Personalized brain stimulation for effective neurointervention across participants

• Published in: PLoS computational biology Vol. 17; no. 9; p. e1008886
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.09.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Arithmetical ability; Bayes Theorem; Bayesian analysis; Behavior; Bioaccumulation; Biology and Life Sciences; Brain - physiology; Brain research; Brain stimulation; Care and treatment; Cognitive ability; Customization; Electroencephalography - methods; Female; Human performance; Humans; Learning algorithms; Machine learning; Male; Mathematics; Medicine and Health Sciences; Methods; Optimization; Organic mental disorder; Parameters; Physical Sciences; Precision medicine; Research and Analysis Methods; Searching; Sequential analysis; Social Sciences; Stimulation; Transcranial Direct Current Stimulation - methods; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008886

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.