Novel DWI Methods to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery.

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Project Summary/Abstract For the millions of epilepsy patients with drug-resistant seizures, surgical resection of epileptogenic brain tissue is often the only remaining therapeutic option. Especially in young patients who do not obtain seizure control or suffer from unacceptable side effects from medications, there are further concerns about the effect of seizures on development: even brief but repetitive seizures cause cognitive regression and detrimental psychosocial effects. This motivates a particular urgency to investigate a more structured, quantitative, and non-invasive tool, which is capable of informing families and providers to decide timely surgery by accurately providing the probabilities of both favorable and unfavorable postoperative outcomes using data from preoperative imaging analysis at the whole-brain level. The overall goal of this project is to develop a novel tool of benefit-risk analysis for the presurgical evaluation of pediatric drug-resistant focal epilepsy. Toward this goal, we will validate a state- of-the-art deep learning-based diffusion MRI technique to provide the resection margin (i.e., the distance between epileptogenic area and eloquent area) resulting in maximized benefits (i.e., seizure freedom and long- term neurocognitive improvement) and minimized risk (i.e., deficits in eloquent functions including motor/language/hearing/vision). With NIH support, we have established diffusion-weighted imaging maximum a posteriori probability (DWI-MAP) analysis with Kalman filter, which can provide individual patients with the optimal resection margin, yielding successful avoidance of motor/language/visual deficits in 93%/91%/90% of patients with ≥75% of patients benefiting from seizure freedom. Recently, we have also found that deep convolutional neural network (DCNN) can provide an excellent accuracy (94-100%) to classify true positive tracts of eloquent brain areas, suggesting that DCNN-based tract classification may outperform the DWI-MAP in detecting diverse function-specific white matter pathways. Aim 1 of this project will investigate if a combination of DCNN-based tract classification with Kalman filter even better predicts the resection margin, resulting in seizure freedom and avoidance of functional deficits at a large cohort. Aim 2 will investigate if an advanced DWI approach integrating DCNN and DWI connectome helps decide timely surgery by providing 1) preoperative imaging markers underlying high likelihood of postoperative neurocognitive improvements and 2) mechanistic insight in structural brain reorganization associated with postoperative verbal IQ improvement. The results of this project are expected to ultimately improve clinical management of pediatric epilepsy by translating deep learning- based diffusion MRI technique to optimize the surgical margin, predict the postoperative neurocognitive outcome, and determine specific mechanism of postoperative brain reorganization, which will be validated for optimizing clinical benefit-risk analysis before surgical intervention.