Emotional comorbidities in epilepsy result from seizure-induced corticosterone activity
Bibliographic record
Abstract
People with epilepsy often have psychiatric comorbidities that can significantly impair their quality of life. We previously reported that repeated seizure activity persistently alters endocannabinoid (eCB) signaling in the amygdala which accounts for comorbid emotional dysregulation in rats, however, the mechanism by which these alterations in eCB signaling within the epileptic brain occur is unclear. Endocannabinoid signaling is influenced by corticosterone (CORT) to modulate cognitive and emotional processes and a hyperactive hypothalamic-pituitary-adrenal (HPA) axis occurs in both people with epilepsy and nonhuman animal models of epilepsy. We employed selective pharmacological tools and a variety of approaches including whole-cell patch-clamp electrophysiology, behavioural paradigms and biochemical assays in amygdala kindled adult male Long-Evans rats. We aimed to determine whether seizures induce hypersecretion of CORT and the role this plays in eCB system dysregulation, impaired fear memory, and anxiety-like behaviours associated with seizure activity. Plasma CORT levels were significantly and consistently elevated following seizures over the course of kindling. Pre-seizure administration with the CORT synthesis inhibitor metyrapone prevented this seizure-induced CORT increase, prevented amygdala anandamide downregulation, and synaptic alteration induced by seizure activity. Moreover, treatment with metyrapone or combined glucocorticoid receptor (GR)/mineralocorticoid receptor (MR) antagonists prior to each elicited seizure were equally effective in preventing chronically altered anxiety-like behaviour and fear memory responses. Inhibiting seizure-induced corticosterone synthesis, or directly blocking the effects of CORT at GR/MR prevents deleterious changes in emotional processing and could be a treatment option for emotional comorbidities in epilepsy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".