Age‐related trends in the cortical sources of transient beta bursts during a sensorimotor task and rest
Bibliographic record
Abstract
Interpreting neurophysiology recordings as a series of transient bursts with varying temporal and spectral characteristics provides meaningful insight into mechanisms underlying neural networks. Previous research has revealed age-related changes in the time-frequency dynamics of sensorimotor beta bursts, but to date, there has been little focus on the spatial localization of these beta bursts or how the localization patterns change with normal healthy ageing. The objective of the current study is to implement existing source localization algorithms for use in the detection of the cortical sources of transient beta bursts, and to uncover age-related trends in the resulting source localization patterns. Two well-established source localization algorithms (minimum-norm estimation and beamformer) were applied to localize beta bursts detected over the sensorimotor cortices in a cohort of 561 healthy participants between the ages of 18 and 88 (CamCAN open access dataset). Age-related trends were then investigated by applying regression analysis between participant age and average source power within several cortical regions of interest. This analysis revealed that beta bursts localized primarily to the sensorimotor cortex ipsilateral to the side of the sensor used for their detection. Region of interest analysis revealed that there were age-related changes in the beta burst localization pattern, with most substantial changes evidenced in frontal brain regions. In addition, regression analysis revealed a tendency of age-related trends to peak around 60 years of age suggesting that 60 is a potential critical age in this population. These results show for the first time that source localization techniques can be implemented for the identification of the sources of transient beta bursts. The exploration of these sources provides us with insight into the anatomical generators of transient beta activity and how they change across the lifespan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".