High resolution magnetic susceptibility mapping of the substantia nigra in Parkinson's disease

2011 ◽  
Vol 35 (1) ◽  
pp. 48-55 ◽  
Author(s):  
Ashley K. Lotfipour ◽  
Samuel Wharton ◽  
Stefan T. Schwarz ◽  
V. Gontu ◽  
Andreas Schäfer ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Magnetic Susceptibility ◽  
High Resolution ◽  
Substantia Nigra ◽  
Susceptibility Mapping

scholarly journals Substantia nigra ferric overload and neuromelanin loss in Parkinson's disease measured with 7T MRI

2021 ◽  
Author(s):  
Catarina Rua ◽  
Claire O'Callaghan ◽  
Rong Ye ◽  
Frank Hubert Hezemans ◽  
Luca Passamonti ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Resolution ◽  
Substantia Nigra ◽  
Clinical Severity ◽  
7 Tesla ◽  
Control Group ◽  
Healthy Controls ◽  
Iron Loading ◽  
Weighted Sequence

Background: Vulnerability of the substantia nigra dopaminergic neurons in Parkinson's disease is associated with ferric overload, leading to neurodegeneration with cognitive and motor decline. Here, we quantify iron and neuromelanin-related markers in vivo using ultra-high field 7-Tesla MRI, and examine the clinical correlates of these imaging assessments. Methods: Twenty-five people with mild-to-moderate Parkinson's disease and twenty-six healthy controls underwent high-resolution imaging at 7-Tesla with a T2*-weighted sequence (measuring susceptibility-χ and R2*, sensitive to iron) and a magnetization transfer-weighted sequence (MT-w, sensitive to neuromelanin). From an independent control group (N=29), we created study-specific regions-of-interest for five neuromelanin- and/or iron-rich subregions within the substantia nigra. Mean R2*, susceptibility-χ and their ratio, as well as the MT-w contrast-to-noise ratio (MT-CNR) were extracted from these regions and compared between groups. We then tested the relationships between these imaging metrics and clinical severity. Results: People with Parkinson's disease showed a significant ~50% reduction in MT-CNR compared to healthy controls. They also showed a 1.2-fold increase in ferric iron loading (elevation of the ΔR2*/Δχ ratio from 0.19±0.058ms/ppm to 0.22±0.059ms/ppm) in an area of the substantia nigra identified as having both high neuromelanin and susceptibility MRI signal in healthy controls. In this region, the ferric-to-ferrous iron loading was associated with disease duration (β=0.0072, pFDR=0.048) and cognitive impairment (β=-0.0115, pFDR=0.048). Conclusions: T2*-weighted and MT-weighted high-resolution 7T imaging markers identified neurochemical consequences of Parkinson's disease, in overlapping but not-identical regions. These changes correlated with non-motor symptoms.

Ultrasound imaging of vagus nerves in patients with Parkinson's disease

2021 ◽  
Author(s):  
AO Chechetkin ◽  
AN Moskalenko ◽  
EYu Fedotova ◽  
SN Illarioshkin
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Resolution ◽  
Substantia Nigra ◽  
Ultrasound Imaging ◽  
Inverse Correlation ◽  
Control Group ◽  
Vagus Nerves ◽  
Multisystem Disorder ◽  
Cross Sectional

Parkinson’s disease (PD) is a neurodegenerative multisystem disorder characterized by pathologic α-synuclein aggregation affecting, among other things, vagal fibers. The aim of this study was to investigate the cross-sectional area (CSA) of the vagus nerve (VN) in patients with PD using ultrasound imaging. The study was conducted in 32 patients with PD (15 men and 17 women; mean age 58 ± 10 years) and 32 healthy controls comparable with the main group in terms of sex and age. All study participants underwent ultrasound examination of the VN using a high-resolution transducer. Left VN CSA was significantly smaller in patients with PD than in the control group (1.78 ± 0.52 mm2 vs 2.11 ± 0.41 mm2; р = 0.007). A similar result was obtained for right VN CSA at the trend level. ROC analysis demonstrated that the threshold CSA value of < 2.10 mm2 for the left VN has low diagnostic sensivity (59%) for VN atrophy in patients with PD. Right VN CSA was significantly larger than left VN CSA in both groups (p < 0.001). The analysis of the PD group did not reveal any associations between VN CSA and age, duration and stage of the disease, motor (UPDRS III) and non-motor (NMSQ) scores. Patients with akinetic-rigid form of PD had smaller left VN CSA than patients with the mixed form of the disease (р < 0.05). A moderate inverse correlation was established between left VN CSA and the area of substantia nigra hyperechogenicity on both sides (р < 0.04); for the right VN a similar correlation was established at the trend level. High-resolution ultrasound of patients with PD demonstrated atrophy of the VN and the association of VN CSA with the clinical form of the disease and the ultrasound features of the substantia nigra.

scholarly journals Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Bin Xiao ◽  
Naying He ◽  
Qian Wang ◽  
Feng Shi ◽  
Zenghui Cheng ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Logistic Regression ◽  
Image Segmentation ◽  
Substantia Nigra ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Negative Impact ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

Purpose: Parkinson’s disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling. However, the difference between SN labels from different experts (or segmentation algorithms) will have a negative impact on downstream diagnostic tasks, such as the decrease of the accuracy of the algorithm or different diagnostic results for the same sample. In this article, we quantify the accuracy of the algorithm on different label sets and then improve the convolutional neural network (CNN) model to obtain a high-precision and highly robust diagnosis algorithm.Methods: The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling. Then, based on the CNN model with better performance, we further proposed a novel “gated pooling” operation and integrated it with deep learning to attain a joint framework for image segmentation and classification.Results: The experimental results show that, with different sets of SN labeling that mimic different experts, the CNN model can maintain a stable classification accuracy at around 86.4%, while the conventional logistic regression model yields a large fluctuation ranging from 78.9 to 67.9%. Furthermore, the “gated pooling” operation, after being integrated for joint image segmentation and classification, can improve the diagnosis accuracy to 86.9% consistently, which is statistically better than the baseline.Conclusion: The CNN model, compared with the conventional logistic regression model using radiomics features, has better stability in PD diagnosis. Furthermore, the joint end-to-end CNN model is shown to be suitable for PD diagnosis from the perspectives of accuracy, stability, and convenience in actual use.

Quantitative Susceptibility Mapping of the Substantia Nigra in Parkinson’s Disease

2017 ◽  
Vol 48 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Xinxin Zhao ◽  
Hedi An ◽  
Tian Liu ◽  
Nan Shen ◽  
Binshi Bo ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

PATH53 Magnetic susceptibility of substantia nigra in Parkinson's disease: a 7-T in vivo MRI study

2010 ◽  
Vol 81 (11) ◽  
pp. e22-e22 ◽  
Author(s):  
N. Bajaj ◽  
A. Schafer ◽  
S. Wharton ◽  
V. Gontu ◽  
R. Bowtell ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Magnetic Susceptibility ◽  
Substantia Nigra ◽  
Mri Study
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