NIH-funded Project on Pediatric Epilepsy Surgery

Chronic seizures are often associated with impaired language functions in children, but these issues may improve shortly after successful epilepsy surgery. This study aims to develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC). To achieve this, we employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of true-positive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN. The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5% of F-statistics across different LMNs. The prediction accuracy increased by up to 40% across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 94%/94%/96% to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort. These findings hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery.