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Goel R, Tse T, Smith LJ, Floren A, Naylor B, Williams MW, Salas R, Rizzo AS, Ress D. Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress. Chronic Stress (Thousand Oaks) 2023; 7:24705470231203655. [PMID: 37780807 PMCID: PMC10540591 DOI: 10.1177/24705470231203655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/11/2023] [Indexed: 10/03/2023]
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.
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Affiliation(s)
- Rahul Goel
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Teresa Tse
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Lia J. Smith
- Department of Psychology, University of Houston, Houston, TX, USA
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Andrew Floren
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA
| | - Bruce Naylor
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA
| | - M. Wright Williams
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Ramiro Salas
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
| | - Albert S. Rizzo
- Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA
| | - David Ress
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
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