Neuroinflammatory Remodeling of the Anterior Cingulate Cortex as a Key Driver of Mood Disorders in Gastrointestinal Disease and Disorders
Bibliographic record
Abstract
The brain reciprocally communicates with the rest of the body via neural, endocrine, immune, and other systems. This is crucial for coordinating the complex behavioral and physiological responses needed to cope with the many challenges of life. The Anterior Cingulate Cortex (ACC) is a key brain structure involved in assessing rewards and threats, as well as activating appropriate responses. This is a dynamic process that depends on evolving needs and challenges. Important challenges include illness or injury. These typically involve inflammation and pain, which evoke neuroinflammatory processes in the brain to drive sickness behaviours. In the short term, sickness behaviours are considered adaptive, as they promote convalescence (e.g. low mood; lethargy, fatigue, social withdrawal), and enhanced threat appraisal (e.g. anxiety) to combat increased risk/vulnerability associated with sickness. Chronic inflammation, however, appears to remodel the system to inappropriately activate threat-coping responses, resulting in depressive and/or anxious phenotypes. These mood disorders are particularly pronounced in diseases and disorders associated with gut dysfunction, which feature chronic inflammation and altered ACC function. We propose that chronic inflammation remodels ACC physiology such that it errantly predicts heightened danger based on a mental model (a.k.a ‘schema’) of the world. This evokes chronic activation of threat-coping systems, including endocrine signaling (e.g. adrenaline), and anxiety. Inflammation can be driven by brain systems involving ACC, leading to a feedback-cycle that self-reinforces pathological states. This theory accounts for a wealth of clinical and preclinical data that implicate the ACC in disorders of mood and gastrointestinal function, and reveals a key player in the gut-brain axis that may represent a novel therapeutic target.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".