Investigations of Factors Associated With Treatment Response and Prognosis in Anxiety Disorders

The purpose of this study is to build a comprehensive brain and behavioral dataset on anxiety disorders and identify clinical factors related to treatment response and prognosis in patients with anxiety disorders. By conducting an integrated analysis of clinical features (phenotypes), comorbid conditions, virtual reality (VR)-based behavioral data, electroencephalogram (EEG) data, and MRI-based data in patients undergoing treatment for anxiety disorders and healthy controls, this study aims to enhance the understanding of the pathophysiology of anxiety disorders and establish a foundation for precision medicine. Anxiety disorders significantly impair quality of life by inducing irrational fear and apprehension even in everyday situations. Over the past few years, the prevalence of anxiety disorders and associated medical expenses have surged, escalating the societal and economic burden. However, the current diagnostic system, DSM-5, relies on symptom-based approaches, which often lead to heterogeneity within the same diagnostic category. This limitation hinders the accurate prediction of treatment response and the design of optimal therapeutic strategies. To address these issues, the U.S. National Institute of Mental Health (NIMH) proposed the Research Domain Criteria (RDoC). RDoC is a multidimensional framework that integrates behavioral, psychological, and neurological data to facilitate the development of treatment strategies rooted in pathophysiology. Nonetheless, the acquisition of objective and reliable assessment data remains a challenge due to the inherent characteristics of mental disorders. Virtual reality (VR) technology has emerged as a promising solution to overcome these limitations. VR enables the creation of immersive 3D environments that can elicit anxiety-related behaviors and precisely evaluate them in controlled settings. It also allows data collection in scenarios that closely resemble real-life situations while offering advantages such as repeatability and ease of use. Combining behavioral data collected in VR environments with EEG data can provide a more detailed understanding of the neurophysiological representations underlying anxiety behaviors. EEG, as a biomarker reflecting neural activity in real time, facilitates intuitive and effective analyses. Moreover, MRI-based brain imaging plays a critical role in identifying structural and functional connectivity differences between anxiety disorder patients and healthy controls. It offers crucial insights into the biomarkers that predict treatment outcomes and prognosis. By integrating these data, this study seeks to uncover the key factors influencing treatment response and prognosis in patients with anxiety disorders.

NCT Number	NCT06773585
Lead Sponsor	Kangbuk Samsung Hospital
Conditions	Panic Disorder
Enrollment	185 participants
Start Date	2025-05-15
Primary Completion	2029-05-15 (estimated)
Study Completion	2029-12-15 (estimated)
Updated on ClinicalTrials.gov	2025-04-30