Glia Imaging Differentiates Multiple System Atrophy from Parkinson's Disease: A Pet Study Using [ 11C ] PBR28 and Machine Learning Enhanced Analysis

2021 ◽  
Author(s):  
Aurelija Jucaite ◽  
Zsolt Cselényi ◽  
William C. Kreisl ◽  
Eugenii A. Rabiner ◽  
Andrea Varrone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Multiple System Atrophy

scholarly journals Differentiation of multiple system atrophy from Parkinson’s disease by structural connectivity derived from probabilistic tractography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexandra Abos ◽  
Hugo C. Baggio ◽  
Barbara Segura ◽  
Anna Campabadal ◽  
Carme Uribe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Multiple System Atrophy ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Discrimination Performance ◽  
Probabilistic Tractography ◽  
Subcortical Structures ◽  
Diffusion Weighted Images

Abstract Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson’s disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

Cutaneous Sympathetic Vasoconstrictor Function in Multiple System Atrophy and Parkinson's Disease

2004 ◽  
Vol 35 (03) ◽  
Author(s):  
G Wasner ◽  
P Remien ◽  
C Guballa ◽  
M Hirschner ◽  
A Binder ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Multiple System Atrophy

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Dermal and cardiac autonomic fiber involvement in Parkinson's disease and multiple system atrophy

2021 ◽  
pp. 105332
Author(s):  
Joachim Brumberg ◽  
Anastasia Kuzkina ◽  
Constantin Lapa ◽  
Sona Mammadova ◽  
Andreas Buck ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Multiple System Atrophy

scholarly journals Phenotypic manifestation of α-synuclein strains derived from Parkinson’s disease and multiple system atrophy in human dopaminergic neurons

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Benedict Tanudjojo ◽  
Samiha S. Shaikh ◽  
Alexis Fenyi ◽  
Luc Bousset ◽  
Devika Agarwal ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Multiple System Atrophy ◽  
Neuronal Death ◽  
Dopaminergic Neurons ◽  
De Novo ◽  
Dependent Manner ◽  
Human Neurons ◽  
Phenotypic Manifestation ◽  
Induced Aggregation

Abstractα-Synuclein is critical in the pathogenesis of Parkinson’s disease and related disorders, yet it remains unclear how its aggregation causes degeneration of human dopaminergic neurons. In this study, we induced α-synuclein aggregation in human iPSC-derived dopaminergic neurons using fibrils generated de novo or amplified in the presence of brain homogenates from Parkinson’s disease or multiple system atrophy. Increased α-synuclein monomer levels promote seeded aggregation in a dose and time-dependent manner, which is associated with a further increase in α-synuclein gene expression. Progressive neuronal death is observed with brain-amplified fibrils and reversed by reduction of intraneuronal α-synuclein abundance. We identified 56 proteins differentially interacting with aggregates triggered by brain-amplified fibrils, including evasion of Parkinson’s disease-associated deglycase DJ-1. Knockout of DJ-1 in iPSC-derived dopaminergic neurons enhance fibril-induced aggregation and neuronal death. Taken together, our results show that the toxicity of α-synuclein strains depends on aggregate burden, which is determined by monomer levels and conformation which dictates differential interactomes. Our study demonstrates how Parkinson’s disease-associated genes influence the phenotypic manifestation of strains in human neurons.

Sign in / Sign up

Export Citation Format

Share Document