Eggers, T. | Search bioRxiv

bioRxiv2026

Kilohertz electrical stimulation drives widespread neuronal activation and temporal interference imposes focal rhythmic modulation

Temporal interference (TI) stimulation has attracted attention as a non-invasive approach that can target deep brain structures through interacting kilohertz-frequency electric fields. Its basic premise is that TI stimulation evokes neuronal responses through low-frequency amplitude modulation (AM), while the underlying kilohertz-frequency stimulation is ineffective to induce activity, the latter of which has not been tested rigorously at the cellular level. Here, we combined two-photon imaging in mouse models with immunohistochemistry and computational modeling to characterize spatiotemporal patterns of neuronal responses during TI stimulation. We found that kilohertz-frequency stimulation was sufficient to evoke activity without AM. In contrast, AM organized neuronal activity into rhythmic responses at AM frequency, with the strongest entrainment near the center of interference. Thus, kilohertz-frequency stimulation can drive widespread neuronal activation, upon which TI imposes focal rhythmic modulation. Our results provide a framework for refining TI stimulation and highlight the potential of kilohertz-frequency stimulation for brain stimulation.

neuroscience↗

bioRxiv2024

SAFE-OPT: A Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. We then deploy both SAFE-OPT and conventional Bayesian optimization in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subjects safety threshold. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of deep brain stimulation.

bioengineering↗