4.6 Article

From BDNF to reading: Neural activation and phonological processing as multiple mediators

Journal

BEHAVIOURAL BRAIN RESEARCH
Volume 396, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.bbr.2020.112859

Keywords

BDNF-Val(66)Met polymorphism; Reading; Intermediate phenotypes; Serial multiple mediation model; Compensatory mechanisms

Funding

  1. Italian Ministry of Health Grant RC2019
  2. Italian Ministry of Health Grant RC2020
  3. National Science Foundation NSF) IGERT grant [DGE-1144399]
  4. NSF [GRFPDGE-1747453]
  5. National Institutes of Health (NIH) [R01 HD48830, R03 HD053409]
  6. NIH [P50 HD052120]

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The BDNF gene Val(66)Met polymorphism affects brain activation in reading-related regions, which in turn influences phonological processing and reading ability. This suggests that brain imaging data and neurocognitive traits serve as viable intermediate phenotypes for complex behaviors.
The BDNF gene is a prominent promoter of neuronal development, maturation and plasticity. Its Val(66)Met polymorphism affects brain morphology and function within several areas and is associated with several cognitive functions and neurodevelopmental disorder susceptibility. Recently, it has been associated with reading, reading-related traits and altered neural activation in reading-related brain regions. However, it remains unknown if the intermediate phenotypes (IPs, such as brain activation and phonological skills) mediate the pathway from gene to reading or reading disability. By conducting a serial multiple mediation model in a sample of 94 children (age 5-13), our findings revealed no direct effects of genotype on reading. Instead, we found that genotype is associated with brain activation in reading-related and more domain general regions which in turn is associated with phonological processing which is associated with reading. These findings suggest that the BDNF-Val(66)Met polymorphism is related to reading via phonological processing and functional activation. These results support brain imaging data and neurocognitive traits as viable IPs for complex behaviors.

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