Organization of the Thermal Grill Illusion by Spinal Segments
Bibliographic record
Abstract
OBJECTIVE: A common symptom of neuropathy is the misperception of heat and pain from cold stimuli. Similar cold allodynic sensations can be experimentally induced using the thermal grill illusion (TGI) in humans. It is currently unclear whether this interaction between thermosensory and nociceptive signals depends on spinal or supraspinal integration mechanisms. To address this issue, we developed a noninvasive protocol to assess thermosensory integration across spinal segments. METHODS: We leveraged anatomical knowledge regarding dermatomes and their spinal projections to investigate potential contributions of spinal integration to the TGI. We simultaneously stimulated a pair of skin locations on the arm or lower back using 1 cold (∼20°C) and 1 warm thermode (∼40°C). The 2 thermodes were always separated by a fixed physical distance on the skin, but elicited neural activity across a varying number of spinal segments, depending on which dermatomal boundaries the 2 stimuli spanned. RESULTS: Participants consistently overestimated the actual cold temperature on the skin during combined cold and warm stimulation, confirming the TGI effect. The TGI was present when cold and warm stimuli were delivered within the same dermatome, or across dermatomes corresponding to adjacent spinal segments. In striking contrast, no TGI effect was found when cold and warm stimuli projected to nonadjacent spinal segments. INTERPRETATION: These results demonstrate that the strength of the illusion is modulated by the segmental distance between cold and warm afferents. This suggests that both temperature perception and thermal-nociceptive interactions depend upon low-level convergence mechanisms operating within a single spinal segment and its immediate neighbors. Ann Neurol 2018;84:463-472.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".