Extreme signal amplitude events in neuromagnetic oscillations reveal brain aging processing across adulthood
Bibliographic record
Abstract
Introduction: Neurophysiological activity, as noninvasively captured by electro- and magnetoencephalography (EEG and MEG), demonstrates complex temporal fluctuations approximated by typical variations around the mean values and rare events with large amplitude. The statistical properties of these extreme and rare events in neurodynamics may reflect the limits or capacity of the brain as a complex system in information processing. However, the exact role of these extreme neurodynamic events in ageing, and their spectral and spatial patterns remain elusive. Our study hypothesized that ageing would be associated with frequency specific alterations in the brain's tendency to synchronize large ensembles of neurons and to produce extreme events. Methods: = 645), aged 18 to 89. We characterized extreme neurodynamics by computing sample skewness and kurtosis, and used Partial Least Squares to test for differences across age groups. Results: Our findings revealed that each canonical frequency, from theta to lower gamma, displayed unique spatial patterns of either age-related increases, decreases, or both in the brain's tendency to produce extreme neuromagnetic events. Discussion: Our study introduces a novel neuroimaging framework for understanding ageing through the extreme and rare events of the neurophysiological activity, offering more sensitivity than typical comparative approaches.
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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".