Evidence map›Paper›PMID 40208373›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2025

Predicting response to non-invasive brain stimulation in post-stroke upper extremity motor impairment: the importance of neurophysiological and clinical biomarkers.

Gabriel Barreto, André Fonseca, Rhayssa Albuquerque, Camilla Santos, Rodrigo Brito, Daniele Piscitelli, Maria das Graças Rodrigues de Araújo, Katia Monte-Silva

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Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Gabriel BarretoApplied Neuroscience Laboratory, Universidade Federal de Pernambuco, Recife, Brazil.ORCID http://orcid.org/0000-0001-8781-0644
André FonsecaCenter of Mathematics, Computation and Cognition, Universidade Federal do ABC, São Paulo, Brazil.
Rhayssa AlbuquerqueApplied Neuroscience Laboratory, Universidade Federal de Pernambuco, Recife, Brazil.
Camilla SantosApplied Neuroscience Laboratory, Universidade Federal de Pernambuco, Recife, Brazil.
Rodrigo BritoApplied Neuroscience Laboratory, Universidade Federal de Pernambuco, Recife, Brazil.ORCID https://orcid.org/0000-0003-2511-3143
Daniele PiscitelliDoctor of Physical Therapy Program, Department of Kinesiology, University of Connecticut, 3107 Horsebarn Hill Road, Storrs, CT, U-4137, 06269-1101, USA. daniele.piscitelli@uconn.edu.ORCID http://orcid.org/0000-0002-5240-3798
Maria das Graças Rodrigues de AraújoLaboratory of kinesiotherapy and manual therapeutic resources, Universidade Federal de Pernambuco, Recife, Brazil.
Katia Monte-SilvaApplied Neuroscience Laboratory, Universidade Federal de Pernambuco, Recife, Brazil.ORCID https://orcid.org/0000-0001-7301-2553

Funding

CNPq/Brazil 311224/2019-9Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Finance Code 001
6 · The paper itself

Abstract

backgroundNon-invasive brain stimulation (NIBS) is a promising approach to enhance upper extremity motor impairment (UEMI) recovery in post-stroke individuals. However, variability in treatment response poses a significant challenge. Identifying neurophysiological and clinical biomarkers that predict NIBS response could improve personalization and treatment efficacy.

objectivesThis study aims to determine the predictive relevance of neurophysiological and clinical biomarkers for responses to NIBS in post-stroke UEMI using a machine learning model.

methodsThis secondary analysis involved 63 post-stroke individuals with UEMI (age 56.9 ± 11.1 years). A support vector machine model was used to assess the importance of two neurophysiological biomarkers-brain activity in the lesioned hemisphere quantified using quantitative electroencephalography (power ratio index, PRI) and corticospinal tract (CST) integrity assessed via transcranial magnetic stimulation-and one clinical biomarker-the level of UEMI assessed with Fugl-Meyer upper extremity (FMA-UE)-in predicting responders (ΔFMA-UE ≥ 5 points) and those with excellent response (ΔFMA-UE ≥ 10 points) to NIBS based on the change of FMA-UE before and after treatment.

resultsOf the 63 participants, 42 (65%) were classified as responders, and 14 (22%) demonstrated excellent responses. Predictive importance for responders was 0.78 for PRI-LH, 0.21 for UEMI level, and 0.01 for CST integrity. For predicting excellent responses, PRI-LH had an importance of 0.39, UEMI level 0.37, and CST integrity 0.24.

conclusionsThe study highlights the importance of electrical brain activity in the LH and UEMI level in predicting NIBS responders and excellent responses, with CST integrity being particularly valuable for excellent outcomes.

Indexed as

StrokeStroke RehabilitationTranscranial Magnetic StimulationUpper ExtremityAdultAgedBiomarkersElectroencephalographyFemaleHumansMaleMiddle AgedPyramidal TractsRecovery of FunctionSupport Vector MachineBiomarkersAlgorithmsCerebrovascular accidentFunctional recoveryMotor activityRepetitive transcranial magnetic stimulationTranscranial direct current stimulation

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.