Improved Automatic Morphology-Based Classification of Parkinson's Disease and Progressive Supranuclear Palsy

Clinical Neuroradiology
Aron S TalaiNils D Forkert

Abstract

The overlapping symptoms of Parkinson's disease (PD) and progressive supranuclear palsy-Richardson's syndrome (PSP-RS) often make a correct clinical diagnosis difficult. The volume of subcortical brain structures derived from high-resolution T1-weighted magnetic resonance imaging (MRI) datasets is frequently used for individual level classification of PD and PSP-RS patients. The aim of this study was to evaluate the benefit of including additional morphological features beyond the simple regional volume, as well as clinical features, and morphological features of cortical structures for an automatic classification of PD and PSP-RS patients. A total of 98 high-resolution T1-weighted MRI datasets from 76 PD patients, and 22 PSP-RS patients were available for this study. Using an atlas-based approach, the volume, surface area, and surface-area-to-volume ratio (SA:V) of 21 subcortical and 48 cortical brain regions were calculated and used as features for a support vector machine classification after application of a RELIEF feature selection method. The comparison of the classification results suggests that including all three morphological parameters (volume, surface area and SA:V) can considerably improve classification accuracy c...Continue Reading

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Citations

Mar 30, 2021·International Journal of Geriatric Psychiatry·Sascha GillUNKNOWN Alzheimer's Disease Neuroimaging Initiative
May 20, 2021·Current Opinion in Neurology·Lydia ChougarStéphane Lehéricy

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