OPM-MEG in multiple sclerosis: Proof of principle, and the effect of naturalistic posture
Bibliographic record
Abstract
• OPM-MEG is newly developed instrumentation to measure human brain electrophysiology. • We used OPM-MEG to detect differences between people with MS (pwMS) and controls. • Measured delayed movement induced beta responses and reduced visual gamma effects. • OPM-MEG allows recording in participants in multiple postures (sitting/standing). • We found reduced beta power and functional connectivity when standing (cf. sitting). Multiple Sclerosis (MS) is a common neurological disorder in which myelin damage affects neuronal signalling. Magnetoencephalography (MEG – the measurement of magnetic fields generated by neuronal currents) offers metrics of brain function that relate directly to electrophysiological signalling, making it a valuable tool for exploring how abnormal function relates to MS symptoms. However, conventional MEG requires participants to be seated or supine with limited head and body motion. This makes it hard to measure brain function whilst simultaneously asking patients to carry out tasks they find challenging – many of which relate to movement. Here, we used a newly developed OPM-MEG system, with a wearable helmet and a lightweight backpack-mounted control unit, to measure MEG signals in people with MS (pwMS), both at rest and during a visuo-motor task. Uniquely, our system enabled data collection in participants who were seated and standing. We found that established markers of MS – including delayed beta-band responses to finger movement and diminished gamma-band responses to visual stimulation – were measurable using OPM-MEG. Further, we showed that standing (compared to sitting) decreased beta-band connectivity (in patients and controls, but the effect was only significant in controls) and decreased oscillatory power (in patients but not controls). In summary, our paper confirms that OPM-MEG is a useful means to investigate MS; it also demonstrates the importance of investigating how changes in posture relate to oscillations and connectivity, and lays the groundwork for broader studies of movement.
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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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".