A machine learning-based classification approach on Parkinson's disease diffusion tensor imaging datasets

Jannik Prasuhn; Marcus Heldmann; Thomas F Münte; Norbert Brüggemann

doi:10.1186/s42466-020-00092-y

A machine learning-based classification approach on Parkinson's disease diffusion tensor imaging datasets

Neurol Res Pract. 2020 Nov 10:2:46. doi: 10.1186/s42466-020-00092-y. eCollection 2020.

Authors

Jannik Prasuhn^{1

2}, Marcus Heldmann^{2

3}, Thomas F Münte², Norbert Brüggemann^{1

2}

Affiliations

¹ Department of Neurology, Institute of Neurogenetics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.
² Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.
³ Institute of Psychology II, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.

Abstract

Introduction: The presence of motor signs and symptoms in Parkinson's disease (PD) is the result of a long-lasting prodromal phase with an advancing neurodegenerative process. The identification of PD patients in an early phase is, however, crucial for developing disease-modifying drugs. The objective of our study is to investigate whether Diffusion Tensor Imaging (DTI) of the Substantia nigra (SN) analyzed by machine learning algorithms (ML) can be used to identify PD patients.

Methods: Our study proposes the use of computer-aided algorithms and a highly reproducible approach (in contrast to manually SN segmentation) to increase the reliability and accuracy of DTI metrics used for classification.

Results: The results of our study do not confirm the feasibility of the DTI approach, neither on a whole-brain level, ROI-labelled analyses, nor when focusing on the SN only.

Conclusions: Our study did not provide any evidence to support the hypothesis that DTI-based analysis, in particular of the SN, could be used to identify PD patients correctly.

Keywords: DTI; Machine learning; Neuroimaging; Parkinson’s disease; Substantia nigra.