Enhanced Excitability of Thalamic Sensory Neurons and Slow-Wave EEG Pattern after Stimuli That Induce Spinal Long-Term Potentiation
Bibliographic record
Abstract
Spinal nociceptive neurons are well known to undergo a process of long-term potentiation (LTP) following conditioning by high-frequency sciatic nerve stimulation (HFS) at intensities recruiting C-fibers. However, little if any information exists as to whether such HFS conditioning that produces spinal LTP affects sensory transmission at supraspinal levels. The present study explored this possibility. Conventional extracellular recording methods were used to examine the consequences of HFS versus sham HFS conditioning on individual wide-dynamic range thalamic neurons located in the ventro-postero-lateral (VPL) nucleus in isoflurane-anesthetized rats. Following HFS, the ongoing firing rate and stimulus-evoked (brush, pinch, sciatic nerve) responses were markedly enhanced as were responses to juxtacellular, microiontophoretic applications of glutamate. These HFS-induced enhancements lasted throughout the recording period. Sham stimuli had no effect on VPL neuron excitability. Cortical electroencephalographic (EEG) wave activities were also measured around HFS in conjunction with VPL neuron recordings. The cortical EEG pattern under baseline conditions consisted of recurring short duration bursts of high-amplitude slow waves followed by longer periods of flat EEG. Following HFS, the EEG shifted to a continuous large-amplitude, slow-wave pattern within the 0.5-8.0 Hz bandwidth lasting throughout the recording period. Sham HFS did not alter EEG activity. Sciatic nerve conditioning at A-δ fiber strength, known to reverse spinal LTP, did not alter enhanced neuronal excitability or the EEG slow-wave pattern induced by HFS. These data support the concept that HFS conditioning of the sciatic nerve, which leads to spinal LTP, is associated with distinct, long-lasting changes in the excitability of neurons comprising thalamocortical networks.
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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) | 0.001 | 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".