Dataset Information

FMRI reactivity to high-calorie food pictures predicts short- and long-term outcome in a weight-loss program.

ABSTRACT: Behavioral studies have suggested that food cues have stronger motivating effects in obese than in normal-weight individuals, which may be a risk factor underlying obesity. Previous cross-sectional neuroimaging studies have suggested that this difference is mediated by increased reactivity to food cues in parts of the reward system in obese individuals. To date, however, only a few prospective neuroimaging studies have been conducted to examine whether individual differences in brain activation elicited by food cues can predict differences in weight change. We used functional magnetic resonance imaging (fMRI) to investigate activation in reward-system as well as other brain regions in response to viewing high-calorie food vs. control pictures in 25 obese individuals before and after a 12-week psychosocial weight-loss treatment and at 9-mo follow-up. In those obese individuals who were least successful in losing weight during the treatment, we found greater pre-treatment activation to high-calorie food vs. control pictures in brain regions implicated in reward-system processes, such as the nucleus accumbens, anterior cingulate, and insula. We found similar correlations with weight loss in brain regions implicated by other studies in vision and attention, such as superior occipital cortex, inferior and superior parietal lobule, and prefrontal cortex. Furthermore, less successful weight maintenance at 9-mo follow-up was predicted by greater post-treatment activation in such brain regions as insula, ventral tegmental area, putamen, and fusiform gyrus. In summary, we found that greater activation in brain regions mediating motivational and attentional salience of food cues in obese individuals at the start of a weight-loss program was predictive of less success in the program and that such activation following the program predicted poorer weight control over a 9-mo follow-up period.

SUBMITTER: Murdaugh DL

PROVIDER: S-EPMC3287079 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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fMRI reactivity to high-calorie food pictures predicts short- and long-term outcome in a weight-loss program.

Murdaugh Donna L DL Cox James E JE Cook Edwin W EW Weller Rosalyn E RE

NeuroImage 20120201 3

Behavioral studies have suggested that food cues have stronger motivating effects in obese than in normal-weight individuals, which may be a risk factor underlying obesity. Previous cross-sectional neuroimaging studies have suggested that this difference is mediated by increased reactivity to food cues in parts of the reward system in obese individuals. To date, however, only a few prospective neuroimaging studies have been conducted to examine whether individual differences in brain activation ...[more]

PMID: 22332246

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Project description:BACKGROUND:Software designed to accurately estimate food calories from still images could help users and health professionals identify dietary patterns and food choices associated with health and health risks more effectively. However, calorie estimation from images is difficult, and no publicly available software can do so accurately while minimizing the burden associated with data collection and analysis. OBJECTIVE:The aim of this study was to determine the accuracy of crowdsourced annotations of calorie content in food images and to identify and quantify sources of bias and noise as a function of respondent characteristics and food qualities (eg, energy density). METHODS:We invited adult social media users to provide calorie estimates for 20 food images (for which ground truth calorie data were known) using a custom-built webpage that administers an online quiz. The images were selected to provide a range of food types and energy density. Participants optionally provided age range, gender, and their height and weight. In addition, 5 nutrition experts provided annotations for the same data to form a basis of comparison. We examined estimated accuracy on the basis of expertise, demographic data, and food qualities using linear mixed-effects models with participant and image index as random variables. We also analyzed the advantage of aggregating nonexpert estimates. RESULTS:A total of 2028 respondents agreed to participate in the study (males: 770/2028, 37.97%, mean body mass index: 27.5 kg/m2). Average accuracy was 5 out of 20 correct guesses, where "correct" was defined as a number within 20% of the ground truth. Even a small crowd of 10 individuals achieved an accuracy of 7, exceeding the average individual and expert annotator's accuracy of 5. Women were more accurate than men (P<.001), and younger people were more accurate than older people (P<.001). The calorie content of energy-dense foods was overestimated (P=.02). Participants performed worse when images contained reference objects, such as credit cards, for scale (P=.01). CONCLUSIONS:Our findings provide new information about how calories are estimated from food images, which can inform the design of related software and analyses.

Dataset Information

FMRI reactivity to high-calorie food pictures predicts short- and long-term outcome in a weight-loss program.

Publications

fMRI reactivity to high-calorie food pictures predicts short- and long-term outcome in a weight-loss program.

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