Jeong-Won Jeong1, Min-Hee Lee2, Nolan O'Hara3, Csaba Juhász4, Eishi Asano5. 1. Pediatrics Department, Wayne State University School of Medicine, Detroit, MI, USA; Neurology Department, Wayne State University School of Medicine, Detroit, MI, USA; Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, USA; Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA. Electronic address: jjeong@med.wayne.edu. 2. Pediatrics Department, Wayne State University School of Medicine, Detroit, MI, USA; Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA. 3. Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, USA; Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA. 4. Pediatrics Department, Wayne State University School of Medicine, Detroit, MI, USA; Neurology Department, Wayne State University School of Medicine, Detroit, MI, USA; Neurosurgery Department, Wayne State University School of Medicine, Detroit, MI, USA; Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, USA; Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA. 5. Pediatrics Department, Wayne State University School of Medicine, Detroit, MI, USA; Neurology Department, Wayne State University School of Medicine, Detroit, MI, USA; Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, USA.
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.
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.
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