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Fagerland SM, Berntsen HR, Fredriksen M, Endestad T, Skouras S, Rootwelt-Revheim ME, Undseth RM. Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2024; 15:41-62. [PMID: 38827812 PMCID: PMC11141335 DOI: 10.2478/joeb-2024-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Indexed: 06/05/2024]
Abstract
Objective The goal of this study was to explore the development and implementation of a protocol for real-time fMRI neurofeedback (rtfMRI-nf) and to assess the potential for enhancing the selective brain activation using stimuli from Virtual Reality (VR). In this study we focused on two specific brain regions, supplementary motor area (SMA) and right inferior frontal gyrus (rIFG). Publications by other study groups have suggested impaired function in these specific brain regions in patients with the diagnoses Attention Deficit Hyperactivity Disorder (ADHD) and Tourette's Syndrome (TS). This study explored the development of a protocol to investigate if attention and contextual memory may be used to systematically strengthen the procedure of rtfMRI-nf. Methods We used open-science software and platforms for rtfMRI-nf and for developing a simulated repetition of the rtfMRI-nf brain training in VR. We conducted seven exploratory tests in which we updated the protocol at each step. During rtfMRI-nf, MRI images are analyzed live while a person is undergoing an MRI scan, and the results are simultaneously shown to the person in the MRI-scanner. By focusing the analysis on specific regions of the brain, this procedure can be used to help the person strengthen conscious control of these regions. The VR simulation of the same experience involved a walk through the hospital toward the MRI scanner where the training sessions were conducted, as well as a subsequent simulated repetition of the MRI training. The VR simulation was a 2D projection of the experience.The seven exploratory tests involved 19 volunteers. Through this exploration, methods for aiming within the brain (e.g. masks/algorithms for coordinate-system control) and calculations for the analyses (e.g. calculations based on connectivity versus activity) were updated by the project team throughout the project. The final procedure involved three initial rounds of rtfMRI-nf for learning brain strategies. Then, the volunteers were provided with VR headsets and given instructions for one week of use. Afterward, a new session with three rounds of rtfMRI-nf was conducted. Results Through our exploration of the indirect effect parameters - brain region activity (directed oxygenated blood flow), connectivity (degree of correlated activity in different regions), and neurofeedback score - the volunteers tended to increase activity in the reinforced brain regions through our seven tests. Updates of procedures and analyses were always conducted between pilots, and never within. The VR simulated repetition was tested in pilot 7, but the role of the VR contribution in this setting is unclear due to underpowered testing. Conclusion This proof-of-concept protocol implies how rtfMRI-nf may be used to selectively train two brain regions (SMA and rIFG). The method may likely be adapted to train any given region in the brain, but readers are advised to update and adapt the procedure to experimental needs.
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Affiliation(s)
- Steffen Maude Fagerland
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
| | - Henrik Røsholm Berntsen
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
| | - Mats Fredriksen
- Neuropsychatric Outpatient Clinic, Vestfold Hospital Trust, Tønsberg, Norway
| | - Tor Endestad
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Norway
| | - Stavros Skouras
- Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, Geneva, CH-1202, Switzerland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, NO-5020, Norway
- Department of Neurology, Inselspital University Hospital Bern, Bern, CH-3010, Switzerland
| | - Mona Elisabeth Rootwelt-Revheim
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ragnhild Marie Undseth
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Radiology Research, The Intervention Centre, Oslo University Hospital, Oslo, Norway
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Cheng YC, Huang YC, Huang WL. Can Heart Rate Variability be Viewed as a Biomarker of Problematic Internet Use? A Systematic Review and Meta-Analysis. Appl Psychophysiol Biofeedback 2023; 48:1-10. [PMID: 35980558 DOI: 10.1007/s10484-022-09557-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/07/2022] [Indexed: 11/30/2022]
Abstract
Heart rate variability (HRV) has been used to explore the parasympathetic activity of individuals with problematic Internet use (PIU), but the results are controversial. We conducted a systematic review and meta-analysis of studies comparing HRV in PIU individuals and healthy participants from several databases. HRV was analyzed according to the parasympathetic activity in hierarchical order (primary analysis), and the total variability (secondary analysis). The baseline HRV and HRV reactivity were both considered. Of the 106 studies screened, 12 were included in the quantitative analysis. Significant differences were observed for baseline HRV in PIU individuals compared to the controls. Regarding HRV reactivity, PIU individuals did not have a significantly lower HRV value during pleasant or unpleasant stimuli. In summary, PIU individuals and healthy subjects had significantly different resting state parasympathetic activity. The finding of HRV reactivity in PIU individuals awaits further investigation.
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Affiliation(s)
- Ying-Chih Cheng
- Department of Psychiatry, China Medical University Hsinchu Hospital, China Medical University, Hsinchu, Taiwan.,Department of Public Health, Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chen Huang
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Department of Dermatology, School of Medicine and College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wei-Lieh Huang
- Department of Psychiatry, National Taiwan University Hospital Yunlin Branch, No. 579, Sec. 2, Yunlin Rd, Yunlin County 640, Douliu, Yunlin, Taiwan. .,Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan. .,Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan. .,Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan. .,Cerebellar Research Center, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan.
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Zhou D, Kang Y, Cosme D, Jovanova M, He X, Mahadevan A, Ahn J, Stanoi O, Brynildsen JK, Cooper N, Cornblath EJ, Parkes L, Mucha PJ, Ochsner KN, Lydon-Staley DM, Falk EB, Bassett DS. Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present. Proc Natl Acad Sci U S A 2023; 120:e2201074119. [PMID: 36595675 PMCID: PMC9926276 DOI: 10.1073/pnas.2201074119] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 10/17/2022] [Indexed: 01/05/2023] Open
Abstract
Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.
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Affiliation(s)
- Dale Zhou
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Xiaosong He
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychology, School of Humanities and Social Sciences, University of Science and Technology of China, 230026 Hefei, People’s Republic of China
| | - Arun Mahadevan
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Jeesung Ahn
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
| | - Ovidia Stanoi
- Department of Psychology, Columbia University, New York, NY 19104
| | - Julia K. Brynildsen
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Nicole Cooper
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Eli J. Cornblath
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Linden Parkes
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755
| | - Kevin N. Ochsner
- Department of Psychology, Columbia University, New York, NY 19104
| | - David M. Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA 19104
| | - Emily B. Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
- Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, PA 19104
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Santa Fe Institute, Santa Fe, NM 87501
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Real-time fMRI neurofeedback compared to cognitive behavioral therapy in a pilot study for the treatment of mild and moderate depression. Eur Arch Psychiatry Clin Neurosci 2022:10.1007/s00406-022-01462-0. [PMID: 35908116 PMCID: PMC10359372 DOI: 10.1007/s00406-022-01462-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/01/2022] [Indexed: 11/03/2022]
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback was found to reduce depressive symptoms. However, no direct comparison of drug-free patients with an active psychotherapy control group is available. The present study compared rt-fMRI neurofeedback with cognitive behavioral therapy, as the standard treatment in patients declining anti-depressants. Twenty adult, drug-free patients with mild or moderate depression were non-randomly assigned either to a course of eight half-hour sessions of neurofeedback targeting the left medial prefrontal cortex (N = 12) or to a 16-session course of cognitive behavioral therapy (N = 8). Montgomery-Asberg Depression Rating Scale was introduced at baseline, mid-treatment, and end-treatment points. In each group, 8 patients each remained in the study to a mid-treatment evaluation and 6 patients each to the study end-point. ANOVA revealed a depression reduction with a significant effect of Time (F(3,6) = 19.0, p < 0.001, η2 = 0.76). A trend to greater improvement in the cognitive behavioral therapy group compared to neurofeedback emerged (Group × Time; p = 0.078). Percent signal change in the region of interest between up- and down-regulation conditions was significantly correlated with session number (Pearson's r = 0.85, p < 0.001) indicating a learning effect. As limitations, small sample size could lead to insufficient power and non-random allocation to selection bias. Both neurofeedback and cognitive behavioral therapy improved mild and moderate depression. Neurofeedback was not superior to cognitive behavioral therapy. Noteworthy, the neurofeedback training course was associated with continuous improvement in the self-regulation skill, without plateau. This study delivers data to plan clinical trials comparing neurofeedback with cognitive behavioral interventions.
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Neurofeedback for cognitive enhancement and intervention and brain plasticity. Rev Neurol (Paris) 2021; 177:1133-1144. [PMID: 34674879 DOI: 10.1016/j.neurol.2021.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/27/2021] [Indexed: 12/18/2022]
Abstract
In recent years, neurofeedback has been used as a cognitive training tool to improve brain functions for clinical or recreational purposes. It is based on providing participants with feedback about their brain activity and training them to control it, initiating directional changes. The overarching hypothesis behind this method is that this control results in an enhancement of the cognitive abilities associated with this brain activity, and triggers specific structural and functional changes in the brain, promoted by learning and neuronal plasticity effects. Here, we review the general methodological principles behind neurofeedback and we describe its behavioural benefits in clinical and experimental contexts. We review the non-specific effects of neurofeedback on the reinforcement learning striato-frontal networks as well as the more specific changes in the cortical networks on which the neurofeedback control is exerted. Last, we analyse the current challenges faces by neurofeedback studies, including the quantification of the temporal dynamics of neurofeedback effects, the generalisation of its behavioural outcomes to everyday life situations, the design of appropriate controls to disambiguate placebo from true neurofeedback effects and the development of more advanced cortical signal processing to achieve a finer-grained real-time modelling of cognitive functions.
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Mindfulness in Treatment Approaches for Addiction — Underlying Mechanisms and Future Directions. CURRENT ADDICTION REPORTS 2021. [DOI: 10.1007/s40429-021-00372-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Abstract
Purpose of Review
While the treatment of addictive disorders proves to be challenging, new treatment approaches that evolved around the concepts of mindfulness and acceptance have been utilized and investigated in recent years. Our goal is to summarize the efficacy and possible underlying mechanisms of mindfulness-based interventions (MBI) in addictive disorders.
Recent Findings
Various meta-analyses have suggested that MBIs show clinical efficacy in the treatment of addictive disorders. Considering the factors that impact addictive disorders, MBIs have been indicated to augment responsiveness to natural rewards in contrast to addiction-related cues as well as to increase top-down cognitive control, decrease subjective and physiological stress perception, and enhance positive affect.
Summary
In summary, MBIs hold promise in treating addictive disorders while larger randomized controlled trials with longitudinal study designs are needed to confirm their utility. Newest clinical endeavors strive to enhance the clinical utility of MBIs by augmentation or personalization.
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Zhao Z, Yao S, Zweerings J, Zhou X, Zhou F, Kendrick KM, Chen H, Mathiak K, Becker B. Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions. Hum Brain Mapp 2021; 42:1879-1887. [PMID: 33400306 PMCID: PMC7978128 DOI: 10.1002/hbm.25336] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
Real-time fMRI guided neurofeedback training has gained increasing interest as a noninvasive brain regulation technique with the potential to modulate functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI data sets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three data sets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning this finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.
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Affiliation(s)
- Zhiying Zhao
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Xinqi Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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