Source Reconstruction Without an MRI using Optically Pumped Magnetometer based Magnetoencephalography
Bibliographic record
Abstract
Abstract Source modelling in magnetoencephalography (MEG) maps the spatial origins of electrophysiological signals in the brain. Typically, this requires an anatomical MRI scan of the subject’s head, from which a model of the neuromagnetic field (the forward model) is derived. Wearable MEG – based on optically pumped magnetometers (OPMs) – enables MEG measurement from participants who struggle to cope with conventional scanning environments (e.g. children), enabling study of novel cohorts. However, its value is limited if an MRI scan is still required for source modelling. Here we describe a method of warping template MRIs to 3D structured-light scans of the head, to generate “pseudo-MRIs”. We apply our method using data from 20 participants during a sensory task, measuring induced (beta band) responses and whole-brain functional connectivity. Results show that the group average locations of peak task-induced beta modulation were separated by 2.75 mm, when comparing real- and pseudo-MRI approaches. Group averaged time-frequency spectra were also highly correlated (Pearson correlation 0.99) as were functional connectome matrices (0.87), and global connectivity (0.98). In sum, our results demonstrate that source-localized OPM-MEG data, modelled with and without an individual MRI scan can be similar. This will be useful for future studies where MRI data capture is challenging.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".