Differentiating weight-restored anorexia nervosa and body dysmorphic disorder using neuroimaging and psychometric markers

Don A Vaughn, Wesley T Kerr, Teena D Moody, Gigi K Cheng, Francesca Morfini, Aifeng Zhang, Alex D Leow, Michael A Strober, Mark S Cohen and Jamie D Feusner

PLOS ONE, 2019, vol. 14, issue 5, 1-16

Abstract: Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are potentially life-threatening conditions whose partially overlapping phenomenology—distorted perception of appearance, obsessions/compulsions, and limited insight—can make diagnostic distinction difficult in some cases. Accurate diagnosis is crucial, as the effective treatments for AN and BDD differ. To improve diagnostic accuracy and clarify the contributions of each of the multiple underlying factors, we developed a two-stage machine learning model that uses multimodal, neurobiology-based, and symptom-based quantitative data as features: task-based functional magnetic resonance imaging data using body visual stimuli, graph theory metrics of white matter connectivity from diffusor tensor imaging, and anxiety, depression, and insight psychometric scores. In a sample of unmedicated adults with BDD (n = 29), unmedicated adults with weight-restored AN (n = 24), and healthy controls (n = 31), the resulting model labeled individuals with an accuracy of 76%, significantly better than the chance accuracy of 35% (p^

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0213974

DOI: 10.1371/journal.pone.0213974

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