
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Differences are found along the nigro-subthalamo-putaminal-thalamo-cortical pathway.
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These results are crucial to improve existing treatments like Deep Brain Stimulation.

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Most cited references 35

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Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.

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Author and article information

Contributors

Martin Cousineau

Journal

Journal ID (nlm-ta): Neuroimage Clin

Journal ID (iso-abbrev): Neuroimage Clin

Title: NeuroImage : Clinical

Publisher: Elsevier

ISSN (Electronic): 2213-1582

Publication date PMC-release: 25 July 2017

Publication date Collection: 2017

Publication date (Electronic): 25 July 2017

Volume: 16

Pages: 222-233

Affiliations

[a ]Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada

[b ]Imeka Solutions Inc., Sherbrooke, QC, Canada

[c ]Department of Neurology, Klinikum Grosshadern, University of Munich, Germany

[d ]Biospective Inc., Montréal, QC, Canada

[e ]McGill University, Montréal, QC, Canada

Author notes

[* ]Corresponding author at: Université de Sherbrooke, Computer Science Department, 2500, de l’Université, Sherbrooke J1K 2R1, PQ, Canada.Université de SherbrookeComputer Science Department2500, de l’UniversitéSherbrookePQJ1K 2R1Canada martin.p.cousineau@ 123456usherbrooke.ca

Article

Publisher ID: S2213-1582(17)30186-9

DOI: 10.1016/j.nicl.2017.07.020

PMC ID: 5547250

PubMed ID: 28794981

SO-VID: 2d1363d9-4278-4282-88a6-e9de7ba871d6

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 12 March 2017

Date revision received : 13 July 2017

Date accepted : 22 July 2017

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A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles

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Most cited references 35

Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

Functional significance of the cortico-subthalamo-pallidal 'hyperdirect' pathway.

Morphometric analysis of white matter lesions in MR images: method and validation.

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