ArticleFrontiers in neuroscience2022
Structural gray matter features and behavioral preliterate skills predict future literacy - A machine learning approach.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Heritable and experience-dependent cortical traits of reading ability.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Glutamatergic signaling underlies brain structural organization for mathematical and reading abilities in children.Nature communications · 2026Article
- Multimodal investigation of the neurocognitive deficits underlying dyslexia in adulthood.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- White Matter Networks of Phonological Awareness in Chinese Readers.Brain and behavior · 2025Article
- From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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-two native German-speaking children underwent T1-weighted magnetic resonance imaging and psychometric testing at the end of kindergarten. They were tested again 2 years later to assess their literacy skills. A leave-one-out cross-validated machine-learning regression approach was applied to identify the best predictors of future literacy based on cognitive-linguistic preliterate behavioral skills and cortical measures in
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Registered trials
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