The Experts below are selected from a list of 9270 Experts worldwide ranked by ideXlab platform
Yury Koush - One of the best experts on this subject based on the ideXlab platform.
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can we predict real time fmri Neurofeedback learning success from pre training brain activity
bioRxiv, 2020Co-Authors: Stavros Skouras, Ronald Sladky, Amelie Haugg, Amalia Mcdonald, Camero R Craddock, Matthias Kirschne, Marcus Herdene, Yury KoushAbstract:Abstract Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI Neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different Neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of Neurofeedback) could predict Neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and Neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general Neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence Neurofeedback learning.
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opennft an open source python matlab framework for real time fmri Neurofeedback training based on activity connectivity and multivariate pattern analysis
NeuroImage, 2017Co-Authors: Yury Koush, Joh Ashburne, Evgeny Prilepi, Ronald Sladky, Pete Zeidma, S A Ibikov, Frank ScharnowskiAbstract:Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI Neurofeedback become possible. Here we outline the core components of a novel open-source Neurofeedback framework, termed Open Neurofeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include Neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel Neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.
Frank Scharnowski - One of the best experts on this subject based on the ideXlab platform.
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The effects of psychiatric history and age on self-regulation of the default mode network
NeuroImage, 2019Co-Authors: Stavros Skouras, Frank ScharnowskiAbstract:Abstract Real-time Neurofeedback enables human subjects to learn to regulate their brain activity, effecting behavioral changes and improvements of psychiatric symptomatology. Neurofeedback up-regulation and down-regulation have been assumed to share common neural correlates. Neuropsychiatric pathology and aging incur suboptimal functioning of the default mode network. Despite the exponential increase in real-time neuroimaging studies, the effects of aging, pathology and the direction of regulation on Neurofeedback performance remain largely unknown. Using real-time fMRI data shared through the Rockland Sample Real-Time Neurofeedback project (N = 136) and open-access analyses, we first modeled Neurofeedback performance and learning in a group of subjects with psychiatric history (na = 74) and a healthy control group (nb = 62). Subsequently, we examined the relationship between up-regulation and down-regulation learning, the relationship between age and Neurofeedback performance in each group and differences in Neurofeedback performance between the two groups. For interpretative purposes, we also investigated functional connectomics prior to Neurofeedback. Results show that in an initial session of default mode network Neurofeedback with real-time fMRI, up-regulation and down-regulation learning scores are negatively correlated. This finding is related to resting state differences in the eigenvector centrality of the posterior cingulate cortex. Moreover, age correlates negatively with default mode network Neurofeedback performance, only in absence of psychiatric history. Finally, adults with psychiatric history outperform healthy controls in default mode network up-regulation. Interestingly, the performance difference is related to no up-regulation learning in controls. This finding is supported by marginally higher default mode network centrality during resting state, in the presence of psychiatric history.
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The effects of psychiatric history and age on self-regulation of the default mode network
bioRxiv, 2018Co-Authors: Stavros Skouras, Frank ScharnowskiAbstract:Real-time Neurofeedback enables human subjects to learn to regulate their brain activity, effecting behavioral changes and improvements of psychiatric symptomatology. Neurofeedback up-regulation and down-regulation have been assumed to share common neural correlates. Neuropsychiatric pathology and aging incur suboptimal functioning of the default mode network. Despite the exponential increase in real-time neuroimaging studies, the effects of aging, pathology and the direction of regulation on Neurofeedback performance remain largely unknown. Using open-access analyses and real-time fMRI data shared through the Rockland Sample Real-Time Neurofeedback project (N=136), we first modeled Neurofeedback performance and learning in a group of subjects with psychiatric history (na=74) and a healthy control group (nb=62). Subsequently, we examined the relationship between up-regulation and down-regulation learning, the relationship between age and Neurofeedback performance in each group and differences in Neurofeedback performance between the two groups. Results show that in an initial session of default mode network Neurofeedback with real-time fMRI, up-regulation and down-regulation learning scores are negatively correlated. Moreover, age correlates negatively with default mode network Neurofeedback performance, only in absence of psychiatric history. Finally, adults with psychiatric history outperform healthy controls in default mode network up-regulation. Interestingly, the performance difference is related to no up-regulation learning in controls.
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opennft an open source python matlab framework for real time fmri Neurofeedback training based on activity connectivity and multivariate pattern analysis
NeuroImage, 2017Co-Authors: Yury Koush, Joh Ashburne, Evgeny Prilepi, Ronald Sladky, Pete Zeidma, S A Ibikov, Frank ScharnowskiAbstract:Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI Neurofeedback become possible. Here we outline the core components of a novel open-source Neurofeedback framework, termed Open Neurofeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include Neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel Neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.
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Closed-loop brain training: the science of Neurofeedback
Nature Reviews Neuroscience, 2017Co-Authors: Ranganatha Sitaram, Frank Scharnowski, Nikolaus Weiskopf, Luke Stoeckel, Sven Haller, Jarrod Lewis-peacock, Maria Laura Blefari, Mohit Rana, Ethan Oblak, Niels BirbaumerAbstract:Neurofeedback is a type of biofeedback in which neural activity is measured and presented through one or more sensory channels to the participant in real time to facilitate self-regulation of the putative neural substrates that underlie a particular behaviour or pathology Animal and human brain self-regulation has been demonstrated using various invasive and non-invasive recording methods and with different features of the brain signals, such as frequency spectra, functional connectivity or spatiotemporal patterns of brain activity Neurofeedback provides the possibility of endogenously manipulating brain activity as an independent variable, making it a powerful neuroscientific tool Neurofeedback training results in specific neural changes relevant to the trained brain circuit and the associated behavioural changes. These changes have been shown to last anywhere from hours to months after training and to correlate with changes in grey and white matter structure The underlying neural circuitry relating to the process of brain self-regulation is becoming clearer. Accumulating evidence suggests the involvement of the thalamus and the dorsolateral prefrontal, posterior parietal and occipital cortices in Neurofeedback control, and the dorsal and ventral striatum, anterior cingulate cortex and anterior insula in Neurofeedback reward processing Psychological factors, such as the differential influence of feedback, reward and experimental instructions, and other factors, such as sense of agency and locus of control, are now being investigated for their effects on Neurofeedback The demonstration of robust clinical effects remains a major hurdle in Neurofeedback research. The results of randomized controlled trials in attention deficit and hyperactivity disorder and stroke rehabilitation have been mixed, and have been affected by differences in study design, difficulty of identifying responders and the scarcity of homogenous patient populations Future Neurofeedback research will probably clarify the psychological and neural mechanisms that may help to address issues in clinical translation In Neurofeedback, an individual receives online feedback of their neural activity to facilitate self-regulation of a brain region and, as a result, a particular behaviour or pathology. In this Review, the authors examine how this technique has been used and its underlying mechanisms. Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain–machine interfaces, Neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying Neurofeedback, which have started to be uncovered. We also discuss how Neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.
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closed loop brain training the science of Neurofeedback
Nature Reviews Neuroscience, 2017Co-Authors: Ranganatha Sitaram, Frank Scharnowski, Nikolaus Weiskopf, Luke Stoeckel, Tomas Ros, Sve Halle, Jarrod A Lewispeacock, Maria Laura Lefari, Mohi RanaAbstract:Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain-machine interfaces, Neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying Neurofeedback, which have started to be uncovered. We also discuss how Neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.
Ronald Sladky - One of the best experts on this subject based on the ideXlab platform.
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can we predict real time fmri Neurofeedback learning success from pre training brain activity
bioRxiv, 2020Co-Authors: Stavros Skouras, Ronald Sladky, Amelie Haugg, Amalia Mcdonald, Camero R Craddock, Matthias Kirschne, Marcus Herdene, Yury KoushAbstract:Abstract Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI Neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different Neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of Neurofeedback) could predict Neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and Neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general Neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence Neurofeedback learning.
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opennft an open source python matlab framework for real time fmri Neurofeedback training based on activity connectivity and multivariate pattern analysis
NeuroImage, 2017Co-Authors: Yury Koush, Joh Ashburne, Evgeny Prilepi, Ronald Sladky, Pete Zeidma, S A Ibikov, Frank ScharnowskiAbstract:Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI Neurofeedback become possible. Here we outline the core components of a novel open-source Neurofeedback framework, termed Open Neurofeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include Neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel Neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.
Marco Congedo - One of the best experts on this subject based on the ideXlab platform.
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Neurofeedback en psychiatrie : une technique du présent ? [Neurofeedback: one of today's techniques in psychiatry?]
L'Encéphale, 2017Co-Authors: Martijn Arns, Jean-marie Batail, Stéphanie Bioulac, Marco Congedo, C. Daudet, Dominique Drapier, Thomas Fovet, Renaud Jardri, Le Van Quyen, Fabien LotteAbstract:ObjectivesNeurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of Neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) Neurofeedback and real-time functional magnetic resonance imaging (fMRI) Neurofeedback for therapeutic application in psychiatry.MethodLiterature on Neurofeedback and mental disorders but also on Brain Computer Interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the NExT (Neurofeedback Evaluation & Training) section of the French Association of Biological Psychiatry and Neuropsychopharmacology (AFPBN).ResultsResults show a potential efficacy of EEG-Neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-Neurofeedback in clinical practice. Regarding fMRI-Neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics), and techniques (EEG, fMRI, signal processing). ConclusionThe field of Neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain-computer-interfaces. Future studies should determine the criteria for optimizing Neurofeedback sessions. A better understanding of the learning processes underpinning Neurofeedback could be a key element to develop the use of this technique in clinical practice.
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Neurofeedback: one of today's techniques in psychiatry?
Encephale-revue De Psychiatrie Clinique Biologique Et Therapeutique, 2016Co-Authors: Martijn Arns, Jean-marie Batail, Stéphanie Bioulac, Marco Congedo, C. Daudet, Dominique Drapier, Thomas Fovet, Renaud Jardri, M. Le-van-quyen, Fabien LotteAbstract:Objectives Neurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of Neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) Neurofeedback and real-time functional magnetic resonance imaging (fMRI) Neurofeedback for therapeutic application in psychiatry. Method Literature on Neurofeedback and mental disorders but also on Brain Computer Interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the NExT (Neurofeedback Evaluation & Training) section of the French Association of Biological Psychiatry and Neuropsychopharmacology (AFPBN). Results Results show a potential efficacy of EEG-Neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-Neurofeedback in clinical practice. Regarding fMRI-Neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics), and techniques (EEG, fMRI, signal processing). Conclusion The field of Neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain-computer-interfaces. Future studies should determine the criteria for optimizing Neurofeedback sessions. A better understanding of the learning processes underpinning Neurofeedback could be a key element to develop the use of this technique in clinical practice.
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Source-based Neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation
Frontiers in Behavioral Neuroscience, 2014Co-Authors: David J White, Marco Congedo, Joseph CiorciariAbstract:A developing literature explores the use of Neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst Neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any Neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional Neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional Neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG Neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of Neurofeedback targeting this individually defined functional brain source. Source-based EEG Neurofeedback methods using BSS may offer important advantages over traditional Neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG Neurofeedback.
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Neurofeedback improves executive functioning in children with autism spectrum disorders
Research in Autism Spectrum Disorders, 2009Co-Authors: Mirjam Kouijzer, Marco Congedo, Jan De Moor, Berrie Gerrits, Hein Van SchieAbstract:Seven autistic children diagnosed with autism spectrum disorders (ASD) received a Neurofeedback treatment that aimed to improve their level of executive control. Neurofeedback successfully reduced children's heightened theta/beta ratio by inhibiting theta activation and enhancing beta activation over sessions. Following treatment, children's executive capacities were found to have improved greatly relative to pre-treatment assessment on a range of executive function tasks. Additional improvements were found in children's social, communicative and typical behavior, relative to a waiting list control group. These findings suggest a basic executive function impairment in ASD that can be alleviated through specific Neurofeedback treatment. Possible neural mechanisms that may underlie Neurofeedback mediated improvement in executive functioning in autistic children are discussed.
D. Corydon Hammond - One of the best experts on this subject based on the ideXlab platform.
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Integrating Clinical Hypnosis and Neurofeedback.
American Journal of Clinical Hypnosis, 2019Co-Authors: D. Corydon HammondAbstract:Hypnosis and Neurofeedback each provide unique therapeutic strengths and opportunities. This article provides an overview of some of the research on Neurofeedback and hypnosis. The author’s perspec...
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19 Channel Z-Score and LORETA Neurofeedback: Does the Evidence Support the Hype?
Applied Psychophysiology and Biofeedback, 2019Co-Authors: Robert Coben, D. Corydon Hammond, Martijn ArnsAbstract:Neurofeedback is a well-investigated treatment for ADHD and epilepsy, especially when restricted to standard protocols such as theta/beta, slow cortical potentials and sensori-motor rhythm Neurofeedback. Advances in any field are welcome and other techniques are being pursued. Manufacturers and clinicians are marketing ‘superior’ Neurofeedback approaches including 19 channel Z-score Neurofeedback (ZNFB) and 3-D LORETA Neurofeedback (with or without Z-scores; LNFB). We conducted a review of the empirical literature to determine if such claims were warranted. This review included the above search terms in Pubmed, Google scholar and any references that met our criteria from the ZNFB publication list and was restricted to group based studies examining improvement in a clinical population that underwent peer review (book chapters, magazine articles or conference presentations are not included since these are not peer reviewed). Fifteen relevant studies emerged with only six meeting our criterion. Based on review of these studies it was concluded that empirical validation of these approaches is sorely lacking. There is no empirical data that supports the notion that 19-channel z-score Neurofeedback is effective or superior. The quality of studies for LNFB was better compared to ZNFB and some suggestion for efficacy was demonstrated for ADHD and Tinnitus distress. However, these findings need to be replicated, extended to other populations and have yet to show any “superiority.” Our conclusions continue to emphasize the pervasive lack of evidence supporting these approaches to Neurofeedback and the implications of this are discussed.
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LENS : the Low Energy Neurofeedback System
2013Co-Authors: D. Corydon HammondAbstract:* Preface (Tim Tinius) * Introduction (D. Corydon Hammond) * The Low Energy Neurofeedback System (LENS): Theory, Background, and Introduction (Len Ochs) * Treatment of Fibromyalgia Syndrome Using Low-Intensity Neurofeedback with the Flexyx Neurotherapy System: A Randomized Controlled Clinical Trial (Howard M. Kravitz, Mary Lee Esty, Robert S. Katz, and Jan Fawcett) * Comment on the Treatment of Fibromyalgia Syndrome Using Low- Intensity Neurofeedback with the Flexyx Neurotherapy System: A Randomized Controlled Clinical Trial, or How to Go Crazy Over Nearly Nothing (Len Ochs) * Reflections on FMS Treatment, Research, and Neurotherapy: Cautionary Tales (Mary Lee Esty) * The LENS (Low Energy Neurofeedback System): A Clinical Outcomes Study on One Hundred Patients at Stone Mountain Center, New York (Stephen Larsen, Kristen Harrington, and Susan Hicks) * Effective Use of LENS Unit as an Adjunct to Cognitive Neuro-Developmental Training (Curtis T Cripe) * The LENS Neurofeedback with Animals (Stephen Larsen, Robin Larsen, D. Corydon Hammond, Stephen Sheppard, Len Ochs, Sloan Johnson, Carla Adinaro, and Carrie Chapman) * Index * Reference Notes Included
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What is Neurofeedback: An Update
Journal of Neurotherapy, 2011Co-Authors: D. Corydon HammondAbstract:Written to educate both professionals and the general public, this article provides an update and overview of the field of Neurofeedback (EEG biofeedback). The process of assessment and Neurofeedback training is explained. Then, areas in which Neurofeedback is being used as a treatment are identified and a survey of research findings is presented. Potential risks, side effects, and adverse reactions are cited and guidelines provided for selecting a legitimately qualified practitioner.
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What Is Neurofeedback
Journal of Neurotherapy, 2007Co-Authors: D. Corydon HammondAbstract:ABSTRACT EEG biofeedback (Neurofeedback) originated in the late 1960s as a method for retraining brainwave patterns through operant conditioning. Since that time a sizable body of research has accumulated on the effectiveness of Neurofeedback in the treatment of uncontrolled epilepsy, ADD/ADHD, anxiety, alcoholism, posttraumatic stress disorder, and mild head injuries. Studies also provide encouraging indications that Neurofeedback offers a treatment alternative for use with learning disabilities, stroke, depression, fibromyalgia, autism, insomnia, tinnitus, headaches, problems with physical balance, and for the enhancement of peak performance. At a time when an increasing number of people are concerned with negative effects from relying solely on medication treatments, Neurofeedback may offer an additional treatment alternative for many conditions. This article assists the reader to understand how Neurofeedback works, how assessment allows Neurofeedback to be individualized, and briefly reviews evidence ...