From BDNF to reading: Neural activation and phonological processing as multiple mediators
Bibliographic record
Abstract
The BDNF gene is a prominent promoter of neuronal development, maturation and plasticity. Its Val66Met 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- Val66Met 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".