Anterior temporal lobectomy (ATL) is a highly sucessful treatment for eliminating seizures in patients with temporal lobe epilepsy (TLE). However, ATL-induced memory decline is frequent and often severe, having a deleterious impact on quality of life and functional outcomes. Stereotactic laser amygdalohippocampectomy (SLAH) has been introduced as a minimally-invasive alternative that could minimize risk of memory decline. However, it is unclear which patients would benefit the most from SLAH and whether SLAH decreases risk for targeted aspects of episodic memory decline compared to ATL. During the previous grant funding period, we demonstrated the clinical value of combining information from structural (sMRI), diffusion (DTI), and functional (fMRI) imaging to better characterize the neural networks that underlie preoperative language and memory impairment and (re)organization and in TLE. We propose that the same multimodal imaging (MMI) approach can be used to quantify risk for postoperative memory decline. In this competing renewal, we extend our MMI approach, combining sMRI/DTI/fMRI with intracranial recordings (iEEG), enabling us to delve deep into the spatial and temporal dynamics of episodic memory networks in TLE. We employ multimodal associative learning tasks with real-world implications (i.e., pairing a face with a name) that have not before been studied in the surgical context. In addition, we draw from preclinical and computational models of hippocampal functioning that may inform why many patients struggle to make fine-grain distinctions in memory (i.e., impaired pattern separation), even when simple item memory appears intact. We propose that our MMI approach will yield a more complete characterization of episodic memory networks in TLE, reveal patterns of structural and functional reorganization in individual patients, and enable a personalized approach to risk assessment when considering surgical options. Finally, we will track cognitive and imaging changes post- ATL and SLAH and identify patient-specific factors that promote reorganization and improved cognitive outcomes. The goals of this renewal are perfectly aligned with the 2014 NINDS Benchmarks for Epilepsy Research (Part IV, Limit or prevent adverse consequences of seizures and their treatment across the lifespan), which encourage mitigating the effects of surgical interventions on cognitive co-morbidities in epilepsy. Our renewal directly addresses this request, striving to improve surgical decision-making, which will have an immediate and sustained impact on patient care. Epilepsy is a common neurological disease that costs the healthcare system approximately $15.5 billion annually and can negatively impact quality of life, employment, and health status. The current project has strong implications for public health because it strives to improve health outcomes in patients with epilepsy by using advanced, noninvasive technology to identify individual predictors of memory decline that can help to guide surgical decisions and possibly reduce morbidity associated with removal of eloquent brain regions.
This project will investigate how advanced, multimodal neuroimaging can be used to evaluate memory reorganization in patients with epilepsy and to estimate personalized risk for memory decline following different surgical approaches. This research will be accomplished by combining information from functional magnetic resonance imaging, diffusion imaging, structural MRI and task-based intracranial recordings to identify brain networks that contribute to associative and non-associative memory, which will enable us to develop models for predicting memory decline. The information gained from this investigation may facilitate surgical planning and reduce the potential for postoperative cognitive morbidity in patients with epilepsy.
