Electrical Nerve Stimulation Can Be Used as a Tool in fMRI Studies of Pain‐ and Tingling‐Evoked Activations
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESES: To determine whether transcutaneous electrical nerve stimulation (TENS) provides adequate, inexpensive and simple means to image innocuous and pain‐related activations in the thalamus and cortex. SUBJECTS AND METHODS: High resolution functional magnetic resonance imaging (fMRI) was used to obtain functional data sets on a 1.5T General Electric echospeed scanner (General Electric, Milwaukee) from six axial slices during interleaved periods of rest and TENS at either nonpainful tingling or painful intensities. The volume of brain imaged allowed inspection of stimulation‐related activations in the thalamus, insula and second somatosensory cortex (S2). RESULTS: Tingling TENS activations were identified primarily in the contralateral posterolateral thalamus. Painful TENS activations were found in the contralateral posterolateral thalamus, medial and/or anterior thalamus. The insula and S2 were activated in four of the subjects with tingling TENS and in all subjects with painful TENS. Tingling TENS activations were located in the posterior insula, whereas pain‐related activations were located in the anterior insula. Painful TENS activations found in S2 overlapped with tingling TENS activations. CONCLUSIONS: These findings demonstrate that TENS is a simple mode of stimulation that produces fairly consistent cortical activations, especially at painful levels, and thus may be useful in carefully designed and controlled clinical fMRI studies of pain and touch.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".