Brain-Derived Neurotrophic Factor ( <i>BDNF</i> ) Epigenomic Modifications and Brain-Related Phenotypes in Humans: A Systematic Review
Bibliographic record
Abstract
Abstract Epigenomic modifications of the brain-derived neurotrophic factor ( BDNF ) gene have been postulated to underlie the pathogenesis of neurodevelopmental, psychiatric, and neurological conditions. This systematic review summarizes current evidence investigating the association of BDNF epigenomic modifications (DNA methylation, non-coding RNA, histone modifications) with brain-related phenotypes in humans. A novel contribution is our creation of an open access web-based application, the BDNF DNA Methylation Map, to interactively visualize specific positions of CpG sites investigated across all studies for which relevant data were available. Our literature search of four databases through September 27, 2021 returned 1,701 articles, of which 153 met inclusion criteria. Our review revealed exceptional heterogeneity in methodological approaches, hindering the identification of clear patterns of robust and/or replicated results. We summarize key findings and provide recommendations for future epigenomic research. The existing literature appears to remain in its infancy and requires additional rigorous research to fulfill its potential to explain BDNF -linked risk for brain-related conditions and improve our understanding of the molecular mechanisms underlying their pathogenesis. Highlights 153 articles examined BDNF epigenomic modifications and brain-related phenotypes Novel BDNF DNA Methylation Map allows users to interactively visualize CpGs BDNF epigenomics lack robust/replicated results due to methodological heterogeneity
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".