BDNF, relative preference, and reward circuitry responses to emotional communication
Bibliographic record
Abstract
Brain derived neurotrophic factor (BDNF) regulates neural development and synaptic transmission. We have tested the hypothesis that functional variation in the BDNF gene (Val66Met polymorphism, rs6265) affects brain reward circuitry encoding human judgment and decision-making regarding relative preference. We quantified relative preference among faces with emotional expressions (angry, fearful, sad, neutral, and happy) by a keypress procedure performed offline to measure effort traded for viewing time. Keypress-based relative preferences across the ensemble of faces were mirrored significantly by fMRI signal in the orbitofrontal cortex, amygdala, and hippocampus when passively viewing these faces. For these three brain regions, there was also a statistically significant group difference by BDNF genotype in the fMRI responses to the emotional expressions. In comparison with Val/Met heterozygotes, Val/Val individuals preferentially sought exposure to positive emotions (e.g., happy faces) and had stronger regional fMRI activation to aversive stimuli (e.g., angry, fearful, and sad faces). BDNF genotype accounted for approximately 30% of the variance in fMRI signal that mirrors keypress responses to these stimuli. This study demonstrates that functional allelic variation in BDNF modulates human brain circuits processing reward/aversion information and relative preference transactions.
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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 | 0.001 |
| 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".