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Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes?

Secondary progressive multiple sclerosis (SPMS) subtype is retrospectively diagnosed, and biomarkers of the SPMS are not available. We aimed to identify possible neurophysiological markers exploring grey matter structures that could be used in clinical practice to better identify SPMS. Fifty-five pe...

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Autores principales: Belvisi, Daniele, Tartaglia, Matteo, Borriello, Giovanna, Baione, Viola, Crisafulli, Sebastiano Giuseppe, Zuccoli, Valeria, Leodori, Giorgio, Ianniello, Antonio, Pasqua, Gabriele, Pantano, Patrizia, Berardelli, Alfredo, Pozzilli, Carlo, Conte, Antonella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869727/
https://www.ncbi.nlm.nih.gov/pubmed/35203440
http://dx.doi.org/10.3390/biomedicines10020231
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author Belvisi, Daniele
Tartaglia, Matteo
Borriello, Giovanna
Baione, Viola
Crisafulli, Sebastiano Giuseppe
Zuccoli, Valeria
Leodori, Giorgio
Ianniello, Antonio
Pasqua, Gabriele
Pantano, Patrizia
Berardelli, Alfredo
Pozzilli, Carlo
Conte, Antonella
author_facet Belvisi, Daniele
Tartaglia, Matteo
Borriello, Giovanna
Baione, Viola
Crisafulli, Sebastiano Giuseppe
Zuccoli, Valeria
Leodori, Giorgio
Ianniello, Antonio
Pasqua, Gabriele
Pantano, Patrizia
Berardelli, Alfredo
Pozzilli, Carlo
Conte, Antonella
author_sort Belvisi, Daniele
collection PubMed
description Secondary progressive multiple sclerosis (SPMS) subtype is retrospectively diagnosed, and biomarkers of the SPMS are not available. We aimed to identify possible neurophysiological markers exploring grey matter structures that could be used in clinical practice to better identify SPMS. Fifty-five people with MS and 31 healthy controls underwent a transcranial magnetic stimulation protocol to test intracortical interneuron excitability in the primary motor cortex and somatosensory temporal discrimination threshold (STDT) to test sensory function encoded in cortical and deep grey matter nuclei. A logistic regression model was used to identify a combined neurophysiological index associated with the SP subtype. We observed that short intracortical inhibition (SICI) and STDT were the only variables that differentiated the RR from the SP subtype. The logistic regression model provided a formula to compute the probability of a subject being assigned to an SP subtype based on age and combined SICI and STDT values. While only STDT correlated with disability level at baseline evaluation, both SICI and STDT were associated with disability at follow-up. SICI and STDT abnormalities reflect age-dependent grey matter neurodegenerative processes that likely play a role in SPMS pathophysiology and may represent easily accessible neurophysiological biomarkers for the SPMS subtype.
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spelling pubmed-88697272022-02-25 Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes? Belvisi, Daniele Tartaglia, Matteo Borriello, Giovanna Baione, Viola Crisafulli, Sebastiano Giuseppe Zuccoli, Valeria Leodori, Giorgio Ianniello, Antonio Pasqua, Gabriele Pantano, Patrizia Berardelli, Alfredo Pozzilli, Carlo Conte, Antonella Biomedicines Article Secondary progressive multiple sclerosis (SPMS) subtype is retrospectively diagnosed, and biomarkers of the SPMS are not available. We aimed to identify possible neurophysiological markers exploring grey matter structures that could be used in clinical practice to better identify SPMS. Fifty-five people with MS and 31 healthy controls underwent a transcranial magnetic stimulation protocol to test intracortical interneuron excitability in the primary motor cortex and somatosensory temporal discrimination threshold (STDT) to test sensory function encoded in cortical and deep grey matter nuclei. A logistic regression model was used to identify a combined neurophysiological index associated with the SP subtype. We observed that short intracortical inhibition (SICI) and STDT were the only variables that differentiated the RR from the SP subtype. The logistic regression model provided a formula to compute the probability of a subject being assigned to an SP subtype based on age and combined SICI and STDT values. While only STDT correlated with disability level at baseline evaluation, both SICI and STDT were associated with disability at follow-up. SICI and STDT abnormalities reflect age-dependent grey matter neurodegenerative processes that likely play a role in SPMS pathophysiology and may represent easily accessible neurophysiological biomarkers for the SPMS subtype. MDPI 2022-01-21 /pmc/articles/PMC8869727/ /pubmed/35203440 http://dx.doi.org/10.3390/biomedicines10020231 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Belvisi, Daniele
Tartaglia, Matteo
Borriello, Giovanna
Baione, Viola
Crisafulli, Sebastiano Giuseppe
Zuccoli, Valeria
Leodori, Giorgio
Ianniello, Antonio
Pasqua, Gabriele
Pantano, Patrizia
Berardelli, Alfredo
Pozzilli, Carlo
Conte, Antonella
Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes?
title Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes?
title_full Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes?
title_fullStr Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes?
title_full_unstemmed Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes?
title_short Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes?
title_sort are neurophysiological biomarkers able to discriminate multiple sclerosis clinical subtypes?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869727/
https://www.ncbi.nlm.nih.gov/pubmed/35203440
http://dx.doi.org/10.3390/biomedicines10020231
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