Cargando…

Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach

Transcranial direct current stimulation (tDCS) has emerged as an appealing rehabilitative approach to improve brain function, with promising data on gait and balance in people with multiple sclerosis (MS). However, single variable weights have not yet been adequately assessed. Hence, the aim of this...

Descripción completa

Detalles Bibliográficos
Autores principales: Marotta, Nicola, de Sire, Alessandro, Marinaro, Cinzia, Moggio, Lucrezia, Inzitari, Maria Teresa, Russo, Ilaria, Tasselli, Anna, Paolucci, Teresa, Valentino, Paola, Ammendolia, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224780/
https://www.ncbi.nlm.nih.gov/pubmed/35743575
http://dx.doi.org/10.3390/jcm11123505
_version_ 1784733452264800256
author Marotta, Nicola
de Sire, Alessandro
Marinaro, Cinzia
Moggio, Lucrezia
Inzitari, Maria Teresa
Russo, Ilaria
Tasselli, Anna
Paolucci, Teresa
Valentino, Paola
Ammendolia, Antonio
author_facet Marotta, Nicola
de Sire, Alessandro
Marinaro, Cinzia
Moggio, Lucrezia
Inzitari, Maria Teresa
Russo, Ilaria
Tasselli, Anna
Paolucci, Teresa
Valentino, Paola
Ammendolia, Antonio
author_sort Marotta, Nicola
collection PubMed
description Transcranial direct current stimulation (tDCS) has emerged as an appealing rehabilitative approach to improve brain function, with promising data on gait and balance in people with multiple sclerosis (MS). However, single variable weights have not yet been adequately assessed. Hence, the aim of this pilot randomized controlled trial was to evaluate the tDCS effects on balance and gait in patients with MS through a machine learning approach. In this pilot randomized controlled trial (RCT), we included people with relapsing–remitting MS and an Expanded Disability Status Scale >1 and <5 that were randomly allocated to two groups—a study group, undergoing a 10-session anodal motor cortex tDCS, and a control group, undergoing a sham treatment. Both groups underwent a specific balance and gait rehabilitative program. We assessed as outcome measures the Berg Balance Scale (BBS), Fall Risk Index and timed up-and-go and 6-min-walking tests at baseline (T0), the end of intervention (T1) and 4 (T2) and 6 weeks after the intervention (T3) with an inertial motion unit. At each time point, we performed a multiple factor analysis through a machine learning approach to allow the analysis of the influence of the balance and gait variables, grouping the participants based on the results. Seventeen MS patients (aged 40.6 ± 14.4 years), 9 in the study group and 8 in the sham group, were included. We reported a significant repeated measures difference between groups for distances covered (6MWT (meters), p < 0.03). At T1, we showed a significant increase in distance (m) with a mean difference (MD) of 37.0 [−59.0, 17.0] (p = 0.003), and in BBS with a MD of 2.0 [−4.0, 3.0] (p = 0.03). At T2, these improvements did not seem to be significantly maintained; however, considering the machine learning analysis, the Silhouette Index of 0.34, with a low cluster overlap trend, confirmed the possible short-term effects (T2), even at 6 weeks. Therefore, this pilot RCT showed that tDCS may provide non-sustained improvements in gait and balance in MS patients. In this scenario, machine learning could suggest evidence of prolonged beneficial effects.
format Online
Article
Text
id pubmed-9224780
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-92247802022-06-24 Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach Marotta, Nicola de Sire, Alessandro Marinaro, Cinzia Moggio, Lucrezia Inzitari, Maria Teresa Russo, Ilaria Tasselli, Anna Paolucci, Teresa Valentino, Paola Ammendolia, Antonio J Clin Med Article Transcranial direct current stimulation (tDCS) has emerged as an appealing rehabilitative approach to improve brain function, with promising data on gait and balance in people with multiple sclerosis (MS). However, single variable weights have not yet been adequately assessed. Hence, the aim of this pilot randomized controlled trial was to evaluate the tDCS effects on balance and gait in patients with MS through a machine learning approach. In this pilot randomized controlled trial (RCT), we included people with relapsing–remitting MS and an Expanded Disability Status Scale >1 and <5 that were randomly allocated to two groups—a study group, undergoing a 10-session anodal motor cortex tDCS, and a control group, undergoing a sham treatment. Both groups underwent a specific balance and gait rehabilitative program. We assessed as outcome measures the Berg Balance Scale (BBS), Fall Risk Index and timed up-and-go and 6-min-walking tests at baseline (T0), the end of intervention (T1) and 4 (T2) and 6 weeks after the intervention (T3) with an inertial motion unit. At each time point, we performed a multiple factor analysis through a machine learning approach to allow the analysis of the influence of the balance and gait variables, grouping the participants based on the results. Seventeen MS patients (aged 40.6 ± 14.4 years), 9 in the study group and 8 in the sham group, were included. We reported a significant repeated measures difference between groups for distances covered (6MWT (meters), p < 0.03). At T1, we showed a significant increase in distance (m) with a mean difference (MD) of 37.0 [−59.0, 17.0] (p = 0.003), and in BBS with a MD of 2.0 [−4.0, 3.0] (p = 0.03). At T2, these improvements did not seem to be significantly maintained; however, considering the machine learning analysis, the Silhouette Index of 0.34, with a low cluster overlap trend, confirmed the possible short-term effects (T2), even at 6 weeks. Therefore, this pilot RCT showed that tDCS may provide non-sustained improvements in gait and balance in MS patients. In this scenario, machine learning could suggest evidence of prolonged beneficial effects. MDPI 2022-06-17 /pmc/articles/PMC9224780/ /pubmed/35743575 http://dx.doi.org/10.3390/jcm11123505 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
Marotta, Nicola
de Sire, Alessandro
Marinaro, Cinzia
Moggio, Lucrezia
Inzitari, Maria Teresa
Russo, Ilaria
Tasselli, Anna
Paolucci, Teresa
Valentino, Paola
Ammendolia, Antonio
Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach
title Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach
title_full Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach
title_fullStr Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach
title_full_unstemmed Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach
title_short Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach
title_sort efficacy of transcranial direct current stimulation (tdcs) on balance and gait in multiple sclerosis patients: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224780/
https://www.ncbi.nlm.nih.gov/pubmed/35743575
http://dx.doi.org/10.3390/jcm11123505
work_keys_str_mv AT marottanicola efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT desirealessandro efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT marinarocinzia efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT moggiolucrezia efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT inzitarimariateresa efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT russoilaria efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT tassellianna efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT paolucciteresa efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT valentinopaola efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach
AT ammendoliaantonio efficacyoftranscranialdirectcurrentstimulationtdcsonbalanceandgaitinmultiplesclerosispatientsamachinelearningapproach