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

ABSTRACT:

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, Ω), 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 Ω 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)'. Re

SUBMITTER: Jeong JW 

PROVIDER: S-EPMC8035310 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature