Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI

TLDR

Neuromelanin‑sensitive MRI detects substantia nigra abnormalities in Parkinson’s disease, but existing contrast‑ratio methods are laborious, low‑accuracy, and miss subtle patterns. This study develops a convolutional‑neural‑network–based analysis to generate prognostic and diagnostic biomarkers of Parkinson’s disease from NMS‑MRI. The CNN processes NMS‑MRI images, automatically extracting features and highlighting discriminative regions within the substantia nigra. The CNN achieves 80 % test accuracy versus 56.5 % for contrast ratios and 60.3 % for radiomics, discriminates atypical parkinsonian syndromes at 85.7 %, and identifies the left substantia nigra as the key discriminative region, supporting asymmetry in Parkinson’s disease.

Abstract

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Current techniques employ estimation of contrast ratios of the SNc, visualized on NMS-MRI, to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique that uses convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. Our technique not only performs with a superior testing accuracy (80%) as compared to contrast ratio-based classification (56.5% testing accuracy) and radiomics classifier (60.3% testing accuracy), but also supports discriminating PD from atypical parkinsonian syndromes (85.7% test accuracy). Moreover, it has the capability to locate the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc.