Brain–gut interactions in the regulation of satiety: new insights from functional brain imaging
Bibliographic record
Abstract
Since the US army surgeon, William Beaumont, first described the effects of emotion on the gastric function of Alexis St Martin, a Canadian voyager who developed a gastrocutaneous fistula after a gunshot wound,1 considerable progress has been made in our understanding of the human brain–gut axis. Animal studies show that this axis, which comprises neural, hormonal and immune pathways, modulates all aspects of gut function and, perhaps most importantly, also modulates feeding behaviour. Our understanding of the human brain–gut axis has been greatly facilitated by functional brain imaging techniques, which have been used extensively to study visceral pain in health and disease. This work has led to important insights into the visceral pain neuromatrix, and its modulation by psychological and pharmacological factors.2 With advanced brain imaging methods, it has now become possible to study brain–gut communication that occurs through gut peptide hormones, affording an opportunity to understand the central mechanisms of satiety in health and conditions such as obesity and anorexia. Eating behaviour is not just dictated by metabolic requirements but also by hedonic, psychological, social and environmental influences. Indeed, extremes of eating behaviours leading to obesity and anorexia in the Western world have occurred in the setting of abundance but also a more demanding and stressful environment. While animal studies have improved our basic understanding of the homoeostatic mechanisms of energy balance, translation to humans is limited by the inability to investigate the influence of hedonic and psychological influences on eating behaviour. This limitation is now being overcome through exciting recent brain imaging studies in humans, which are demonstrating a network of brain areas that are activated in response to nutrient ingestion, food cues and orexigenic and anorexigenic gut peptides. …
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".