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

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.

Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network

“…The approaches included combinations of transfer learning, clustering [26], and nearest neighbors for enhancing the classification performance of the model. Shinde et al [27] in his work regarding the detection of Parkinson's Disease leveraged neuromelanin sensitive MRI (NMS-MRI), which can identify abnormalities in the substantia nigra (SNc) in Parkinson's Disease patients. Moreover, the author also plotted concerns over the handcrafted features based on the contrast ratio, area, and volumes of the subcortical structures [28][29][30][31].…”

Microstructural abnormalities of substantia nigra in Parkinson's disease: A neuromelanin sensitive MRI atlas based study

“…Multiple studies have demonstrated the clinical utility and accuracy of this sequence in patients with PD (Castellanos et al, 2015;Ohtsuka et al, 2013;Ohtsuka et al, 2014;Schwarz et al, 2011). To quantify these differences, the processing techniques that are currently used are based on visual inspection, or manual region of interest drawing followed by computation of volumes, contrast ratios or radiomics features and are arduous and time-consuming (Isaias et al, 2016;Kashihara, Shinya, & Higaki, 2011;Matsuura et al, 2013;Matsuura et al, 2016;Ogisu et al, 2013;Ohtsuka et al, 2014;Reimao et al, 2015;Sasaki et al, 2006;Schwarz et al, 2011;Shinde et al, 2019). To overcome this, NMS-MRI sequence can be employed to accurately localize and create a template of the SNc that can be utilized for analysis in parkinsonian disorders.…”

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

“…By using 3D-CNN [57] achieved 100% accuracy on the validation and test sets for PD diagnosis. At the same time, the study in [76] discriminated PD from typical parkinsonian syndromes having 85.7% test accuracy.…”

“…Shinde et al proposed to differentiate PD from HC by employing a fully automated CNN with discriminative localization architecture for creating prognostic and diagnostic biomarkers of PD from Neuro-melanin sensitive MRI or NMS-MRI [76]. For this work, data have been collected from the Department of Neurology, National Institute of Mental Health and Neuro sciences (NIMHANS) which consist of MR imaging, demographic and clinical details such as gender, age at presentation, age at onset of motor symptoms, disease duration, etc., data of PD patients, atypical parkinsonian syndromes (APS) patients, multiple system atrophy (MSA) patients and progressive supranuclear palsy along with some HC as well [76]. The authors were able to capture the subtle changes in PD in the substantia nigra pars compacta (SNc) using selected features from the NMS-MRI.…”