Realignment of Magnetoencephalographic Data for Group Analysis in the Sensor Domain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Magnetoencephalography (MEG) is a neuroimaging modality with high temporal resolution for studying functional brain processes in relatively small neural assemblies on the time scale of <100 milliseconds and with synchrony and coherence in the recorded signals at high frequencies. Advanced MEG signal analysis gained importance for clinical applications, e.g., as a sensitive classifier for the diagnosis of neuropsychiatric disorders. Despite tremendous improvements in magnetic source imaging, MEG analysis often does not require explicit source estimation and can be performed in the sensor domain. However, group analysis of MEG sensor data is complicated by variable positioning of the sensor array relative to the head and needs realignment of the sensor configuration. Here, the authors provide an algorithm for transforming the magnetic field data as recorded at various sensor positions onto a common sensor array. Based on the measured magnetic field at the original sensor position, they estimate a source distribution and project it onto a virtual sensor array using the leadfield description of the magnetic forward solution. First, they analyzed the variation of sensor positioning in a typical MEG study and reported the impact on the leadfield matrix. Then they evaluated the realignment algorithm and reported its properties. Including efficient regularization to the inverse solution, they demonstrated that the introduced error is in the order of the sensor noise, and smoothing of data is limited to the set of smallest eigenvalues of the data. They demonstrated the performance of the algorithm with dipole source modeling on group averaged MEG data and comparison of grand averaged auditory evoked responses with and without sensor realignment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it