Age influence on repetition suppression revealed in the hippocampus with MEG
Bibliographic record
Abstract
BACKGROUND: One candidate biomarker for Alzheimer's Disease (AD) is the hyperexcitability of the hippocampus in presymptomatic stages or Mild Cognitive Impairment that transforms into hypoactivation with disease progression. A potential tool to measure this neural shift is repetition suppression, defined as the neural tuning over repeated information during memory formation. Yet, to establish such a neural biomarker, it is important to characterize the aging effects on repetition suppression in the hippocampus. By leveraging high temporal and spatial properties of magnetoencephalography (MEG), we examined distinct neural activation profiles in the hippocampus associated with repetition suppression in healthy young versus older adults, aiming to identify a promising avenue for a neural biomarker for AD. METHODS: We collected MEG recordings from 53 participants, 27 healthy young (F=20, Age=18-34) and 26 older adults (F=17, Age=60+), while they viewed series of scenes, where some images repeated after variable (2 to 8 item) lags. We aligned participant-specific MEG data with their structural images and localized hippocampal signals through beamformer analyses. Repetition suppression was computed as the oscillatory difference between the novel and the repeated presentation of the images. In each group, we characterized the clusters of neural synchronies with time-frequency analyses. We compared the probability of significant synchrony differences between the two groups against a null distribution in a permutation test. RESULTS: Healthy older adults exhibited significant clusters of hippocampal alpha (8-12Hz) and beta (13-30Hz) desynchronizations for the repeated images in comparison to the novel (p <0.001), indicating more neural activity. The desynchronization of the hippocampus for the repeated images was not as pronounced in healthy young adults. In fact, permutation testing revealed that the extent of beta desynchronization for the repeated presentation was significantly greater in the hippocampus of healthy older adults than young (p <0.05). CONCLUSION: Neural tuning of the hippocampus during memory formation, namely repetition suppression, changes with age; expressed as greater neural activation in healthy older adults. Characterizing the aging effects on repetition suppression may offer a promising avenue for examination of hippocampal hyperexcitability as a potential biomarker for AD.
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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".