Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress

• Published in: Chronic stress (Thousand Oaks, Calif.) Vol. 7; p. 24705470231203655
• Main Authors: Goel, Rahul, Tse, Teresa, Smith, Lia J., Floren, Andrew, Naylor, Bruce, Williams, M. Wright, Salas, Ramiro, Rizzo, Albert S., Ress, David
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2023; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Original; Post traumatic stress disorder; Self report; Veterans; Virtual reality; veterans; behavioral sciences; algorithm design and analysis; psychiatry; posttraumatic stress disorder (PTSD); machine learning algorithms; functional magnetic resonance imaging; mental disorders
• ISSN: 2470-5470; 2470-5470
• DOI: 10.1177/24705470231203655

Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-time fMRI neurofeedback during VR-like exposure therapy for PTSD.