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

Abstract

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.

Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback

“…Studies have been performed where VR has simulated an MRI experience ( [45]), VR has been used to prepare children for MRI ( [46]), and studies have used VR during hemoencephalographic ( [10]) and MRI sessions ( [47]). Recent studies have explored the use of fMRI VR-like stimuli to prepare PTSD patients for VR training ( [48]), machine learning of biofeedback to optimize VR exposure therapy ( [49]), the combination of biofeedback, VR, and mobile technology to enable home training through sham-feedback ( [50]), and NIRS neurofeedback for at-home use by patients ( [51]). To the best of the authors' knowledge, this is the first protocol aiming to explore how targeted use of contextual memory/VR immersion, and home training with sham-feedback may enhance subsequent real-time fMRI neurofeedback sessions.…”