Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event‐related beamforming approach
Bibliographic record
Abstract
We describe a novel spatial filtering approach to the localization of cortical activity accompanying voluntary movements. The synthetic aperture magnetometry (SAM) minimum-variance beamformer algorithm was used to compute spatial filters three-dimensionally over the entire brain from single trial neuromagnetic recordings of subjects performing self-paced index finger movements. Images of instantaneous source power ("event-related SAM") computed at selected latencies revealed activation of multiple cortical motor areas prior to and following left and right index finger movements in individual subjects, even in the presence of low-frequency noise (e.g., eye movements). A slow premovement motor field (MF) reaching maximal amplitude approximately 50 ms prior to movement onset was localized to the hand area of contralateral precentral gyrus, followed by activity in the contralateral postcentral gyrus at 40 ms, corresponding to the first movement-evoked field (MEFI). A novel finding was a second activation of the precentral gyrus at a latency of approximately 150 ms, corresponding to the second movement-evoked field (MEFII). Group averaging of spatially normalized images indicated additional premovement activity in the ipsilateral precentral gyrus and the left inferior parietal cortex for both left and right finger movements. Weaker activations were also observed in bilateral premotor areas and the supplementary motor area. These results show that event-related beamforming provides a robust method for studying complex patterns of time-locked cortical activity accompanying voluntary movements, and offers a new approach for the localization of multiple cortical sources derived from neuromagnetic recordings in single subject and group data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".