Deep reasoning neural network analysis to predict language deficits from psychometry-driven DWI connectome of young children with persistent language concerns

This study investigated whether current state-of-the-art deep reasoning network analysis on psychometry-driven diffusion tractography connectome can accurately predict expressive and receptive language scores in a cohort of young children with persistent language concerns (n = 31, age: 4.25 ± 2.38 y...

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Bibliographic Details
Main Authors:	Banerjee, S. (Author), Behen, M. (Author), Dong, M. (Author), Jeong, J.-W (Author), Juhász, C. (Author), Lee, M.-H (Author), O'Hara, N. (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2021
Subjects:	adolescent Adolescent Article artificial neural network brain cortex Cerebral Cortex child Child Child, Preschool clinical article cohort analysis connectome convolutional neural network cross validation Deep Learning deep neural network deep reasoning network diagnostic imaging diffusion tensor imaging Diffusion Tensor Imaging diffusion weighted imaging diffusion-weighted imaging connectome female Female human Humans infant Infant language deficits language disability Language Disorders language network male Male nerve cell network Nerve Net network analysis nuclear magnetic resonance imaging pathology pathophysiology prediction of language score preschool child primary motor cortex Psychometrics psychometry relational network tractography
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Deep reasoning neural network analysis to predict language deficits from psychometry-driven DWI connectome of young children with persistent language concerns

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