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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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.
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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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