Multimodal Imaging of Language in the Preoperative Evaluation of Epilepsy

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DESCRIPTION (provided by applicant): The proposed research will investigate the use of non-invasive multimodal imaging to evaluate preoperative language processing and predict postsurgical language outcome in patients with temporal lobe epilepsy (TLE). This research will be accomplished by combining magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) with high-resolution structural MRI. The research goals are to elucidate (1) what brain regions are involved in language processing, and (2) whether or not structural measures (i.e., cortical thickness and white matter tractography) can be combined with MEG and fMRI activation patterns to optimize the prediction of postoperative language outcome in TLE, an issue with important and immediate clinical implications. These goals will be accomplished using the following methods. First, the time course and spatial extent of neural activations will be compared in patients with TLE and healthy controls during tasks of lexical-semantic processing using MEG and fMRI informed by cortical reconstruction from structural MRI. Second, the integrity of key white matter fiber tracts will be quantified using DTI. Third, cortical thickness and hippocampal volumes will be quantified from structural MRI. Fourth, functional (MEG/ fMRI) and structural (MRI/DTI) data will be combined to predict the risk for postoperative language decline in TLE. This study has great clinical utility in that identifying the structural and functional correlates of language processing in TLE can assist with preoperative planning and possibly reduce morbidity associated with removal of eloquent cortex. The goals of this proposal are aligned with the NINDS Benchmarks for Epilepsy Research (Part IIIB, Identify predictors and underlying mechanisms that contribute to co-morbidities), which encourage the combined use of innovative structural and functional neuroimaging methods for identifying the predictors and underlying mechanisms that contribute to cognitive and behavioral co-morbidities in epilepsy. Epilepsy is a common neurological disease that costs the healthcare system approximately $12.5 billion annually and can negatively impact an individual's quality of life, employment, and health status. The current project has strong implications for public health because it strives to improve the presurgical evaluation of patients with epilepsy by using advanced, noninvasive technology to identify regions of brain dysfunction in epilepsy. PUBLIC HEALTH RELEVANCE: This project will investigate how non-invasive neuroimaging can be used to study preoperative language performances in patients with epilepsy, and identify patients at risk for postoperative language decline. This research will be accomplished by combining magnetoencephalography, functional magnetic resonance imaging, and diffusion tensor imaging with high-resolution structural MRI. The information gained from this investigation may facilitate surgical planning and reduce the potential for postoperative language decline in patients with epilepsy.