Neuromagnetic Somatosensory Responses to Natural Moving Tactile Stimulation
Bibliographic record
Abstract
OBJECTIVE: To explore the somatosensory cortical responses to natural moving tactile stimulation in adult subjects using magnetoencephalography. METHODS: We measured cortical somatosensory magnetic evoked fields (SEFs) to moving tactile stimuli by a brush over the right thumb once every 1.5 s in seven subjects. Electric SEFs with various intensity or simulated jitter were used for comparison. RESULTS: Tactile SEFs in primary somatosensory cortex (SI) consisted of two deflections: N24mT and P55mT. Electric SEFs consisted of N24mE, P30mE, P40mE, and P55mE. The amplitude of N24mT was only 34% +/- 12% of N24mE, whereas P55mT and P55mE were of about the same size. With increased jitter or decreased intensity, attenuation of electric SEFs was more clearly found in early deflection than late deflection. CONCLUSIONS: Natural moving tactile stimulation produced simpler cortical somatosensory waveforms in comparison with electric SEFs, partly related to less sharp intensity and stimulation jitter with moving tactile stimulation. We propose that of all the afferent fibers conveying the early deflection, the low threshold components participate the generation of the late deflection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.003 | 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 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".