Early Detection of Parkinson’s Disease Using Parametric Features and Advanced fMRI Analysis

Abstract

According to the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder among older adults, surpassed only by Alzheimer’s disease. PD is characterized by a heterogeneous combination of motor and non-motor symptoms, with involuntary movements constituting a central clinical defining feature. The prodromal phase presents substantial diagnostic challenges, as early symptoms are often mild, nonspecific, and commonly misinterpreted because overt motor signs are absent. Significantly, these early alterations can precede formal diagnosis by up to two decades, highlighting the urgent need for effective early detection strategies. In this work, we propose a functional Magnetic Resonance Imaging (fMRI)-based image-processing and machine learning framework for early detection of PD. Statistical features are extracted to train a set of 25 machine learning kernels, from which the most accurate model is selected. We then apply the Minimum Redundancy Maximum Relevance (mRMR) algorithm to identify the most descriptive fMRI frames for each subject. We introduce a hierarchical classification strategy: an initial binary classification to distinguish control subjects from prodromal + PD cases, followed by a second binary classification to discriminate between Prodromal and PD. The experimental results demonstrate a maximum precision of 96.3% with 16 axial slices and 86.4% with a single slice, indicating the effectiveness of the preprocessing strategy and its potential as a non-invasive biomarker for early PD detection, even with reduced input data. These findings suggest that high classification performance can be achieved with a limited number of fMRI slices, facilitating data acquisition and reducing subject burden in clinical studies.