Human Hippocampal and Parahippocampal Theta during Goal-Directed Spatial Navigation Predicts Performance on a Virtual Morris Water Maze
Bibliographic record
Abstract
The hippocampus and parahippocampal cortices exhibit theta oscillations during spatial navigation in animals and humans, and in the former are thought to mediate spatial memory formation. Functional specificity of human hippocampal theta, however, is unclear. Neuromagnetic activity was recorded with a whole-head 275-channel magnetoencephalographic (MEG) system as healthy participants navigated to a hidden platform in a virtual reality Morris water maze. MEG data were analyzed for underlying oscillatory sources in the 4-8 Hz band using a spatial filtering technique (i.e., synthetic aperture magnetometry). Source analyses revealed greater theta activity in the left anterior hippocampus and parahippocampal cortices during goal-directed navigation relative to aimless movements in a sensorimotor control condition. Additional analyses showed that left anterior hippocampal activity was predominantly observed during the first one-half of training, pointing to a role for this region in early learning. Moreover, posterior hippocampal theta was highly correlated with navigation performance, with the former accounting for 76% of the variance of the latter. Our findings suggest human spatial learning is dependent on hippocampal and parahippocampal theta oscillations, extending to humans a significant body of research demonstrating such a pivotal role for hippocampal theta in animal navigation.
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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".