Bayesian Optimization of Neurostimulation (BOONStim).
| Autor: | Oliver LD, Jeyachandra J, Dickie EW, Hawco C, Mansour S, Hare SM, Buchanan RW, Malhotra AK, Blumberger DM, Deng ZD, Voineskos AN |
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| Jazyk: | angličtina |
| Zdroj: | BioRxiv : the preprint server for biology [bioRxiv] 2024 Mar 28. Date of Electronic Publication: 2024 Mar 28. |
| DOI: | 10.1101/2024.03.08.584169 |
| Abstrakt: | 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. |
| Databáze: | MEDLINE |
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