Hippocampal slow EEG frequencies during NREM sleep are involved in spatial memory consolidation in humans
Bibliographic record
Abstract
The hypothesis that sleep is instrumental in the process of memory consolidation is currently largely accepted. Hippocampal formation is involved in the acquisition of declarative memories and particularly of spatial memories. Nevertheless, although largely investigated in rodents, the relations between spatial memory and hippocampal EEG activity have been scarcely studied in humans. Aimed to evaluate the effects of spatial learning on human hippocampal sleep EEG activity, we recorded hippocampal Stereo-EEG (SEEG) in a group of refractory epilepsy patients undergoing presurgical clinical evaluation, after a training on a spatial navigation task. We observed that hippocampal high-delta (2-4 Hz range) activity increases during the first NREM episode after learning compared to the baseline night. Moreover, the amount of hippocampal NREM high-delta power was correlated with task performance at retest. The effect involved only the hippocampal EEG frequencies inasmuch no differences were observed at the neocortical electrodes and in the traditional polysomnographic measures. The present findings support the crucial role of hippocampal slow EEG frequencies during sleep in the memory consolidation processes. More generally, together with previous results, they suggest that slow frequency rhythms are a fundamental characteristic of human hippocampal EEG during both sleep and wakefulness, and are related to the consolidation of different types of memories.
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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".