Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson's disease. (January 2021)

• Record Type: Journal Article
• Authors: Solana-Lavalle, Gabriel; Rosas-Romero, Roberto
• Is Part Of: Computer methods and programs in biomedicine. Volume 198(2021)
• Publication Date: 2021-01
• Subjects: Parkinson's disease -- Magnetic resonance imaging -- Voxel-based morphometry -- Machine learning -- Feature selection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105793
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: MRI-based PD detection is assisted by conducting separate experiments in men and women. Voxel-based morphometry is used to obtain regions of interest for feature extraction. Almost all selected features arise from the co-occurrence matrix of 3-D MRI scans. 99.01% of accuracy in men and 96.97% of accuracy in women. Interesting findings are confirmed by neurobiology in men and women. Graphical abstract: Abstract: Background and objectives : Qualitative and quantitative analyses of Magnetic Resonance Imaging (MRI) scans are carried out to study and understand Parkinson's Disease, the second most common neurodegenerative disorder in people at their 60's. Some quantitative analyses are based on the application of voxel-based morphometry (VBM) on magnetic resonance images to determine the regions of interest, within gray matter, where there is a loss of the nerve cells that generate dopamine. This loss of dopamine is indicative of Parkinson's disease. The purpose of this research is the introduction of a new method to classify the 3-D magnetic resonance scans of an individual, as an assisting tool for diagnosis of Parkinson's disease by using the largest MRI dataset ( Parkinson's Progression Markers Initiative ) from a population of patients with Parkinson's disease and control individuals. A contribution is that separate studies are conducted for men and women since gender plays a significant role within Neurobiology, which is demonstrated by the fact that men are more prone to Parkinson's disease than women are. Methods : Previous to classification, VBM is conducted on magnetic resonance images to detect the regions where features are extracted by using first- and second-order statistics methods. Furthermore, the number of features is considerably reduced by using feature selection techniques . Seven classifiers are used and we are conducting separate experiments for men and women. Results : The best detection performance achieved in men is 99.01% of accuracy, 99.35% of sensitivity, 100% of specificity, and 100% of precision. The best detection performance achieved in women is 96.97% of accuracy, 100% of sensitivity, 96.15% of specificity, and 97.22% of precision. During classification of magnetic resonance images, the corresponding computational complexity is reduced since few features are selected. Conclusions : The proposed method provides high performance as an assisting tool in the diagnosis of Parkinson's disease, by conducting separate experiments in men and women. While previous works have focused their analysis to the striatum region of the brain (the largest nuclear complex of the basal ganglia), the proposed approach is based on analysis over the whole brain by looking for decreases of tissue thickness, with the consequence of finding other regions of interest such as the cortex.