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Lee E, Hong JK, Choi H, Yoon IY. Modest Effects of Neurofeedback-Assisted Meditation Using a Wearable Device on Stress Reduction: A Randomized, Double-Blind, and Controlled Study. J Korean Med Sci 2024; 39:e94. [PMID: 38469966 PMCID: PMC10927393 DOI: 10.3346/jkms.2024.39.e94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/08/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND To evaluate the therapeutic effectiveness and safety of a neurofeedback wearable device for stress reduction. METHODS A randomized, double-blind, controlled study was designed. Participants had psychological stress with depression or sleep disturbances. They practiced either neurofeedback-assisted meditation (n = 20; female, 15 [75.0%]; age, 49.40 ± 11.76 years) or neurofeedback non-assisted meditation (n = 18; female, 11 [61.1%]; age, 48.67 ± 12.90 years) for 12 minutes twice a day for two weeks. Outcome variables were self-reported questionnaires, including the Korean version of the Perceived Stress Scale, Beck Depression Inventory-II, Insomnia Severity Index, Pittsburgh Sleep Quality Index, and State Trait Anxiety Index, quantitative electroencephalography (qEEG), and blood tests. Satisfaction with device use was measured at the final visit. RESULTS The experimental group had a significant change in PSS score after two weeks of intervention compared with the control group (6.45 ± 0.95 vs. 3.00 ± 5.54, P = 0.037). State anxiety tended to have a greater effect in the experimental group than in the control group (P = 0.078). Depressive mood and sleep also improved in each group, with no significant difference between the two groups. There were no significant differences in stress-related physiological parameters, such as stress hormones or qEEG, between the two groups. Subjective device satisfaction was significantly higher in the experimental group than in the control group (P = 0.008). CONCLUSION Neurofeedback-assisted meditation using a wearable device can help improve subjective stress reduction compared with non-assisted meditation. These results support neurofeedback as an effective adjunct to meditation for relieving stress. TRIAL REGISTRATION Clinical Research Information Service Identifier: KCT0007413.
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Affiliation(s)
- Eunyoung Lee
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jung Kyung Hong
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Hayun Choi
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
- Department of Psychiatry, Veteran Health Service Medical Center, Seoul, Korea
| | - In-Young Yoon
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea.
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Sim S, Maldonado IL, Castelnau P, Barantin L, El-Hage W, Andersson F, Cottier JP. Neural correlates of mindfulness meditation and hypnosis on magnetic resonance imaging: similarities and differences. A scoping review. J Neuroradiol 2024; 51:131-144. [PMID: 37981196 DOI: 10.1016/j.neurad.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/23/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Mindfulness meditation (MM) and hypnosis practices are gaining interest in mental health, but their physiological mechanisms remain poorly understood. This study aimed to synthesize the functional, morphometric and metabolic changes associated with each practice using magnetic resonance imaging (MRI), and to identify their similarities and differences. METHODS MRI studies investigating MM and hypnosis in mental health, specifically stress, anxiety, and depression, were systematically screened following PRISMA guidelines from four research databases (PubMed, Web of Science, Embase, PsycINFO) between 2010 and 2022. RESULTS In total, 97 references met the inclusion criteria (84 for MM and 13 for hypnosis). This review showed common and divergent points regarding the regions involved and associated brain connectivity during MM practice and hypnosis. The primary commonality between mindfulness and hypnosis was decreased default mode network intrinsic activity and increased central executive network - salience network connectivity. Increased connectivity between the default mode network and the salience network was observed in meditative practice and mindfulness predisposition, but not in hypnosis. CONCLUSIONS While MRI studies provide a better understanding of the neural basis of hypnosis and meditation, this review underscores the need for more rigorous studies.
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Affiliation(s)
- Sindy Sim
- CHRU de Tours, service de radiologie, Tours, France
| | | | - Pierre Castelnau
- UMR 1253, iBrain, Université de Tours, Inserm, Tours, France; Service de Neuropédiatrie et Handicaps, Hôpital Clocheville, CHRU, Tours, France; CUMIC, Collège Universitaire des Médecines Intégratives et Complémentaires, Nantes, France
| | | | - Wissam El-Hage
- UMR 1253, iBrain, Université de Tours, Inserm, Tours, France; CHRU de Tours, Clinique Psychiatrique Universitaire, Tours, France
| | | | - Jean-Philippe Cottier
- CHRU de Tours, service de radiologie, Tours, France; UMR 1253, iBrain, Université de Tours, Inserm, Tours, France; CUMIC, Collège Universitaire des Médecines Intégratives et Complémentaires, Nantes, France.
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Braun J, Patel M, Kameneva T, Keatch C, Lambert G, Lambert E. Central stress pathways in the development of cardiovascular disease. Clin Auton Res 2024; 34:99-116. [PMID: 38104300 DOI: 10.1007/s10286-023-01008-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/02/2023] [Indexed: 12/19/2023]
Abstract
PURPOSE Mental stress is of essential consideration when assessing cardiovascular pathophysiology in all patient populations. Substantial evidence indicates associations among stress, cardiovascular disease and aberrant brain-body communication. However, our understanding of the flow of stress information in humans, is limited, despite the crucial insights this area may offer into future therapeutic targets for clinical intervention. METHODS Key terms including mental stress, cardiovascular disease and central control, were searched in PubMed, ScienceDirect and Scopus databases. Articles indicative of heart rate and blood pressure regulation, or central control of cardiovascular disease through direct neural innervation of the cardiac, splanchnic and vascular regions were included. Focus on human neuroimaging research and the flow of stress information is described, before brain-body connectivity, via pre-motor brainstem intermediates is discussed. Lastly, we review current understandings of pathophysiological stress and cardiovascular disease aetiology. RESULTS Structural and functional changes to corticolimbic circuitry encode stress information, integrated by the hypothalamus and amygdala. Pre-autonomic brain-body relays to brainstem and spinal cord nuclei establish dysautonomia and lead to alterations in baroreflex functioning, firing of the sympathetic fibres, cellular reuptake of norepinephrine and withdrawal of the parasympathetic reflex. The combined result is profoundly adrenergic and increases the likelihood of cardiac myopathy, arrhythmogenesis, coronary ischaemia, hypertension and the overall risk of future sudden stress-induced heart failure. CONCLUSIONS There is undeniable support that mental stress contributes to the development of cardiovascular disease. The emerging accumulation of large-scale multimodal neuroimaging data analytics to assess this relationship promises exciting novel therapeutic targets for future cardiovascular disease detection and prevention.
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Affiliation(s)
- Joe Braun
- School of Health Sciences, Swinburne University of Technology, PO Box 218, Hawthorn, Melbourne, VIC, 3122, Australia.
| | - Mariya Patel
- School of Health Sciences, Swinburne University of Technology, PO Box 218, Hawthorn, Melbourne, VIC, 3122, Australia
| | - Tatiana Kameneva
- Iverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Charlotte Keatch
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Gavin Lambert
- School of Health Sciences, Swinburne University of Technology, PO Box 218, Hawthorn, Melbourne, VIC, 3122, Australia
- Iverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia
| | - Elisabeth Lambert
- School of Health Sciences, Swinburne University of Technology, PO Box 218, Hawthorn, Melbourne, VIC, 3122, Australia
- Iverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia
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Mitsea E, Drigas A, Skianis C. Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review. Behav Sci (Basel) 2023; 13:1008. [PMID: 38131865 PMCID: PMC10740653 DOI: 10.3390/bs13121008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/27/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities.
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Affiliation(s)
- Eleni Mitsea
- Net Media Lab & Mind & Brain R&D, Institute of Informatics & Telecommunications, National Centre of Scientific Research ‘Demokritos’ Athens, Agia Paraskevi, 15341 Athens, Greece;
- Department of Information and Communication Systems Engineering, University of Aegean, 82300 Mytilene, Greece;
| | - Athanasios Drigas
- Net Media Lab & Mind & Brain R&D, Institute of Informatics & Telecommunications, National Centre of Scientific Research ‘Demokritos’ Athens, Agia Paraskevi, 15341 Athens, Greece;
| | - Charalabos Skianis
- Department of Information and Communication Systems Engineering, University of Aegean, 82300 Mytilene, Greece;
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Shang B, Duan F, Fu R, Gao J, Sik H, Meng X, Chang C. EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning. Front Hum Neurosci 2023; 17:1033420. [PMID: 37719770 PMCID: PMC10500069 DOI: 10.3389/fnhum.2023.1033420] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 06/16/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP). Methods We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario. Results For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively. Conclusion Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.
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Affiliation(s)
- Baoxiang Shang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Feiyan Duan
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Deepbay Innovation Technology Corporation Ltd., Shenzhen, China
| | - Ruiqi Fu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Junling Gao
- Buddhist Practice and Counselling Science Lab, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Hinhung Sik
- Buddhist Practice and Counselling Science Lab, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Chunqi Chang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
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Todd J, Aspell JE. Mindfulness, Interoception, and the Body. Brain Sci 2022; 12:brainsci12060696. [PMID: 35741582 PMCID: PMC9220884 DOI: 10.3390/brainsci12060696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 02/01/2023] Open
Abstract
In recent years, there has been a surge of interest in the topics of interoception and mindfulness from researchers, clinicians, and the general public alike (e [...]
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Affiliation(s)
- Jennifer Todd
- School of Psychology and Sport Science, Anglia Ruskin University, Cambridge CB1 1PT, UK;
- Centre for Psychological Medicine, Perdana University, Kuala Lumpur 50490, Malaysia
- Correspondence:
| | - Jane E. Aspell
- School of Psychology and Sport Science, Anglia Ruskin University, Cambridge CB1 1PT, UK;
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