Structural gray matter features and behavioral preliterate skills predict future literacy - A machine learning approach.

ABSTRACT: When children learn to read, their neural system undergoes major changes to become responsive to print. There seem to be nuanced interindividual differences in the neurostructural anatomy of regions that later become integral parts of the reading network. These differences might affect literacy acquisition and, in some cases, might result in developmental disorders like dyslexia. Consequently, the main objective of this longitudinal study was to investigate those interindividual differences in gray matter morphology that might facilitate or hamper future reading acquisition. We used a machine learning approach to examine to what extent gray matter macrostructural features and cognitive-linguistic skills measured before formal literacy teaching could predict literacy 2 years later. Forty-tw

SUBMITTER: Beyer M 

PROVIDER: S-EPMC9558903 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature