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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-8869727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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