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

ABSTRACT: 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 years). A dilated convolutional neural network combined with a relational network (dilated CNN + RN) was trained to reason the nonlinear relationship between "dilated CNN features of language network" and "clinically acquired language score". Three-fold cross-validation was then used to compare the Pearson correlation and mean absolute error (MAE) between dilated CNN + RN-predicted and actual language scores. The dilated CNN + RN outperformed other methods providing the most significant correlati

SUBMITTER: Jeong JW 

PROVIDER: S-EPMC8193535 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature