Epidural optogenetics for controlled analgesia
Bibliographic record
Abstract
BACKGROUND: Optogenetic tools enable cell selective and temporally precise control of neuronal activity; yet, difficulties in delivering sufficient light to the spinal cord of freely behaving animals have hampered the use of spinal optogenetic approaches to produce analgesia. We describe an epidural optic fiber designed for chronic spinal optogenetics that enables the precise delivery of light at multiple wavelengths to the spinal cord dorsal horn and sensory afferents. RESULTS: The epidural delivery of light enabled the optogenetic modulation of nociceptive processes at the spinal level. The acute and repeated activation of channelrhodopsin-2 expressing nociceptive afferents produced robust nocifensive behavior and mechanical sensitization in freely behaving mice, respectively. The optogenetic inhibition of GABAergic interneurons in the spinal cord dorsal horn through the activation of archaerhodopsin also produced a transient, but selective induction of mechanical hypersensitivity. Finally, we demonstrate the capacity of optogenetics to produce analgesia in freely behaving mice through the inhibition of nociceptive afferents via archaerhodopsin. CONCLUSION: Epidural optogenetics provides a robust and powerful solution for activation of both excitatory and inhibitory opsins in sensory processing pathways. Our results demonstrate the potential of spinal optogenetics to modulate sensory behavior and produce analgesia in freely behaving animals.
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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 | 0.001 |
| 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".