Three‐dimensional neuromelanin‐sensitive magnetic resonance imaging of the substantia nigra in Parkinson's disease

Chougule

et al. 2019

Human Brain Mapping

Microstructural changes associated with degeneration of dopaminergic neurons of the substantia nigra pars compacta (SNc) in Parkinson's 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 3D 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 registration we created a novel SNc atlas in the MNI space using NMS‐MRI sequences of 27 healthy controls (HC). We first quantitatively evaluated this atlas and then employed it to investigate the micro‐structural abnormalities in SNc using diffusion MRI from 133 patients with PD and 99 HCs. Our results demonstrated significant increase in diffusivity with no changes in anisotropy. In addition, we also observed an asymmetry of the diffusion metrics with a higher diffusivity and lower anisotropy in the left SNc than the right. Finally, a multivariate classifier based on SNc diffusion features could delineate patients with PD with an average accuracy of 71.7%. 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.

Section: Methodsmentioning

confidence: 99%

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

Safai

Chougule

et al. 2019

Human Brain Mapping

“…Another group of 27 healthy controls whose NMS-MRI sequence was acquired as part of a different study (Prasad, Stezin, et al, 2018) of the same group, was used in the construction of our probabilistic atlas.…”

Section: Subject Recruitment and Clinical Evaluationmentioning

confidence: 99%

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

Safai

Saini

et al. 2019

Preprint

Microstructural changes associated with degeneration of dopaminergic neurons of the substantia nigra pars compacta (SNc) in Parkinson"s 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.

“…The diagnosis of idiopathic PD was based on the UK Parkinson's Disease Society Brain Bank criteria (Hughes, Daniel, Blankson, & Lees, 1993) and confirmed by a trained movement disorder specialist (author PKP). Patients included in this study have been part of other studies from this group Prasad, Stezin, et al, 2018) and all patients and controls provided informed consent prior to recruitment in the original projects.…”

Section: Subject Recruitment and Clinical Evaluationmentioning

confidence: 99%

“…To compute the contrast ratio, technique described in Prasad et al (Prasad, Stezin, et al, 2018) was employed. A section of the midbrain at the midpoint of the mamillary bodies was selected for placement of ROIs.…”

Section: Contrast Ratio Computationmentioning

confidence: 99%

“…Following this, several studies have demonstrated the utility of this sequence to differentiate between patients with PD and controls using manually extracted features of the SNc that include the contrast ratio, area and volumes (Matsuura et al, 2013;Ogisu et al, 2013;Ohtsuka et al, 2013;Prasad, Stezin, et al, 2018). Between these features the contrast ratios are well-accepted as they have demonstrated the highest discriminative power.…”

Section: Introductionmentioning

confidence: 99%

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

Shinde

Saboo

et al. 2019

Preprint

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.