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Neural Engineering

Neuromodulation: Brain & Spinal Cord Stimulation

Efficient optimization of transcranial current stimulation

Friday, October 25, 2024
2:15 PM – 2:30 PM EST
Location: Room 315

Introduction:  Transcranial current stimulation (tCS) can noninvasively induce neuromodulation, resulting in improved brain function in both healthy people and those suffering from neuropathologies. However, electric fields are unfocused, low, and superficial compared to invasive methods. Finite element models and computational optimization aid in improving tCS field distributions. Additionally, a new form of tCS, temporal interference stimulation (tTIS), has the potential to create more focused fields in deeper brain areas. Unfortunately, modeling and optimization methods for tCS are often difficult to use and time-consuming, limiting their use in experiments. Here, we present new efficient methods and compare them to existing ones.

Materials and

Methods:  We developed and implemented methods to simulate tCS fields in humans and animals, and to optimize the injected currents for modulation of a specific brain target. Optimization methods included convex optimization, exhaustive search, non-convex optimization, genetic algorithms and neural networks. All methods were applied to 15 head models with 88 electrodes covering the scalp, neck and face, and 150 target regions in cerebral and cerebellar white matter, and cortical and subcortical gray matter. All methods were evaluated based on effectivity (median field strength in the target region) and focality (volume of the brain receiving fields over 0.25 V/m).

Results, Conclusions, and Discussions: A potentially effective field strength could be reached in most targets with tTIS, and most superficial targets with tCS. With tTIS the amount of unwanted stimulation was less than with conventional tCS, but it is impossible to completely avoid potentially effective fields outside the target. There were large differences between optimization methods in capabilities, effectivity and efficiency. For experiments with individual modeling, we present recommendations on which methods to use depending on the experiment’s needs. For other experiments, we provide group-averaged optimal configurations for all studied targets.

Acknowledgements (Optional):