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Abstract #1042

Graph-based global reasoning of DWI tractography connectome allows reproducible prediction of language impairment in pediatric epilepsy

Jeong-Won Jeong1, Soumyanil Banerjee2, Min-Hee Lee1, Nolan O'Hara3, Csaba Juhasz1, Eishi Asano1, and Ming Dong2
1Pediatrics, Wayne State University, Detroit, MI, United States, 2Computer Science, Wayne State University, Detroit, MI, United States, 3Translational Neuroscience Program, Wayne State University, Detroit, MI, United States

We propose a deep learning-based DWI connectome (DWIC) analytic method, characterized by convolutional neural network combined with graph convolutional network. This method trained DWIC features to predict the severity of expressive and receptive language impairment, defined by the clinical evaluation of language fundamentals test. It outperformed other state-of-the-art deep learning approaches in predicting the expressive/receptive language scores in children with focal epilepsy. It also demonstrated the smallest prediction error without a noticeable variation in the random permutation test. Further investigation is warranted to determine the feasibility of a DWIC-based prognostic biomarker of language impairment in clinical practice.

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