Predicting response to non-invasive brain stimulation in post-stroke upper extremity motor impairment: the importance of neurophysiological and clinical biomarkers.
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| Title: | Predicting response to non-invasive brain stimulation in post-stroke upper extremity motor impairment: the importance of neurophysiological and clinical biomarkers. |
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| Authors: | Barreto, Gabriel (AUTHOR), Fonseca, André (AUTHOR), Albuquerque, Rhayssa (AUTHOR), Santos, Camilla (AUTHOR), Brito, Rodrigo (AUTHOR), Piscitelli, Daniele (AUTHOR), de Araújo, Maria das Graças Rodrigues (AUTHOR), Monte-Silva, Katia (AUTHOR) |
| Source: | Neurological Sciences. Aug2025, Vol. 46 Issue 8, p3747-3755. 9p. |
| Subjects: | Transcranial direct current stimulation, Machine learning, Transcranial magnetic stimulation, Brain stimulation, Medical sciences |
| Abstract: | Background: Non-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. Objectives: This 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. Methods: This 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. Results: Of 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. Conclusions: The 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 186678227 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting response to non-invasive brain stimulation in post-stroke upper extremity motor impairment: the importance of neurophysiological and clinical biomarkers. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Barreto%2C+Gabriel%22">Barreto, Gabriel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fonseca%2C+André%22">Fonseca, André</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Albuquerque%2C+Rhayssa%22">Albuquerque, Rhayssa</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Santos%2C+Camilla%22">Santos, Camilla</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brito%2C+Rodrigo%22">Brito, Rodrigo</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Piscitelli%2C+Daniele%22">Piscitelli, Daniele</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22de+Araújo%2C+Maria+das+Graças+Rodrigues%22">de Araújo, Maria das Graças Rodrigues</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Monte-Silva%2C+Katia%22">Monte-Silva, Katia</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neurological+Sciences%22">Neurological Sciences</searchLink>. Aug2025, Vol. 46 Issue 8, p3747-3755. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Transcranial+direct+current+stimulation%22">Transcranial direct current stimulation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Transcranial+magnetic+stimulation%22">Transcranial magnetic stimulation</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+stimulation%22">Brain stimulation</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+sciences%22">Medical sciences</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Non-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. Objectives: This 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. Methods: This 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. Results: Of 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. Conclusions: The 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10072-025-08156-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 3747 Subjects: – SubjectFull: Transcranial direct current stimulation Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Transcranial magnetic stimulation Type: general – SubjectFull: Brain stimulation Type: general – SubjectFull: Medical sciences Type: general Titles: – TitleFull: Predicting response to non-invasive brain stimulation in post-stroke upper extremity motor impairment: the importance of neurophysiological and clinical biomarkers. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Barreto, Gabriel – PersonEntity: Name: NameFull: Fonseca, André – PersonEntity: Name: NameFull: Albuquerque, Rhayssa – PersonEntity: Name: NameFull: Santos, Camilla – PersonEntity: Name: NameFull: Brito, Rodrigo – PersonEntity: Name: NameFull: Piscitelli, Daniele – PersonEntity: Name: NameFull: de Araújo, Maria das Graças Rodrigues – PersonEntity: Name: NameFull: Monte-Silva, Katia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15901874 Numbering: – Type: volume Value: 46 – Type: issue Value: 8 Titles: – TitleFull: Neurological Sciences Type: main |
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