Open AccessPersonalized brain stimulation for effective neurointervention across participants
Author(s) -
Nienke E. R. van Bueren,
Thomas Reed,
Vu Nguyen,
James G. Sheffield,
Sanne H. G. van der Ven,
Michael A. Osborne,
Evelyn H. Kroesbergen,
Roi Cohen Kadosh
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1008886
Subject(s) - psychological intervention , computer science , transcranial direct current stimulation , brain stimulation , artificial intelligence , machine learning , baseline (sea) , sample size determination , intervention (counseling) , bayesian probability , medicine , psychology , neuroscience , stimulation , mathematics , statistics , biology , psychiatry , fishery
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.