A robust method for the detection of small changes in relaxation parameters and free water content in the vicinity of the substantia nigra in Parkinson's disease patients

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  • Krzysztof Dzieciol, Jülich Research Centre
  • ,
  • Elene Iordanishvili, Jülich Research Centre
  • ,
  • Zaheer Abbas, Jülich Research Centre
  • ,
  • Adjmal Nahimi
  • Michael Winterdahl
  • N. Jon Shah, Jülich Research Centre, RWTH Aachen University

Alterations in the substantia nigra are strongly associated with Parkinson's disease. However, due to low contrast and partial volume effects present in typical MRI images, the substantia nigra is not of sufficient size to obtain a reliable segmentation for region-of-interest based analysis. To combat this problem, the approach proposed here offers a method to investigate and reveal changes in quantitative MRI parameters in the vicinity of substantia nigra without any a priori delineation. This approach uses an alternative method of statistical, voxel-based analysis of quantitative maps and was tested on 18 patients and 15 healthy controls using a well-established, quantitative free water mapping protocol. It was possible to reveal the topology and the location of pathological changes in the substantia nigra and its vicinity. Moreover, a decrease in free water content, T1 and T2* in the vicinity of substantia nigra was indicated in the Parkinson's disease patients compared to the healthy controls. These findings reflect a disruption of grey matter and iron accumulation, which is known to lead to neurodegeneration. Consequently, the proposed method demonstrates an increased sensitivity for the detection of pathological changes-even in small regions-and can facilitate disease monitoring via quantitative MR parameters.

Original languageEnglish
Article numbere0247552
JournalPLOS ONE
Volume16
Number of pages16
ISSN1932-6203
DOIs
Publication statusPublished - Feb 2021

Bibliographical note

Publisher Copyright:
Copyright: © 2021 Dzieciol et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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