Deep Relational Reasoning for the Prediction of Language Impairment and Postoperative Seizure Outcome Using Preoperative DWI Connectome Data of Children With Focal Epilepsy

• Published in: IEEE transactions on medical imaging Vol. 40; no. 3; pp. 793 - 804
• Main Authors: Banerjee, Soumyanil, Dong, Ming, Lee, Min-Hee, O'Hara, Nolan, Juhasz, Csaba, Asano, Eishi, Jeong, Jeong-Won
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Brain; Brain mapping; Brain modeling; Cerebellum; Child; Children; Classification; Cognition; Connectome; convolutional neural network; Convulsions & seizures; Cortex (frontal); Diffusion-weighted imaging; Epilepsies, Partial - diagnostic imaging; Epilepsies, Partial - surgery; Epilepsy; Explainable artificial intelligence; functional brain mapping; Hippocampus; Humans; Imaging; Iron; Language; Language Development Disorders; Neural networks; Neuroimaging; pediatric epilepsy surgery; Pediatrics; Reasoning; Regression analysis; Regression models; relational reasoning; Seizures; Seizures - diagnostic imaging; Surgery; Thermal resistance
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2020.3036933

Prolonged seizures in children with focal epilepsy (FE) may impair language functions and often reoccur after surgical intervention. This study is aimed at developing a novel deep relational reasoning network to investigate whether conventional diffusion-weighted imaging connectome analysis can be improved when predicting expressive and receptive scores of preoperative language impairments and classifying postoperative seizure outcomes (seizure freedom or recurrence) in individual FE children. To deeply reason the dependencies of axonal connections that are sparsely distributed in the whole brain, this study proposes the "dilated CNN + RN", a dilated convolutional neural network (CNN) combined with a relation network (RN). The performance of the dilated CNN + RN was evaluated using whole brain connectome data from 51 FE children. It was found that when compared with other state-of-the-art algorithms, the dilated CNN + RN led to an average improvement of 90.2% and 97.3% in predicting expressive and receptive language scores, and 2.2% and 4% improvement in classifying seizure freedom and seizure recurrence, respectively. These improvements were independent of the prefixed connectome densities. Also, the dilated CNN + RN could provide an explainable artificial intelligence (AI) model by computing gradient-based regression/classification activation maps. This mapping analysis revealed left superior-medial frontal cortex, bilateral hippocampi, and cerebellum as crucial hubs, facilitating important connections that were most predictive of language function and seizure refractoriness after surgery.