Cocaine self‐administration is increased after frontal traumatic brain injury and associated with neuroinflammation
Bibliographic record
Abstract
Traumatic brain injury (TBI) has been linked to the development of numerous psychiatric diseases, including substance use disorder. However, it can be difficult to ascertain from clinical data whether the TBI is cause or consequence of increased addiction vulnerability. Surprisingly few studies have taken advantage of animal models to investigate the causal nature of this relationship. In terms of a plausible neurobiological mechanism through which TBI could magnify the risk of substance dependence, numerous studies indicate that TBI can cause widespread disruption to monoaminergic signaling in striatal regions, and also increases neuroinflammation. In the current study, male Long-Evans rats received either a mild or severe TBI centered over the frontal cortex via controlled cortical impact, and were subsequently trained to self-administer cocaine over 10 6-hour sessions. At the end of the study, markers of striatal dopaminergic function, and levels of inflammatory cytokine levels in the frontal lobes, were assessed via western blot and multiplex ELISA, respectively. There was significantly higher cocaine intake in a subset of animals with either mild or severe TBI. However, many animals within both TBI groups failed to acquire self-administration. Principal components analysis suggested that both dopaminergic and neuroinflammatory proteins were associated with overall cocaine intake, yet only an inflammatory component was associated with acquisition of self-administration, suggesting neuroinflammation may make a more substantial contribution to the likelihood of drug-taking. Should neuroinflammation play a causal role in mediating TBI-induced addiction risk, anti-inflammatory therapy may reduce the likelihood of substance abuse in TBI populations.
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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 | 0.001 | 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".