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

Romy Lorenz*, Laura E. Simmons, Ricardo P. Monti, Joy L. Arthur, Severin Limal, Ilkka Laakso, Robert Leech, Ines R. Violante

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
51 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)1484-1489
Number of pages6
JournalBrain Stimulation
Volume12
Issue number6
DOIs
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian optimization
  • Experimental design
  • Machine-learning
  • Phosphenes
  • Real-time
  • Transcranial alternating current stimulation

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