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Seeking Optimal Montage for Single-Pair Transcranial Direct Current Stimulation Using Bayesian Optimization and

Cheolki Im1, Jongseung Lee2, Donghyeon Kim2

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.

Neuromodulation : Journal of the International Neuromodulation Society
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

Bayesian optimization and hyperband (BOHB) method optimizes transcranial direct current stimulation (tDCS) montages more effectively than random search. This approach enables efficient selection of optimal tDCS parameters for neuromodulation.

Keywords:
Bayesian optimizationhyperbandoptimizationstimulation montagetranscranial direct current stimulation

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Transcranial direct current stimulation (tDCS) is an emerging neuromodulation technique.
  • Optimizing tDCS montages is critical due to the significant impact of electrode position and current intensity on stimulation effects.
  • Conventional optimization methods are limited by high computational load, restricting the search space of electrode positions.

Purpose of the Study:

  • To introduce and evaluate the Bayesian optimization and hyperband (BOHB) method for optimizing tDCS montages across the entire human scalp.
  • To overcome the constraints of traditional optimization approaches that use limited electrode position candidates.
  • To seek optimal tDCS montages without conventional limitations.

Main Methods:

  • The study employed the Bayesian optimization and hyperband (BOHB) method, a probabilistic approach for iterative montage selection.
  • BOHB was compared against random search and greedy search for tDCS simulation suitability.
  • The objective function was defined as maximizing the average electric field norm (|E|) in the motor area (M1) and left dorsal lateral prefrontal cortex.

Main Results:

  • The BOHB method demonstrated superior performance compared to random search in both regions of interest (ROIs) for the same number of iterations.
  • Statistically significant improvements were observed, with smaller iteration indices for BOHB in M1 (p < 0.0001).
  • BOHB yielded a higher normalized electric field (|E|) in the M1 region compared to random search.

Conclusions:

  • The BOHB method is a feasible and effective approach for optimizing transcranial direct current stimulation (tDCS) montages.
  • It outperforms traditional random search methods in finding optimal stimulation parameters.
  • BOHB can serve as an efficient tool for seeking optimal tDCS montages, adjustable via control parameters.