A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease

• Early detection of Parkinson’s disease using MRI and PBL-McRBFN classifier.
• Whole brain morphometry to detect significant volumetric change in gray matter.
• Recursive feature elimination to detect critical brain region responsible for PD.
• Identified brain regions are detected as affected region in medical autopsy.

In this paper, we present a novel approach for the identification of critical brain regions responsible for Parkinson’s disease (PD) based on magnetic resonance images (MRI) using meta-cognitive radial basis function network (McRBFN) classifier with Recursive Feature Elimination (RFE). The McRBFN classifier uses voxel based morphometric (VBM) features extracted from MRI and employs a projection based learning (PBL) algorithm. The meta-cognitive learning present in PBL-McRBFN helps in selecting proper samples to learn based on its current knowledge and evolve the architecture automatically. Since, the classifier developed using PBL-McRBFN is efficient, we propose recursive feature elimination approach (called PBL-McRBFN-RFE) to identify most relevant brain regions responsible for PD prediction.The study has been conducted using the Parkinson’s Progression Markers Initiative (PPMI) data set. First, we conducted the study on PD prediction using the PBL-McRBFN classifier on the PPMI data set. We have also compared the results of the PBL-McRBFN classifier with the support vector machine (SVM) classifier. The study results clearly show that the PBL-McRBFN classifier produces better generalization performance on PD prediction. Finally, we use RFE approach with PBL-McRBFN to identify the brain regions responsible for PD. The PBL-McRBFN-RFE selected features indicate that the loss of gray matter in the superior temporal gyrus region may be responsible for the onset of PD, and is consistent with the earlier findings from medical research studies.