Journal
BRAIN STIMULATION
Volume 12, Issue 6, Pages 1484-1489Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.brs.2019.07.003
Keywords
Transcranial alternating current stimulation; Experimental design; Machine-learning; Bayesian optimization; Real-time; Phosphenes
Categories
Funding
- NIHR Imperial BRC [P60478]
- Leverhulme Trust [P45930]
- EPSRC [P70597]
- BBSRC [BB/S008314/1]
- Wellcome Trust [209139/Z/17/Z, 103045/Z/13/Z]
- Wellcome Trust [209139/Z/17/Z, 103045/Z/13/Z] Funding Source: Wellcome Trust
- BBSRC [BB/S008314/1] Funding Source: UKRI
- MRC [MR/R005370/1] Funding Source: UKRI
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Background: Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation. Objective: We demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time. Methods: To validate the method, Bayesian optimization was employed using participants' binary judgements about the intensity of phosphenes elicited through tACS. Results: We demonstrate the efficiency of Bayesian optimization in identifying parameters that maximize phosphene intensity in a short timeframe (5 min for >190 possibilities). Our results replicate frequency-dependent effects across three montages and show phase-dependent effects of phosphene perception. Computational modelling explains that these phase effects result from constructive/destructive interference of the current reaching the retinas. Simulation analyses demonstrate the method's versatility for complex response functions, even when accounting for noisy observations. Conclusion: Alongside subjective ratings, this method can be used to optimize tACS parameters based on behavioral and neural measures and has the potential to be used for tailoring stimulation protocols to individuals. (C) 2019 The Authors. Published by Elsevier Inc.
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