Biology subjects

Ingalhalikar, M.

Publications and source records attributed to Ingalhalikar, M..

2 recordsLinked to original sources

bioRxiv2019

Microstructural Abnormalities of Substantia Nigra in Parkinson’s disease: A Neuromelanin Sensitive MRI Atlas Based Study

Microstructural changes associated with degeneration of dopaminergic neurons of the substantia nigra pars compacta (SNc) in Parkinsons disease (PD) have been studied using Diffusion Tensor Imaging (DTI). However, these studies show inconsistent results, mainly due to methodological variations in delineation of SNc. To mitigate this, our work aims to construct a probabilistic atlas of SNc based on a Neuromelanin sensitive MRI (NMS-MRI) sequence and demonstrate its applicability to investigate microstructural changes on a large dataset of PD. Using manual segmentation and deformable registrations, we create a novel SNc atlas in the MNI space using NMS-MRI sequences of 27 healthy controls (HC). We employ this atlas to evaluate the diffusivity and anisotropy measures, derived from diffusion MRI in the SNc of 135 patients with PD and 99 HCs. Our observations of significantly increased diffusivity measures provide evidence of microstructural abnormalities in PD. However, no changes in the anisotropy were observed. Moreover, the asymmetry in abnormalities is prominent as the left SNc showed significant increase in diffusivity, and a reduction in FA when compared to the right SNc. Further the diffusivity and FA values also demonstrated a trend when correlated with the PD severity scores. Overall, from this work we establish a normative baseline for the SNc region of interest using NMS-MRI while the application on PD data emphasizes on the contribution of diffusivity measures rather than anisotropy of white matter in PD.

neuroscience↗

bioRxiv2019

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

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinsons 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.

neuroscience↗