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Journal Article

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

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Haynes,  John-Dylan
Max Planck Fellow Research Group Attention and Awareness, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Bernstein Center for Computational Neuroscience, Berlin, Germany;

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Citation

Weygandt, M., Schaefer, A., Schienle, A., & Haynes, J.-D. (2012). Diagnosing different binge-eating disorders based on reward-related brain activation patterns. Human Brain Mapping, 33(9), 2135-2146. doi:10.1002/hbm.21345.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-B67C-0
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.