Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network

Purpose: Focal epilepsy is a risk factor for language impairment in children. We investigated whether the current state-of-the-art deep learning network on diffusion tractography connectome can accurately predict expressive and receptive language scores of children with epilepsy. Methods: We studied 37 children with a diagnosis of drug-resistant focal epilepsy (age: 11.8 +/- 3.1 years) using 3 T MRI and diffusion tractography connectome: G = (S, Omega), where S is an adjacency matrix of edges representing the connectivity strength (number of white-matter tract streamlines) between each pair of brain regions, and Omega reflects a set of brain regions. A convolutional neural network (CNN) was trained to learn the nonlinear relationship between 'S (input)' and 'language score (output)'. Repeated hold-out validation was then employed to measure the Pearson correlation and mean absolute error (MAE) between CNN-predicted and actual language scores. Results: We found that CNN-predicted and actual scores were significantly correlated (i.e., Pearson's R/p-value: 0.82/<0.001 and 0.75/<0.001), yielding MAE: 7.77 and 7.40 for expressive and receptive scores, respectively. Specifically, sparse connectivity not only within the left cortico-cortical network but also involving the right subcortical structures was predictive of language impairment of expressive or receptive domain. Subsequent subgroup analyses inferred that the effectiveness of diffusion tractography-based prediction of language outcome was independent of clinical variables. Intrinsic diffusion tractography connectome properties may be useful for predicting the severity of baseline language dysfunction and possibly provide a better understanding of the biological mechanisms of epilepsy-related language impairment in children. (C) 2021 Elsevier Inc. All rights reserved.