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Bayesian Optimization of Neurostimulation (BOONStim)
Background:
Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed.
Objective:
The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline.
Methods:
BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization.
Results:
Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time.
Conclusions:
BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.
