Brain Responses to High-Calorie Visual Food Cues in Individuals with Normal-Weight or Obesity: An Activation Likelihood Estimation Meta-Analysis
Bibliographic record
Abstract
Overconsumption of high-calorie or unhealthy foods commonly leads to weight gain. Understanding people's neural responses to high-calorie food cues might help to develop better interventions for preventing or reducing overeating and weight gain. In this review, we conducted a coordinate-based meta-analysis of functional magnetic resonance imaging studies of viewing high-calorie food cues in both normal-weight people and people with obesity. Electronic databases were searched for relevant articles, retrieving 59 eligible studies containing 2410 unique participants. The results of an activation likelihood estimation indicate large clusters in a range of structures, including the orbitofrontal cortex (OFC), amygdala, insula/frontal operculum, culmen, as well as the middle occipital gyrus, lingual gyrus, and fusiform gyrus. Conjunction analysis suggested that both normal-weight people and people with obesity activated OFC, supporting that the two groups share common neural substrates of reward processing when viewing high-calorie food cues. The contrast analyses did not show significant activations when comparing obesity with normal-weight. Together, these results provide new important evidence for the neural mechanism underlying high-calorie food cues processing, and new insights into common and distinct brain activations of viewing high-calorie food cues between people with obesity and normal-weight people.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".