Assessing the stability of evoked, induced, and passive MEG responses for repeat testing with optically pumped magnetometers
Bibliographic record
Abstract
Passive and task-based MEG responses have been extensively studied in clinical populations to identify signatures that could serve as markers for clinical diagnosis or to monitor progression of treatment. Establishing the reliability of these responses across repeat testing as a benchmark is therefore essential. Emerging MEG technology using optically pumped magnetometers (OPMs) promises a new era for MEG, enhancing both research capabilities and clinical applications. However, the test-retest reliability of various MEG responses measured by these new systems has not yet been characterised. In this study, we measured a range of neural responses to task and rest using a whole-head OPM-MEG system. We assessed the stability of these responses over time in five adult participants, each tested across five different days. Our findings indicated that the well-established face-sensitive M170 response shows reliable group amplitude and latency over time, with a standard deviation of only 1 ms in latency. We showed that induced responses were more variable than evoked. Passive MEG power (via movie-watching as a pseudo-resting state metric) particularly in the alpha band, demonstrated high consistency across sessions, aligning with conventional MEG literature. Our results demonstrate reliability of a range of MEG metrics and provide a benchmark for evaluating changes over successive recordings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".