RT Journal Article
SR Electronic
T1 Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 523100
DO 10.1101/523100
A1 Sumeet Shinde
A1 Shweta Prasad
A1 Yash Saboo
A1 Rishabh Kaushick
A1 Jitender Saini
A1 Pramod Kumar Pal
A1 Madhura Ingalhalikar
YR 2019
UL http://biorxiv.org/content/early/2019/01/17/523100.abstract
AB 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). PD is characterized by loss of dopaminergic neurons in SNc and 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 based on convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. The technique not only performs with a superior cross-validation accuracy (83.7%) as well as testing accuracy (80%) as compared to contrast ratio-based classification (52.7% cross-validation and 56.5% testing accuracy) and radiomics based classifier (81.1% cross-validation and 60.3% testing accuracy); but also locates 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.