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Πέμπτη 26 Νοεμβρίου 2020

A distinct neuromelanin magnetic resonance imaging pattern in parkinsonian multiple system atrophy

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Abstract

Background

Parkinsonian variant of multiple system atrophy is a neurodegenerative disorder frequently misdiagnosed as Parkinson's disease. No early imaging biomarkers currently differentiate these disorders.

Methods

Simple visual imaging analysis of the substantia nigra and locus coeruleus in neuromelanin-sensitive magnetic resonance imaging and nigrosome 1 in susceptibility-weighted sequences was performed in thirty patients with parkinsonian variant of multiple system atrophy fulfilling possible/probable second consensus diagnostic criteria. The neuromelanin visual pattern was compared to patients with Parkinson's disease with the same disease duration (n = 10) and healthy controls (n = 10). Substantia nigra semi-automated neuromelanin area/signal intensity was compared to the visual data.

Results

Groups were similar in age, sex, disease duration, and levodopa equivalent dose. Hoehn & Yahr stage was higher in parkinsonian multiple system atrophy patients, 69% of whom had normal neuromelanin size/signal, significantly different from Parkinson's disease patients, and similar to controls. Nigrosome 1 signal was lost in 74% of parkinsonian multiple system atrophy patients. Semi-automated neuromelanin substantia nigra signal, but not area, measurements were able to differentiate groups.

Conclusions

In patients with parkinsonism, simple visual magnetic resonance imaging analysis showing normal neuromelanin substantia nigra and locus coeruleus, combined with nigrosome 1 loss, allowed the distinction of the parkinsonian variant of multiple system atrophy from Parkinson's disease and healthy controls. This easy and widely available method was superior to semi-automated measurements in identifying specific imaging changes in substantia nigra and locus coeruleus.

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Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182,00306932607174,alsfakia@gmail.com,

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