Neuroinflammation in the prefrontal-amygdala-hippocampus network is associated with maladaptive avoidance behaviour
Bibliographic record
Abstract
Maladaptive avoidance behaviour is often observed in patients suffering from anxiety and trauma- and stressor-related disorders. The prefrontal-amygdala-hippocampus network is implicated in learning and memory consolidation. Neuroinflammation in this circuitry alters network dynamics, resulting in maladaptive avoidance behaviour. The two-way active avoidance test is a well-established translational model for assessing avoidance responses to stressful situations. While some animals learn the task and show adaptive avoidance (AA), others show strong fear responses to the test environment and maladaptive avoidance (MA). Here, we investigated if a distinct neuroinflammation pattern in the prefrontal-amygdala-hippocampus network underlies the behavioural difference observed in these animals. Wistar rats were tested 8 times and categorized as AA or MA based on behaviour. Brain recovery followed for the analysis of neuroinflammatory markers in this network. AA and MA presented distinct patterns of neuroinflammation, with MA showing increased astrocyte, EAAT-2, IL-1β, IL-17 and TNF-ɑ in the amygdala. This neuroinflammatory pattern may underlie these animals' fear response and maladaptive avoidance. Further studies are warranted to determine the specific contributions of each inflammatory factor, as well as the possibility of treating maladaptive avoidance behaviour in patients with psychiatric disorders with anti-inflammatory drugs targeting the amygdala.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".