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

Lorenz, R., Simmons, L. E., Monti, R. P., Arthur, J. L., Limal, S., Laakso, I., et al. (2019). Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization. Brain Stimulation, 12(6), 1484-1489. doi:10.1016/j.brs.2019.07.003.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-4744-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-46B4-1
Genre: Journal Article

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 Creators:
Lorenz, Romy1, 2, Author              
Simmons, Laura E.3, Author
Monti, Ricardo P.4, Author
Arthur, Joy L.3, Author
Limal, Severin5, Author
Laakso, Ilkka6, Author
Leech, Robert7, Author
Violante, Ines R.8, Author
Affiliations:
1MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom, ou_persistent22              
2Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
3Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College School of Medicine, Imperial College London, United Kingdom, ou_persistent22              
4Gatsby Computational Neuroscience Unit, University College London, United Kingdom, ou_persistent22              
5Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom, ou_persistent22              
6Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland, ou_persistent22              
7Centre for Neuroimaging Science, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom, ou_persistent22              
8School of Psychology, University of Surrey, Guildford, United Kingdom, ou_persistent22              

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Free keywords: Transcranial alternating current stimulation; Experimental design; Machine-learning; Bayesian optimization; Real-time; Phosphenes
 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.

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Language(s): eng - English
 Dates: 2019-06-262019-03-092019-07-012019-07-042019-11
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.brs.2019.07.003
Other: Epub 2019
PMID: 31289013
 Degree: -

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Project name : -
Grant ID : P60478
Funding program : Imperial BRC
Funding organization : National Institute for Health Research (NIHR)
Project name : -
Grant ID : P45930
Funding program : -
Funding organization : Leverhulme Trust
Project name : -
Grant ID : P70597
Funding program : -
Funding organization : Engineering and Physical Sciences Research Council (EPSRC)
Project name : -
Grant ID : BB/S008314/1
Funding program : -
Funding organization : Biotechnology and Biological Sciences Research Council (BBSRC)
Project name : -
Grant ID : 209139/Z/17/Z ; 103045/Z/13/Z
Funding program : -
Funding organization : Wellcome Trust

Source 1

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Title: Brain Stimulation
  Abbreviation : Brain Stimul
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY : Elsevier
Pages: - Volume / Issue: 12 (6) Sequence Number: - Start / End Page: 1484 - 1489 Identifier: ISSN: 1935-861X
CoNE: https://pure.mpg.de/cone/journals/resource/1935-861X