Active clinical trials for "Body Dysmorphic Disorders"

Previous research shows that individuals with Body Dysmorphic Disorder (BDD) misinterpret ambiguous social information in a negative and threatening manner. These erroneous threat appraisals are thought to maintain disorder symptomatology and psychosocial impairment by reinforcing individuals' distorted self-image and ideas of social undesirability. Thus, maladaptive interpretation biases represent an important treatment target for this population; however, existing bias assessments and modification protocols are limited by the hypothetical and distal nature of scenarios and do not capture momentary experiential threat processes. The proposed study seeks to test virtual reality (VR) technology as a novel, in vivo means of eliciting, identifying, and measuring threat interpretation biases in a clinical sample to better understand the fear/threat structure activated during social interactions in BDD. Findings have the potential to enhance our understanding of disorder maintenance and identify more nuanced treatment targets. This study represents a critical first step in the long-term goal of harnessing VR gaming technology to supercharge existing treatment approaches for this debilitating illness.

The purpose of this study is to find out more about the brain's response to selective serotonin reuptake inhibitor (SSRIs) treatment in people with body dysmorphic disorder (BDD). The investigators will use positron emission tomography (PET) and Magnetic Resonance Imaging (MRI) scans to look at brain activity in people with BDD before and after treatment with an SSRI. The investigators hope that what is learned will allow prediction of whether someone will improve with SSRI treatment.

Body Dysmorphic Disorder (BDD) participants will demonstrate greater attentional biases as compared to healthy control (HC) participants. Greater attention bias will be associated with greater distress. Greater attention bias will be associated with greater symptom severity.

Body dysmorphic disorder (BDD) is associated with high risk for suicide attempts (22-28%) and substance use disorders (49%), underscoring the importance of accurate, real-time risk detection in BDD. This study aims to use smartphone-based digital phenotyping to develop and validate unobtrusive, time-sensitive, and ecologically valid measures of key risk factors for suicide and substance misuse in BDD: negative affect states. As next steps, this research can be extended to detect risk transdiagnostically, with the goal of enabling just-in-time interventions to target suicide and substance misuse across psychiatric illnesses.

A white light scanner can more accurately measure body contouring subjects than standard anthropomorphic methods.