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Hum Brain Mapp. 2012 Sep;33(9):2135-46. doi: 10.1002/hbm.21345. Epub 2011 Aug 30.

Diagnosing different binge-eating disorders based on reward-related brain activation patterns.

Author information

1
Charité - University Medicine Berlin, Bernstein Center for Computational Neuroscience, Berlin, Germany. martin.weygandt@bccn-berlin.de

Abstract

This study addresses how visual food cues are encoded in reward related brain areas and whether this encoding might provide information to differentiate between patients suffering from eating disorders [binge-eating disorder (BED) and bulimia nervosa (BN)], overweight controls (C-OW), and normal-weight controls (C-NW). Participants passively viewed pictures of food stimuli and neutral stimuli in a cue reactivity design. Two classification analyses were conducted. First, we used multivariate pattern recognition techniques to decode the category of a currently viewed picture from local brain activity patterns. In the second analysis, we applied an ensemble classifier to predict the clinical status of subjects (BED, BN, C-OW, and C-NW) based on food-related brain response patterns. The left insular cortex separated between food and neutral contents in all four groups. Patterns in the right insular cortex provided a maximum diagnostic accuracy for the separation of BED patients and C-NW (86% accuracy, P < 10(-5) , 82% sensitivity, and 90% specificity) as well as BN patients and C-NW (78% accuracy, P = 0.001, 86% sensitivity, and 70% specificity). The right ventral striatum separated maximally between BED patients and C-OW (71% accuracy, P = 0.013, 59% sensitivity, and 82% specificity). The right lateral orbitofrontal cortex separated maximally between BN patients and C-OW (86% accuracy, P < 10(-4) , 79% sensitivity, and 94% specificity). The best differential diagnostic separation between BED and BN patients was obtained in the left ventral striatum (84% accuracy, P < 10(-3) , 82% sensitivity, and 86% specificity). Our results indicate that pattern recognition techniques are able to contribute to a reliable differential diagnosis of BN and BED.

PMID:
22887826
DOI:
10.1002/hbm.21345
[Indexed for MEDLINE]

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