DNA methylation of the serotonin transporter gene (<i>SLC6A4</i>) is associated with brain function involved in processing emotional stimuli
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to investigate the association of fMRI blood oxygen-level dependent (BOLD) reactivity with the level of epigenetic methylation of SLC6A4 in blood DNA from a sample of healthy participants and patients with major depressive disorder (MDD). METHODS: We investigated patients with MDD and healthy controls using fMRI and an emotional attention-shifting task. We assessed site-specific DNA methylation of a previously characterized SLC6A4 region in peripheral blood DNA using pyrosequencing. RESULTS: Our study involved 25 patients with MDD and 35 healthy controls. Activation in the anterior insula elicited by negative emotional content was significantly positively associated with the degree of SLC6A4 methylation. Significantly negative associations were observed between activation in the posterior insula and the degree of SLC6A4 methylation when judging the geometry of pictures after seeing negative in contrast to positive emotional stimuli. Healthy controls with a high degree of SLC6A4 methylation depicted significantly more activity elicited by positive stimuli in limbic regions and more activity elicited by negative stimuli in limbic as well as cognitive control regions than those with a low degree of SLC6A4 methylation. LIMITATIONS: It is impossible to measure methylation directly in the brain and thus we assessed peripheral methylation of SLC6A4. Since the association was cross-sectional, no conclusion about cause and effect can be drawn. CONCLUSION: Our study provides further support to the hypothesis that particular DNA methylation states that are associated with brain function during emotion processing are detectable in the periphery.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".