CRF Mediates Stress-Induced Pathophysiological High-Frequency Oscillations in Traumatic Brain Injury
Bibliographic record
Abstract
Abstract It is not known why there is increased risk to have seizures with increased anxiety and stress after traumatic brain injury (TBI). Stressors cause the release of corticotropin-releasing factor (CRF) both from the hypothalamic pituitary adrenal (HPA) axis and from CNS neurons located in the central amygdala and GABAergic interneurons. We have previously shown that CRF signaling is plastic, becoming excitatory instead of inhibitory after the kindling model of epilepsy. Here, using Sprague Dawley rats we have found that CRF signaling increased excitability after TBI. Following TBI, CRF type 1 receptor (CRFR1)-mediated activity caused abnormally large electrical responses in the amygdala, including fast ripples, which are considered to be epileptogenic. After TBI, we also found the ripple (120–250 Hz) and fast ripple activity (>250 Hz) was cross-frequency coupled with θ (3–8 Hz) oscillations. CRFR1antagonists reduced the incidence of phase coupling between ripples and fast ripples. Our observations indicate that pathophysiological signaling of the CRFR1increases the incidence of epileptiform activity after TBI. The use for CRFR1antagonist may be useful to reduce the severity and frequency of TBI associated epileptic seizures.
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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".