The BDNF val66met polymorphism is associated with decreased use of landmarks and decreased fMRI activity in the hippocampus during virtual navigation
Bibliographic record
Abstract
People can navigate in a new environment using multiple strategies dependent on different memory systems. A series of studies have dissociated between hippocampus-dependent 'spatial' navigation and habit-based 'response' learning mediated by the caudate nucleus. The val66met polymorphism of the brain-derived neurotrophic factor (BDNF) gene leads to decreased secretion of BDNF in the brain, including the hippocampus. Here, we aim to investigate the role of the BDNF val66met polymorphism on virtual navigation behaviour and brain activity in healthy older adults. A total of 139 healthy older adult participants (mean age = 65.8 ± 4.4 years) were tested in this study. Blood samples were collected, and BDNF val66met genotyping was performed. Participants were divided into two genotype groups: val homozygotes and met carriers. Participants were tested on virtual dual-solution navigation tasks in which they could use either a hippocampus-dependent spatial strategy or a caudate nucleus-dependent response strategy to solve the task. A subset of the participants (n = 66) were then scanned in a 3T functional magnetic resonance imaging (fMRI) scanner while engaging in another dual-solution navigation task. BDNF val/val individuals and met carriers did not differ in learning performance. However, the two BDNF groups differed in learning strategy. BDNF val/val individuals relied more on landmarks to remember target locations (i.e., increased use of flexible spatial learning), while met carriers relied more on sequences and patterns to remember target locations (i.e., increased use of inflexible response learning). Additionally, BDNF val/val individuals had more fMRI activity in the hippocampus compared with BDNF met carriers during performance on the navigation task. This is the first study to show in older adults that BDNF met carriers use alternate learning strategies from val/val individuals and to identify differential brain activation of this behavioural difference between the two groups.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".