Characterizing Sleep Signatures and its effects on Cognition in New-Onset Temporal Lobe Epilepsy

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Project Summary/Abstract Temporal Lobe Epilepsy (TLE) is characterized by disordered neural network activity and temporal lobe seizures. As many as 3 million individuals with TLE in the United States also experience cognitive and sleep problems, resulting in poor school performance in childhood, with high risk of underemployment in adulthood, and consequent lower socioeconomic status. Individuals with TLE frequently experience sleep fragmentation, which disrupts memory consolidation and sustained attention, both of which are impaired in this disorder. While these comorbidities can be long-term consequences of repeated seizures and medications, it is now known that they also often present prior to the first recognized seizure and worsen over time even with successful seizure treatment. This suggests that an early neural network abnormality may underlie seizure development while simultaneously impairing sleep and cognitive development, even prior to the added effects of disorder chronicity. In spite of this, there has been limited research addressing mechanisms underlying these sleep and cognitive problems in TLE. This represents a critical unmet public health need and both the National Academy of Medicine and NINDS have identified this notable gap as a research priority. I will begin to address this gap with the my K23 proposal by investigating abnormal sleep architecture patterns in TLE that directly contribute to cognitive deficits using both an observational (Aim 1) and a mechanistic interventional (Aim 2) approach. In typical NREM sleep, electroencephalogram (EEG) slow wave oscillations are phase-locked and coupled with sleep spindle oscillations (SW-SSO), which facilitates memory consolidation and potentially improves attention. In TLE, disordered networks that result in interictal epileptic discharges and seizures may also contribute to altered SW-SSO coupling during sleep, resulting in memory and attention deficits. A single night of acoustic stimulation (AS) has been proven effective in enhancing SW-SSO coupling and improving cognitive performance in healthy older adults but has not been studied in TLE. My central hypothesis is that disordered networks in newly diagnosed TLE patients result in altered sleep architecture, which disrupt memory consolidation and attention capability. I will test this hypothesis by: (1) characterizing TLE sleep architecture using computational EEG – sleep spindle density, slow wave power, interictal epileptiform discharges, and SW-SSO coupling (Aim 1a), (2) linking these specific TLE-related sleep architecture patterns to cognitive processing (Aim 1b); (3) determining if AS enhances SW-SSO coupling in young adults with TLE (Aim 2a) and (4) determining if enhanced SW-SSO coupling improves memory and attention in TLE (Aim 2b). This training award will provide me the opportunity to extend my research expertise into computational sleep EEG acquisition and analysis, acoustic stimulation techniques, and clinical trial design. My long-term goal is to leverage connections between sleep, behavior and neural network activity to develop and implement tailored cognitive and sleep interventions for individuals with epilepsy.