Towards accurate and unbiased imaging-based differentiation of Parkinson's disease, progressive supranuclear palsy and corticobasal syndrome.

Brain Communications
Marta M CorreiaJ. B. Rowe

Abstract

The early and accurate differential diagnosis of parkinsonian disorders is still a significant challenge for clinicians. In recent years, a number of studies have used magnetic resonance imaging data combined with machine learning and statistical classifiers to successfully differentiate between different forms of Parkinsonism. However, several questions and methodological issues remain, to minimize bias and artefact-driven classification. In this study, we compared different approaches for feature selection, as well as different magnetic resonance imaging modalities, with well-matched patient groups and tightly controlling for data quality issues related to patient motion. Our sample was drawn from a cohort of 69 healthy controls, and patients with idiopathic Parkinson's disease (n = 35), progressive supranuclear palsy Richardson's syndrome (n = 52) and corticobasal syndrome (n = 36). Participants underwent standardized T1-weighted and diffusion-weighted magnetic resonance imaging. Strict data quality control and group matching reduced the control and patient numbers to 43, 32, 33 and 26, respectively. We compared two different methods for feature selection and dimensionality reduction: whole-brain principal components analysi...Continue Reading

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Citations

Mar 19, 2021·Journal of Neurology, Neurosurgery, and Psychiatry·Maura MalpettiJames Benedict Rowe
May 20, 2021·Current Opinion in Neurology·Lydia ChougarStéphane Lehéricy
Aug 25, 2021·Nature Reviews. Neurology·Maria StamelouGünter U Höglinger

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Methods Mentioned

BETA
MDS
feature extraction
PCA
feature

Software Mentioned

matlab
DTI
FSL
DARTEL ( Diffeomorphic Anatomical Registration Through Exponen...
eddy
DARTEL
TK
SPM
LIBSVM
Segment

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