
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.09.02.610880
preprint
1
Article
Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization
Sendi Mohammad S. E.
Cole Eric R.
Piallat Brigitte
Ellis Charles A. http://orcid.org/0000-0002-1547-9996

Eggers Thomas E.
Laxpati Nealen G.
Mahmoudi Babak
Gutekunst Claire-Anne
Devergnas Annaelle
Mayberg Helen
Gross Robert E.
Calhoun Vince D. http://orcid.org/0000-0001-9058-0747

03 9 2024
2024.09.02.610880https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.09.02.610880
nihpp-2024.09.02.610880.pdf
Abstract

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments. We validated this framework through three experiments: (1) in silico modeling with synthetic data from a Parkinson’s disease model, (2) in silico modeling with real data from a non-human primate, and (3) in vivo modeling with a real-time rat optogenetic stimulation experiment. In each experiment, we compared AL models to RS models, using various query strategies and stimulation parameters (amplitude, frequency, pulse width). AL models consistently outperformed RS models, achieving lower error on unseen test data in silico ( p <0.0056, N =1,000) and in vivo ( p =0.0036, N =20). This approach represents a significant advancement in brain stimulation, potentially improving both research and clinical applications by making them more efficient and effective. Our findings suggest that AL can substantially reduce the cost and time required for developing personalized brain stimulation therapies, paving the way for more effective and accessible treatments for brain disorders.
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