Hemispheric Asymmetry in Stress Processing in Rat Prefrontal Cortex and the Role of Mesocortical Dopamine
Bibliographic record
Abstract
The prefrontal cortex (PFC) is known to play an important role not only in the regulation of emotion, but in the integration of affective states with appropriate modulation of autonomic and neuroendocrine stress regulatory systems. The present review highlights findings in the rat which helps to elucidate the complex nature of prefrontal involvement in emotion and stress regulation. The medial PFC is particularly important in this regard and while dorsomedial regions appear to play a suppressive role in such regulation, the ventromedial (particularly infralimbic) region appears to activate behavioral, neuroendocrine and sympathetic autonomic systems in response to stressful situations. This may be especially true of spontaneous stress-related behavior or physiological responses to relatively acute stressors. The role of the medial PFC is somewhat more complex in conditions involving learned adjustments to stressful situations, such as the extinction of conditioned fear responses, but it is clear that the medial PFC is important in incorporating stressful experience for future adaptive behavior. It is also suggested that mesocortical dopamine plays an important adaptive role in this region by preventing excessive behavioral and physiological stress reactivity. The rat brain shows substantial hemispheric specialization in many respects, and while the right PFC is normally dominant in the activation of stress-related systems, the left may play a role in countering this activation through processes of interhemispheric inhibition. This proposed basic template for the lateralization of stress regulatory systems is suggested to be associated with efficient stress and emotional self-regulation, and also to be shaped by both early postnatal experience and gender differences.
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) | 0.001 | 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".