Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing

Abstract Biomarkers suitable for early diagnosis and monitoring disease progression are the cornerstone of developing disease-modifying treatments for neurodegenerative diseases such as Parkinson’s disease (PD). Besides motor complications, PD is also characterized by deficits in visual processing....

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Autores principales:	Freja Gam Østergaard, Marc M. Himmelberg, Bettina Laursen, Hartwig R. Siebner, Alex R. Wade, Kenneth Vielsted Christensen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0d30f39be362461791363a38bd12b9f6
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spelling	oai:doaj.org-article:0d30f39be362461791363a38bd12b9f62021-12-02T15:32:59ZClassification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing10.1038/s41598-020-68808-32045-2322https://doaj.org/article/0d30f39be362461791363a38bd12b9f62020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-68808-3https://doaj.org/toc/2045-2322Abstract Biomarkers suitable for early diagnosis and monitoring disease progression are the cornerstone of developing disease-modifying treatments for neurodegenerative diseases such as Parkinson’s disease (PD). Besides motor complications, PD is also characterized by deficits in visual processing. Here, we investigate how virally-mediated overexpression of α-synuclein in the substantia nigra pars compacta impacts visual processing in a well-established rodent model of PD. After a unilateral injection of vector, human α-synuclein was detected in the striatum and superior colliculus (SC). In parallel, there was a significant delay in the latency of the transient VEPs from the affected side of the SC in late stages of the disease. Inhibition of leucine-rich repeat kinase using PFE360 failed to rescue the VEP delay and instead increased the latency of the VEP waveform. A support vector machine classifier accurately classified rats according to their `disease state’ using frequency-domain data from steady-state visual evoked potentials (SSVEP). Overall, these findings indicate that the latency of the rodent VEP is sensitive to changes mediated by the increased expression of α-synuclein and especially when full overexpression is obtained, whereas the SSVEP facilitated detection of α-synuclein across reflects all stages of PD model progression.Freja Gam ØstergaardMarc M. HimmelbergBettina LaursenHartwig R. SiebnerAlex R. WadeKenneth Vielsted ChristensenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Freja Gam Østergaard Marc M. Himmelberg Bettina Laursen Hartwig R. Siebner Alex R. Wade Kenneth Vielsted Christensen Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing
description	Abstract Biomarkers suitable for early diagnosis and monitoring disease progression are the cornerstone of developing disease-modifying treatments for neurodegenerative diseases such as Parkinson’s disease (PD). Besides motor complications, PD is also characterized by deficits in visual processing. Here, we investigate how virally-mediated overexpression of α-synuclein in the substantia nigra pars compacta impacts visual processing in a well-established rodent model of PD. After a unilateral injection of vector, human α-synuclein was detected in the striatum and superior colliculus (SC). In parallel, there was a significant delay in the latency of the transient VEPs from the affected side of the SC in late stages of the disease. Inhibition of leucine-rich repeat kinase using PFE360 failed to rescue the VEP delay and instead increased the latency of the VEP waveform. A support vector machine classifier accurately classified rats according to their `disease state’ using frequency-domain data from steady-state visual evoked potentials (SSVEP). Overall, these findings indicate that the latency of the rodent VEP is sensitive to changes mediated by the increased expression of α-synuclein and especially when full overexpression is obtained, whereas the SSVEP facilitated detection of α-synuclein across reflects all stages of PD model progression.
format	article
author	Freja Gam Østergaard Marc M. Himmelberg Bettina Laursen Hartwig R. Siebner Alex R. Wade Kenneth Vielsted Christensen
author_facet	Freja Gam Østergaard Marc M. Himmelberg Bettina Laursen Hartwig R. Siebner Alex R. Wade Kenneth Vielsted Christensen
author_sort	Freja Gam Østergaard
title	Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing
title_short	Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing
title_full	Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing
title_fullStr	Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing
title_full_unstemmed	Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing
title_sort	classification of α-synuclein-induced changes in the aav α-synuclein rat model of parkinson’s disease using electrophysiological measurements of visual processing
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/0d30f39be362461791363a38bd12b9f6
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Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing

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