Personalized Treatment for PTSD

Project Overview

This research focuses on leveraging trait-like (stable) and state-like (dynamic) biomarkers to predict individual outcomes following Prolonged Exposure (PE) therapy, a common treatment for Post-Traumatic Stress Disorder (PTSD). By understanding how these different types of biomarkers contribute to treatment success, we aim to develop personalized approaches that can optimize PE therapy delivery.

Key Research Questions

• Which trait-like biomarkers (e.g., baseline brain structure, genetics) predict overall response to PE?
• How do state-like biomarkers (e.g., changes in brain activity during therapy, symptom fluctuations) track session-by-session progress and predict final outcome?
• Can combining trait and state biomarkers improve the accuracy of treatment outcome predictions?
• How can these findings be used to tailor PE therapy intensity or duration for individual patients?

Methodology

We analyze longitudinal data from individuals undergoing PE therapy, including repeated neuroimaging scans (fMRI, DTI), ecological momentary assessment (EMA) of symptoms, genetic data, and clinical outcome measures. Advanced statistical modeling and machine learning techniques are used to integrate these diverse data streams and predict treatment trajectories.

Current Status

We are currently analyzing existing datasets and developing novel computational models to capture the interplay between trait and state factors in PE treatment response. The goal is to build predictive tools that can be tested in future clinical trials for personalized PTSD treatment.