Abstract
Temporal lobe epilepsy (TLE) is a common neurological disease characterized by recurrent focal seizures. These seizures often originate from the mesial temporal limbic networks and the parahippocampal neocortex. People with TLE frequently experience comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy for reducing TLE seizures, but the optimal stimulation parameters for improving seizures and MMS comorbidities remains unclear. We developed a neurotechnology platform for tracking seizures and MMS during ANT-DBS to address this clinical gap. The platform enables bidirectional data streaming between a brain sensing-stimulation implant, compact mobile devices, and cloud-based data storage, viewing, and computing environment. Machine learning algorithms provided accurate, unbiased catalogs of seizures, interictal epileptiform spikes (IES), and wake-sleep brain states to inform ANT-DBS. Remotely administered memory and mood assessments were used to objectively and densely sample cognitive and behavioral response to ANT-DBS. In participants with mesial TLE, we evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. Low-frequency and high-frequency ANT-DBS both reduced reported seizures. But continuous low-frequency ANT-DBS showed greater reductions in electrographic seizures and IES, as well as better sleep and verbal memory compared to high-frequency ANT-DBS. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapies.
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