Neuroanatomical correlates of food addiction symptoms and body mass index in the general population
Bibliographic record
Abstract
The food addiction model suggests neurobiological similarities between substance-related and addictive disorders and obesity. While structural brain differences have been consistently reported in these conditions, little is known about the neuroanatomical correlates of food addiction. We therefore aimed to determine whether symptoms of food addiction related to body mass index (BMI), personality, and brain structure in a large population-based sample. Participants of the LIFE-Adult study (n = 625; 20-59 years old, 45% women) answered the Yale Food Addiction Scale (YFAS) and further personality measures, underwent anthropometric assessments and high-resolution 3T-neuroimaging. A higher YFAS symptom score correlated with higher BMI, eating behavior traits, neuroticism, and stress. Higher BMI predicted significantly lower thickness of (pre)frontal, temporal and occipital cortex and increased volume of left nucleus accumbens. In a whole-brain analysis, YFAS symptom score was not associated with significant differences in cortical thickness or subcortical gray matter volumes. A hypothesis-driven Bayes factor analysis suggested a small, additional contribution of YFAS symptom score to lower right lateral orbitofrontal cortex thickness over the effect of BMI. Our study indicates that symptoms of food addiction do not account for the major part of the structural brain differences associated with BMI in the general population. Yet, symptoms of food addiction might explain additional variance in orbitofrontal cortex, a hub area of the reward network. Longitudinal studies implementing both anatomical and functional MRI could further disentangle the neural mechanisms of addictive eating behaviors.
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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 | 0.001 | 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) | 0.001 | 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".