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Reggente N. VR for Cognition and Memory. Curr Top Behav Neurosci 2023; 65:189-232. [PMID: 37440126 DOI: 10.1007/7854_2023_425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
This chapter will provide a review of research into human cognition through the lens of VR-based paradigms for studying memory. Emphasis is placed on why VR increases the ecological validity of memory research and the implications of such enhancements.
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Affiliation(s)
- Nicco Reggente
- Institute for Advanced Consciousness Studies, Santa Monica, CA, USA.
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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, CALIF.) 2023; 7:24705470231203655. [PMID: 37780807 PMCID: PMC10540591 DOI: 10.1177/24705470231203655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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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Gong A, Gu F, Nan W, Qu Y, Jiang C, Fu Y. A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience. Front Neurosci 2021; 15:638369. [PMID: 34127921 PMCID: PMC8195869 DOI: 10.3389/fnins.2021.638369] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 04/14/2021] [Indexed: 11/13/2022] Open
Abstract
Neurofeedback training (NFT) is a non-invasive, safe, and effective method of regulating the nerve state of the brain. Presently, NFT is widely used to prevent and rehabilitate brain diseases and improve an individual's external performance. Among the various NFT methods, NFT to improve sport performance (SP-NFT) has become an important research and application focus worldwide. Several studies have shown that the method is effective in improving brain function and motor control performance. However, appropriate reviews and prospective directions for this technology are lacking. This paper proposes an SP-NFT classification method based on user experience, classifies and discusses various SP-NFT research schemes reported in the existing literature, and reviews the technical principles, application scenarios, and usage characteristics of different SP-NFT schemes. Several key issues in SP-NFT development, including the factors involved in neural mechanisms, scheme selection, learning basis, and experimental implementation, are discussed. Finally, directions for the future development of SP-NFT, including SP-NFT based on other electroencephalograph characteristics, SP-NFT integrated with other technologies, and SP-NFT commercialization, are suggested. These discussions are expected to provide some valuable ideas to researchers in related fields.
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Affiliation(s)
- Anmin Gong
- School of Information Engineering, Engineering University of People's Armed Police, Xi'an, China
| | - Feng Gu
- School of Information Engineering, Engineering University of People's Armed Police, Xi'an, China
| | - Wenya Nan
- Department of Psychology, College of Education, Shanghai Normal University, Shanghai, China
| | - Yi Qu
- School of Information Engineering, Engineering University of People's Armed Police, Xi'an, China
| | - Changhao Jiang
- Key Laboratory of Sports Performance Evaluation and Technical Analysis, Capital Institute of Physical Education, Beijing, China
| | - Yunfa Fu
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, China
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Structural Diversity of Co(II) Coordination Polymers: Treatment Activity on Alzheimer’s Disease Via Reducing the Accumulation of Amyloid in Brain. J Inorg Organomet Polym Mater 2020. [DOI: 10.1007/s10904-020-01783-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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