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Personalized brain stimulation for effective neurointervention across participants
Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8454957/ https://www.ncbi.nlm.nih.gov/pubmed/34499639 http://dx.doi.org/10.1371/journal.pcbi.1008886 |
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author | van Bueren, Nienke E. R. Reed, Thomas L. Nguyen, Vu Sheffield, James G. van der Ven, Sanne H. G. Osborne, Michael A. Kroesbergen, Evelyn H. Cohen Kadosh, Roi |
author_facet | van Bueren, Nienke E. R. Reed, Thomas L. Nguyen, Vu Sheffield, James G. van der Ven, Sanne H. G. Osborne, Michael A. Kroesbergen, Evelyn H. Cohen Kadosh, Roi |
author_sort | van Bueren, Nienke E. R. |
collection | PubMed |
description | Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains. |
format | Online Article Text |
id | pubmed-8454957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-84549572021-09-22 Personalized brain stimulation for effective neurointervention across participants van Bueren, Nienke E. R. Reed, Thomas L. Nguyen, Vu Sheffield, James G. van der Ven, Sanne H. G. Osborne, Michael A. Kroesbergen, Evelyn H. Cohen Kadosh, Roi PLoS Comput Biol Research Article Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains. Public Library of Science 2021-09-09 /pmc/articles/PMC8454957/ /pubmed/34499639 http://dx.doi.org/10.1371/journal.pcbi.1008886 Text en © 2021 van Bueren et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article van Bueren, Nienke E. R. Reed, Thomas L. Nguyen, Vu Sheffield, James G. van der Ven, Sanne H. G. Osborne, Michael A. Kroesbergen, Evelyn H. Cohen Kadosh, Roi Personalized brain stimulation for effective neurointervention across participants |
title | Personalized brain stimulation for effective neurointervention across participants |
title_full | Personalized brain stimulation for effective neurointervention across participants |
title_fullStr | Personalized brain stimulation for effective neurointervention across participants |
title_full_unstemmed | Personalized brain stimulation for effective neurointervention across participants |
title_short | Personalized brain stimulation for effective neurointervention across participants |
title_sort | personalized brain stimulation for effective neurointervention across participants |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8454957/ https://www.ncbi.nlm.nih.gov/pubmed/34499639 http://dx.doi.org/10.1371/journal.pcbi.1008886 |
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