Using 3D CNN for classification of Parkinson’s disease from resting-state fMRI data

Abstract

Parkinson’s disease is a chronic and progressive movement disorder caused by the degeneration of dopamine-producing neurons in the substantia nigra of the brain. Currently, there is no specific diagnostic test available for Parkinson’s disease, and physicians rely on symptoms and medical history for diagnosis. In this study, a 3D-CNN deep learning model is proposed for detecting Parkinson’s disease using 4D-fMRI data. The data is preprocessed using independent component analysis (ICA) and dual regression processes through MELODIC in FSL, which results in a sequence of 30 3D spatial maps, each with its unique time course. A reference network, referred to as an atlas, is then applied using the fslcc command in FSL to map the 3D spatial maps. Fourteen resting-state networks (RSNs) are identified successfully, while the remaining maps are rejected as noise or artifacts. The detected RSNs or 3D spatial maps are fed into the 3D-CNN model, which is trained with a 10-fold cross-validation method. The proposed model has an accuracy of 86.07% on average.