Tactile Sensory and Pain Networks in the Human Spinal Cord and Brain Stem Mapped by Means of Functional MR Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Touch and brush sensory stimuli elicit activity in discriminative touch pathways involving specific regions in the spinal cord and brain stem. However, no study has mapped normal sensory activity noninvasively in healthy humans. The purpose of this study is to map the neuronal activity of sensory input to understand abnormal sensory transmission. MATERIALS AND METHODS: In the present study, spinal fMRI (by using SEEP) was used to map the activity involved with light touch (2 g and 15 g von Frey filaments) and brush stimuli in the brain stem and spinal cords of 8 healthy volunteers. The results were spatially normalized and analyzed with custom-made software. Areas of SEEP activity were identified by using general linear model analysis. RESULTS: The 2 g von Frey filament showed predominant activity in the medulla around the ipsilateral dorsal gracile and cuneate nuclei. The 15 g filament elicited significant activity in the ipsilateral dorsal and contralateral ventral gray matter areas of the spinal cord, areas around the olivary nuclei, pontine reticular formation, periaqueductal gray, and raphe nuclei in the rostral pons and midbrain. The brush stimuli elicited more activity in the medulla around the ipsilateral cuneate and gracile nuclei. CONCLUSIONS: The 2 g filament and brush stimuli activated areas associated with a touch response. The 15 g filament activated areas associated with a pain response. The results from this study identify specific neuronal regions in the brain stem and spinal cord involved in sensory transmission and help understand altered sensory and pain states.
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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 | 0.000 |
| 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) | 0.002 | 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".