Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume.

ABSTRACT: Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict

SUBMITTER: Cui Z 

PROVIDER: S-EPMC6669415 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature