Predicting Treatment Response to GCMRT in Social Anxiety Disorder

Project Overview

This project focuses on developing predictive models for treatment response to Guided Cognitive-Motivational Response Training (GCMRT) in individuals with Social Anxiety Disorder (SAD). We leverage multimodal data, including neuroimaging and clinical assessments, to identify biomarkers that can predict who will benefit most from this specific therapeutic approach.

Key Research Questions

• Can baseline brain function or structure predict GCMRT treatment outcomes in SAD?
• What specific neural circuits are modulated by GCMRT?
• How do clinical and demographic factors interact with neural markers in predicting response?
• Can we develop personalized prediction models to optimize treatment selection for SAD patients?

Methodology

We utilize data from clinical trials involving GCMRT for SAD, including pre- and post-treatment fMRI scans, DTI, structural MRI, and detailed clinical/behavioral assessments. Machine learning algorithms are employed to build predictive models integrating these diverse data types.

Current Status

We are currently analyzing data and refining our predictive models. Initial results suggest potential neural predictors of GCMRT response, which could pave the way for more personalized treatment approaches in social anxiety.