Age-related trends in neuromagnetic transient beta burst characteristics during a sensorimotor task and rest in the Cam-CAN open-access dataset
Bibliographic record
Abstract
Non-invasive neurophysiological recordings, such as those measured by magnetoencelography (MEG), provide insight into the behaviour of neural networks and how these networks change with factors such as task performance, disease state, and age. Recently, there has been a trend in describing neurophysiological recordings as a series of transient bursts of neural activity rather than averaged sustained oscillations as burst characteristics may be more directly correlated with the neurological generators of brain activity. In this work, we investigate how beta burst characteristics change with age in a large open access dataset. The objectives are (1) to detect and characterize transient beta bursts over the ipsilateral and contralateral primary sensorimotor cortices during a unilateral motor task performance and during wakeful resting, and (2) to identify age-related changes in beta burst characteristics, in the context of earlier reports of age-related changes in beta suppression and the post-movement beta rebound. MEG data, acquired at the Cambridge Centre for Ageing and Neuroscience, of roughly 600 participants with a nearly uniform distribution of ages between 18 and 88 years old was used for analysis. We found that burst rate is the predominant factor related to age-related changes in the amplitude of the induced beta rhythm responses associated with a button press task. Furthermore, we present a cross-validation of burst parameters detected at the sensor- (peak sensor and sensor ROI) and source-level (beamformer spatial filter). This work is as an important step in characterizing transient bursts in neuromagnetic signals in the temporal domain, towards a better understanding of the healthy aging human brain.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".