DeepFocus: A Transnasal Approach for Optimized Deep Brain Stimulation of Reward Circuit Nodes

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Abstract

Objective Transcranial electrical stimulation (TES) is an effective technique to modulate brain activity and treat diseases. However, TES is primarily used to stimulate superficial brain regions and is unable to reach deeper targets. The spread of injected currents in the head is affected by volume conduction and the additional spreading of currents as they move through head layers with different conductivities, as is discussed in [1]. In this paper, we introduce DeepFocus, a technique aimed at stimulating deep brain structures in the brain’s “reward circuit” (e.g. the orbitofrontal cortex, Brodmann area 25, amygdala, etc.). Approach To accomplish this, DeepFocus utilizes transnasal electrode placement (under the cribriform plate and within the sphenoid sinus) in addition to electrodes placed on the scalp, and optimizes current injection patterns across these electrodes. To quantify the benefit of DeepFocus, we develop the DeepROAST simulation and optimization platform. DeepROAST simulates the effect of complex skull-base bones’ geometries on the electric fields generated by DeepFocus configurations using realistic head models. It also uses optimization methods to search for focal and efficient current injection patterns, which we use in our simulation and cadaver studies. Main Results In simulations, optimized DeepFocus patterns created larger and more focal fields in several regions of interest than scalp-only electrodes. In cadaver studies, DeepFocus patterns created large fields at the medial orbitofrontal cortex (OFC) with magnitudes comparable to stimulation studies, and, in conjunction with established cortical stimulation thresholds, suggest that the field intensity is sufficient to create neural response, e.g. at the OFC. Significance This minimally invasive stimulation technique can enable more efficient and less risky targeting of deep brain structures to treat multiple neural conditions.

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europepmc
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