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Related Experiment Video

Updated: Jun 29, 2025

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Bayesian Optimization of Neurostimulation (BOONStim).

Lindsay D Oliver, Jerrold Jeyachandra, Erin W Dickie

    Biorxiv : the Preprint Server for Biology
    |April 1, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Bayesian Optimization of Neuro-Stimulation (BOONStim) offers a user-friendly pipeline for personalized transcranial magnetic stimulation (TMS) targeting. This automated approach efficiently identifies optimal TMS parameters, improving treatment response prediction.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering

    Background:

    • Individual variability in brain structure and function impacts transcranial magnetic stimulation (TMS) treatment response.
    • There is a need for sophisticated, user-friendly methods combining functional magnetic resonance imaging (fMRI) and TMS simulation for precise target identification.

    Approach:

    • Developed and validated the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline.
    • BOONStim automates interoperability between surface-based fMRI analysis and TMS electric field modeling for individualized targeting.
    • Evaluated Bayesian optimization performance against grid optimization using standard and functional connectivity targets in a small dataset (N=10).

    Key Points:

    • Bayesian optimization rapidly converged to optimal TMS targets within 30 iterations.
    • Achieved high accuracy, within 5% error of grid optimization maxima for total electric field stimulation.
    • Demonstrated reduced computation time compared to traditional grid optimization methods.

    Conclusions:

    • BOONStim provides a scalable, configurable, and user-friendly pipeline for personalized TMS targeting.
    • The pipeline offers a quick turnaround for identifying optimal stimulation parameters.
    • BOONStim facilitates more precise and effective TMS treatment planning.