Frontal Traumatic Brain Injury Increases Impulsive Decision Making in Rats: A Potential Role for the Inflammatory Cytokine Interleukin-12
Bibliographic record
Abstract
Traumatic brain injury (TBI) is associated with the development of numerous psychiatric diseases. Of particular concern for TBI patients is the impact of chronic impulsivity on daily functioning. Despite the scope of the human problem, little has been done to address impulsivity in animal models of brain injury. In the current study, we examined the effects of either a severe or a milder bilateral frontal controlled cortical impact injury on impulsivity using the Delay Discounting Task (DDT), in which preference for smaller-sooner over larger-later rewards is indicative of greater impulsive choice. Both milder and severe TBI caused a significant, chronic increase in impulsive decision making. Despite these pronounced changes in performance of the DDT, memory function, as assessed by the Morris Water Maze, was not impaired in more mildly injured rats and only transiently impacted in the severe TBI group. Whereas a significant lesion was only evident in severely injured rats, analysis of cytokine levels within the frontal cortex revealed a selective increase in interleukin (IL)-12 that was associated with the magnitude of the change in impulsive choice caused by both milder and severe TBI. These findings suggest that tissue loss alone cannot explain the increased impulsivity observed, and that inflammatory pathways mediated by IL-12 may be a contributing factor. The findings from this study highlight the sensitivity of sophisticated behavioral measures designed to assess neuropsychiatric dysfunction in the detection of TBI-induced cognitive impairments and their utility in identifying potential mechanistic pathways and therapeutic targets.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".