The brain-derived neurotrophic factor Val66Met polymorphism is associated with reduced functional magnetic resonance imaging activity in the hippocampus and increased use of caudate nucleus-dependent strategies in a human virtual navigation task
Bibliographic record
Abstract
Multiple memory systems are involved in parallel processing of spatial information during navigation. A series of studies have distinguished between hippocampus-dependent 'spatial' navigation, which relies on knowledge of the relationship between landmarks in one's environment to build a cognitive map, and habit-based 'response' learning, which requires the memorization of a series of actions and is mediated by the caudate nucleus. Studies have demonstrated that people spontaneously use one of these two alternative navigational strategies with almost equal frequency to solve a given navigation task, and that strategy correlates with functional magnetic resonance imaging (fMRI) activity and grey matter density. Although there is evidence for experience modulating grey matter in the hippocampus, genetic contributions may also play an important role in the hippocampus and caudate nucleus. Recently, the Val66Met polymorphism of the brain-derived neurotrophic factor (BDNF) gene has emerged as a possible inhibitor of hippocampal function. We have investigated the role of the BDNF Val66Met polymorphism on virtual navigation behaviour and brain activation during an fMRI navigation task. Our results demonstrate a genetic contribution to spontaneous strategies, where 'Met' carriers use a response strategy more frequently than individuals homozygous for the 'Val' allele. Additionally, we found increased hippocampal activation in the Val group relative to the Met group during performance of a virtual navigation task. Our results support the idea that the BDNF gene with the Val66Met polymorphism is a novel candidate gene involved in determining spontaneous strategies during navigation behaviour.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".