Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization

TY - JOUR

T1 - Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization

AU - Lorenz, Romy

AU - Simmons, Laura E.

AU - Monti, Ricardo P.

AU - Arthur, J. L.

AU - Limal, Severin

AU - Laakso, I.

AU - Leech, Robert

AU - Violante, Ines R.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - 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.

AB - 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.

KW - Bayesian optimization

KW - Experimental design

KW - Machine-learning

KW - Phosphenes

KW - Real-time

KW - Transcranial alternating current stimulation

UR - http://www.scopus.com/inward/record.url?scp=85068478278&partnerID=8YFLogxK

U2 - 10.1016/j.brs.2019.07.003

DO - 10.1016/j.brs.2019.07.003

M3 - Article

AN - SCOPUS:85068478278

SN - 1935-861X

VL - 12

SP - 1484

EP - 1489

JO - Brain Stimulation

JF - Brain Stimulation

IS - 6

ER -