Dataset Information

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)'. 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.

SUBMITTER: Jeong JW

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

REPOSITORIES: biostudies-literature

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Publications

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

Jeong Jeong-Won JW Lee Min-Hee MH O'Hara Nolan N Juhász Csaba C Asano Eishi E

Epilepsy & behavior : E&B 20210316

<h4>Purpose</h4>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.<h4>Methods</h4>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 e ...[more]

PMID: 33740493

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Dataset Information

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

Purpose

Methods

Results

Publications

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

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