Typical dipole locations can be estimated using averaged somatosensory-evoked potentials and a standard brain model
Bibliographic record
Abstract
It is reasonable to hypothesize that dipoles estimated from grand averaged event-related potentials based on summed-up data obtained from multiple subjects and standard head models could correspond to typical brain regions associated to a particular event. Six healthy subjects were enrolled in a study to test this hypothesis. We estimated dipoles from somatosensory-evoked potentials (SEP) elicited by electrical stimulation to the left median nerve. We also created individual three-layered (scalp, skull, and brain) head models from each subject's magnetic resonance imaging scan, and dipoles were estimated from the individual averaged SEP with each individual head model. We then estimated dipoles using grand averaged SEP across all subjects on the standard head model created from the Montreal Neurological Institute (MNI) standard coordinate system brain template to compare the estimated dipoles located on our own head model and those on the MNI. The dipoles in the post-central gyrus were estimated from negative potentials at 20 ms from the grand averaged data incorporated with the MNI head model, corresponding to a typical location related to SEP stimulation. The results suggest the validity of estimating the dipole location from the grand averaged potential of all subjects with the MNI model if we focus on typical regions related to the task.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".