Neural Synchrony in Neuromagnetic Signals and the Role of Alpha Oscillations for Working Memory
Bibliographic record
Abstract
Brain functions for perception, cognition, and action involve coordination between distributed neural networks across extended brain regions. Findings from invasive intra-cranial recordings in animals suggest that brain networks interact through precise timing of neural activity. Such timing mechanism plays an important role for communication between brain areas and can be measured as synchrony in neuroelectric oscillations. Extra-cranial measurement of synchrony can be obtained from magnetoencephalographic (MEG) recordings in humans. However, measurement of synchrony in brain oscillations with MEG is more challenging than using direct electrode recordings because the small MEG signals are embedded in noise, and propagation of the electromagnetic signal across the brain volume may result in false interpretation of synchrony. I combined simulation studies and analysis of MEG data in a working memory (WM) study to improve the methods of detecting synchrony as an indicator of functional connectivity in the MEG. I analyzed the functional roles of alpha frequencies during working memory encoding and maintenance. I provided findings of the functional relevance of alpha oscillations in the memory retrieval process. These results will improve the feasibility of connectivity analysis using MEG in basic research and clinical applications.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".