Understanding the relationship between brain activity and human behavior is not only one of the most important scientific challenges of our generation but also one of the most important challenges in medicine and public health. This project develops new technology that can address the minute size of the neurons, and the vast amount of data generated by neural activity. This project leverages the collaborative environment between Rice and Texas Medical Center to develop novel electrical stimulation approaches to modulate the seizure network, adaptively and selectively. If successful, the end result would be a reparative therapy that leverages inherent brain plasticity mechanisms and may one day be independent of chronically implanted electronics.

This project develops algorithms that capture the dynamic, frequency dependent connectivity of the brain from real-time monitoring of the brain using ECoG (Electrocorticography) and then identifying the "optimal" parameters of the LFS (low-frequency electrical stimulation) to modulate the connectivity of the epilepsy network with temporal and spatial precision. The complexity of modeling such connectivity in real-time is managed by first segmenting neural activity into different epochs and spectral bands and then deriving the sparse connectivity in each of the segments. Effective connectivity in each spectral-temporal segment is estimated using Granger causality. LFS is applied after detecting interictal epileptiform discharges (IEDs) at spatial locations identified from the model. These critical steps lead to the development of a prototype system of real-time stimulation with a natural trade-off of complexity versus accuracy prompting a compromise between battery life and efficacy. The efficacy of spatially-optimized, activity-triggered LFS is evaluated by measuring the irritability of the seizure network and comparing the rate of IEDs detected during pre- and post-treatment periods. These experiments would point the way to treatment of pharmacologically refractory epilepsy without surgical resection of brain tissue and lead to reparative therapies leveraging inherent brain plasticity. The proposed methodology presents the first of its kind reparative, real-time, and selective network modulation to treat a debilitating disease.

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The University of Texas Health Science Center at Houston
United States
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