1
|
Muñoz-Moldes S, Tursic A, Lührs M, Eck J, Benitez Andonegui A, Peters J, Cleeremans A, Goebel R. Online self-evaluation of fMRI-based neurofeedback performance. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230089. [PMID: 39428884 PMCID: PMC11491843 DOI: 10.1098/rstb.2023.0089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/29/2024] [Accepted: 06/01/2024] [Indexed: 10/22/2024] Open
Abstract
This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level (TL) of blood-oxygen-level-dependent (BOLD) activation. Here, we studied two types of self-evaluation: performance predictions and perceived confidence in the prediction judgement. Participants completed three sessions of SMA regulation in a 7 T fMRI scanner, performing a mental drawing task. During each trial, they modulated their imagery strategy to achieve one of two different levels of SMA activation and reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants' performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not correlate with better and worse predictions. In addition to shedding light on mechanisms of internal self-evaluation during neurofeedback training, these results also point to a dissociation between predictions of performance and confidence reports in the presence of feedback. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.
Collapse
Affiliation(s)
- Santiago Muñoz-Moldes
- Consciousness, Cognition and Computation group, Center for Research in Cognition & Neuroscience, Faculty of Psychology and Education, Université Libre de Bruxelles, Brussels, Belgium
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Anita Tursic
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Michael Lührs
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Judith Eck
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Amaia Benitez Andonegui
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Judith Peters
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Axel Cleeremans
- Consciousness, Cognition and Computation group, Center for Research in Cognition & Neuroscience, Faculty of Psychology and Education, Université Libre de Bruxelles, Brussels, Belgium
| | - Rainer Goebel
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
2
|
Wang X, Zhou X, Li J, Gong Y, Feng Z. A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression. Front Psychiatry 2023; 14:1253727. [PMID: 38125285 PMCID: PMC10732355 DOI: 10.3389/fpsyt.2023.1253727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/06/2023] [Indexed: 12/23/2023] Open
Abstract
Anhedonia is a hallmark symptom of depression that often lacks adequate interventions. The translational gap remains in clinical treatments based on neural substrates of anhedonia. Our pilot study found that depressed individuals depended less on goal-directed (GD) reward learning (RL), with reduced reward prediction error (RPE) BOLD signal. Previous studies have found that anhedonia is related to abnormal activities and/or functional connectivities of the central executive network (CEN) and salience network (SN), both of which belong to the goal-directed system. In addition, it was found that real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) could improve the balance between CEN and SN in healthy individuals. Therefore, we speculate that rt-fMRI NF of the CEN and SN associated with the GD system may improve depressive and/or anhedonic symptoms. Therefore, this study (1) will examine individuals with anhedonic depression using GD-RL behavioral task, combined with functional magnetic resonance imaging and computational modeling to explore the role of CEN/SN deficits in anhedonic depression; and (2) will utilize network-based rt-fMRI NF to investigate whether it is feasible to regulate the differential signals of brain CEN/SN of GD system through rt-fMRI NF to alleviate depressive and/or anhedonic symptoms. This study highlights the need to elucidate the intervention effects of rt-fMRI NF and the underlying computational network neural mechanisms.
Collapse
Affiliation(s)
- Xiaoxia Wang
- Department of Basic Psychology, School of Psychology, Army Medical University, Chongqing, China
| | - Xiaoyan Zhou
- Chongqing City Mental Health Center, Southwest University, Chongqing, China
| | - Jing Li
- Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Yushun Gong
- Department of Medical Equipment and Metrology, College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Zhengzhi Feng
- School of Psychology, Army Medical University, Chongqing, China
| |
Collapse
|
3
|
Bressler RA, Raible S, Lührs M, Tier R, Goebel R, Linden DE. No threat: Emotion regulation neurofeedback for police special forces recruits. Neuropsychologia 2023; 190:108699. [PMID: 37816480 DOI: 10.1016/j.neuropsychologia.2023.108699] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 08/15/2023] [Accepted: 10/06/2023] [Indexed: 10/12/2023]
Abstract
Police officers of the Special Forces are confronted with highly demanding situations in terms of stress, high tension and threats to their lives. Their tasks are specifically high-risk operations, such as arrests of armed suspects and anti-terror interventions. Improving the emotion regulation skills of police officers might be a vital investment, supporting them to stay calm and focused. A promising approach is training emotion regulation by using real-time (rt-) fMRI neurofeedback. Specifically, downregulating activity in key areas of the fronto-limbic emotion regulation network in the presence of threatening stimuli. Thirteen recruits of the Dutch police special forces underwent six weekly rt-fMRI sessions, receiving neurofeedback from individualized regions of their emotion regulation network. Their task was to reduce the image size of threatening images, wherein the image size represented their brain activity. A reduction in image size represented successful downregulation. Participants were free to use their preferred regulation strategy. A control group of fifteen recruits received no neurofeedback. Both groups completed behavioural tests (image rating on evoked valence and arousal, questionnaire) before and after the neurofeedback training. We hypothesized that the neurofeedback group would improve in downregulation and would score better than the control group on the behavioural tests after the neurofeedback training. Neurofeedback training resulted in a significant decrease in image size (t(12) = 2.82, p = .015) and a trend towards decreased activation in the target regions (t(10) = 1.82, p = .099) from the first to the last session. Notably, subjects achieved downregulation below the pre-stimulus baseline in the last two sessions. No relevant differences between groups were found in the behavioural tasks. Through the training of rt-fMRI neurofeedback, participants learned to downregulate the activity in individualized areas of the emotion regulation network, by using their own preferred strategies. The lack of behavioural between-group differences may be explained by floor effects. Tasks that are close to real-life situations may be needed to uncover behavioural correlates of this emotion regulation training.
Collapse
Affiliation(s)
- Ruben Andreas Bressler
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands.
| | - Sophie Raible
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands
| | - Michael Lührs
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands; Brain Innovation, Maastricht, The Netherlands, Oxfordlaan 55, 6229 EV, Maastricht, the Netherlands
| | - Ralph Tier
- Landelijke Eenheid, Dienst Speciale Interventies, Hoofdstraat 54, 3972 LB, Postbus 100, 3970 AC, Driebergen, the Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands; Brain Innovation, Maastricht, The Netherlands, Oxfordlaan 55, 6229 EV, Maastricht, the Netherlands
| | - David E Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands
| |
Collapse
|
4
|
Mirifar A, Keil A, Ehrlenspiel F. Neurofeedback and neural self-regulation: a new perspective based on allostasis. Rev Neurosci 2022; 33:607-629. [PMID: 35122709 PMCID: PMC9381001 DOI: 10.1515/revneuro-2021-0133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/13/2022] [Indexed: 11/15/2022]
Abstract
The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.
Collapse
Affiliation(s)
- Arash Mirifar
- Department of Sport and Health Sciences, Chair of Sport Psychology, Technische Universität München, Munich, Bavaria, Germany
- Institute of Sports Science, Leibniz UniversityHannover, Germany
| | - Andreas Keil
- Center for the Study of Emotion & Attention, University of Florida, Gainesville, Florida, United States of America
| | - Felix Ehrlenspiel
- Department of Sport and Health Sciences, Chair of Sport Psychology, Technische Universität München, Munich, Bavaria, Germany
| |
Collapse
|
5
|
Farkhondeh Tale Navi F, Heysieattalab S, Ramanathan DS, Raoufy MR, Nazari MA. Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs. Neuroscience 2021; 483:104-126. [PMID: 34902494 DOI: 10.1016/j.neuroscience.2021.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 11/27/2022]
Abstract
Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.
Collapse
Affiliation(s)
- Farhad Farkhondeh Tale Navi
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | - Soomaayeh Heysieattalab
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | | | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Ali Nazari
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran; Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
6
|
Haugg A, Renz FM, Nicholson AA, Lor C, Götzendorfer SJ, Sladky R, Skouras S, McDonald A, Craddock C, Hellrung L, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan J, Hendler T, Cohen Kadosh K, Zich C, Kohl SH, Hallschmid M, MacInnes J, Adcock RA, Dickerson KC, Chen NK, Young K, Bodurka J, Marxen M, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Lanius RA, Emmert K, Haller S, Van De Ville D, Kim DY, Lee JH, Marins T, Megumi F, Sorger B, Kamp T, Liew SL, Veit R, Spetter M, Weiskopf N, Scharnowski F, Steyrl D. Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. Neuroimage 2021; 237:118207. [PMID: 34048901 DOI: 10.1016/j.neuroimage.2021.118207] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 05/14/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
Collapse
Affiliation(s)
- Amelie Haugg
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria.
| | - Fabian M Renz
- Faculty of Psychology, University of Vienna, Austria
| | | | - Cindy Lor
- Faculty of Psychology, University of Vienna, Austria
| | | | - Ronald Sladky
- Faculty of Psychology, University of Vienna, Austria
| | - Stavros Skouras
- Department of Biological and Medical Psychology, University of Bergen, Norway
| | - Amalia McDonald
- Department of Psychology, University of Virginia, United States
| | - Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, United States
| | - Lydia Hellrung
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Canada
| | - Marcus Herdener
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland
| | - Yury Koush
- Department of Radiology and Biomedical Imaging, Yale University, United States
| | - Marina Papoutsi
- UCL Huntington's Disease Centre, Institute of Neurology, University College London, United Kingdom; IXICO plc, United Kingdom
| | - Jackob Keynan
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
| | - Talma Hendler
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
| | | | - Catharina Zich
- Nuffiled Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Germany
| | - Manfred Hallschmid
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany
| | - Jeff MacInnes
- Institute for Learning and Brain Sciences, University of Washington, United States
| | - R Alison Adcock
- Duke Institute for Brain Sciences, Duke University, United States; Department of Psychiatry and Behavioral Sciences, Duke University, United States
| | - Kathryn C Dickerson
- Department of Psychiatry and Behavioral Sciences, Duke University, United States
| | - Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, United States
| | - Kymberly Young
- Department of Psychiatry, School of Medicine, University of Pittsburgh, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, United States; Stephenson School of Biomedical Engineering, University of Oklahoma, United States
| | - Michael Marxen
- Department of Psychiatry, Technische Universität Dresden, Germany
| | - Shuxia Yao
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
| | - Benjamin Becker
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
| | - Tibor Auer
- School of Psychology, University of Surrey, United Kingdom
| | | | - Gustavo Pamplona
- Department of Ophthalmology, University of Lausanne and Fondation Asile des Aveugles, Switzerland
| | - Ruth A Lanius
- Department of Psychiatry, University of Western Ontario, Canada
| | - Kirsten Emmert
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel University, Germany
| | - Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Sweden
| | - Dimitri Van De Ville
- Center for Neuroprosthetics, Ecole polytechnique féderale de Lausanne, Switzerland; Faculty of Medicine, University of Geneva, Switzerland
| | - Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Korea
| | - Theo Marins
- D'Or Institute for Research and Education, Brazil
| | | | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Tabea Kamp
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | | | - Ralf Veit
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany; High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Germany
| | - Maartje Spetter
- School of Psychology, University of Birmingham, United Kingdom
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Germany
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
| | - David Steyrl
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
| |
Collapse
|
7
|
Baqapuri HI, Roes LD, Zvyagintsev M, Ramadan S, Keller M, Roecher E, Zweerings J, Klasen M, Gur RC, Mathiak K. A Novel Brain-Computer Interface Virtual Environment for Neurofeedback During Functional MRI. Front Neurosci 2021; 14:593854. [PMID: 33505237 PMCID: PMC7830095 DOI: 10.3389/fnins.2020.593854] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 12/01/2020] [Indexed: 12/11/2022] Open
Abstract
Virtual environments (VEs), in the recent years, have become more prevalent in neuroscience. These VEs can offer great flexibility, replicability, and control over the presented stimuli in an immersive setting. With recent developments, it has become feasible to achieve higher-quality visuals and VEs at a reasonable investment. Our aim in this project was to develop and implement a novel real-time functional magnetic resonance imaging (rt-fMRI)-based neurofeedback (NF) training paradigm, taking into account new technological advances that allow us to integrate complex stimuli into a visually updated and engaging VE. We built upon and developed a first-person shooter in which the dynamic change of the VE was the feedback variable in the brain-computer interface (BCI). We designed a study to assess the feasibility of the BCI in creating an immersive VE for NF training. In a randomized single-blinded fMRI-based NF-training session, 24 participants were randomly allocated into one of two groups: active and reduced contingency NF. All participants completed three runs of the shooter-game VE lasting 10 min each. Brain activity in a supplementary motor area region of interest regulated the possible movement speed of the player's avatar and thus increased the reward probability. The gaming performance revealed that the participants were able to actively engage in game tasks and improve across sessions. All 24 participants reported being able to successfully employ NF strategies during the training while performing in-game tasks with significantly higher perceived NF control ratings in the NF group. Spectral analysis showed significant differential effects on brain activity between the groups. Connectivity analysis revealed significant differences, showing a lowered connectivity in the NF group compared to the reduced contingency-NF group. The self-assessment manikin ratings showed an increase in arousal in both groups but failed significance. Arousal has been linked to presence, or feelings of immersion, supporting the VE's objective. Long paradigms, such as NF in MRI settings, can lead to mental fatigue; therefore, VEs can help overcome such limitations. The rewarding achievements from gaming targets can lead to implicit learning of self-regulation and may broaden the scope of NF applications.
Collapse
Affiliation(s)
- Halim I. Baqapuri
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Linda D. Roes
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Mikhail Zvyagintsev
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Souad Ramadan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Micha Keller
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Erik Roecher
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Martin Klasen
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany
| |
Collapse
|
8
|
Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan JN, Hendler T, Cohen Kadosh K, Zich C, MacInnes J, Adcock RA, Dickerson K, Chen N, Young K, Bodurka J, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Emmert K, Haller S, Van De Ville D, Blefari M, Kim D, Lee J, Marins T, Fukuda M, Sorger B, Kamp T, Liew S, Veit R, Spetter M, Weiskopf N, Scharnowski F. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? Hum Brain Mapp 2020; 41:3839-3854. [PMID: 32729652 PMCID: PMC7469782 DOI: 10.1002/hbm.25089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/18/2020] [Accepted: 05/26/2020] [Indexed: 12/31/2022] Open
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 inter-individual 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 pretraining 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 pretraining 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.
Collapse
Affiliation(s)
- Amelie Haugg
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Ronald Sladky
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Amalia McDonald
- Department of PsychologyUniversity of VirginiaCharlottesvilleVirginia
| | - Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexas
| | - Matthias Kirschner
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- McConnell Brain Imaging CentreMontréal Neurological Institute, McGill UniversityMontrealCanada
| | - Marcus Herdener
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
| | - Yury Koush
- Magnetic Resonance Research Center, Department of Radiology & Biomedical ImagingYale UniversityNew HavenConnecticut
| | - Marina Papoutsi
- UCL Huntington's Disease CentreInstitute of Neurology, University College LondonLondonEngland
| | - Jackob N. Keynan
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | - Talma Hendler
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | | | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordEngland
| | - Jeff MacInnes
- Institute for Learning and Brain SciencesUniversity of WashingtonSeattleWashington
| | - R. Alison Adcock
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Kathryn Dickerson
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Nan‐Kuei Chen
- Department of Biomedical EngineeringUniversity of ArizonaTucsonArizona
| | - Kymberly Young
- Department of Psychiatry, School of MedicineUniversity of PittsburghPittsburghPennsylvania
| | | | - Shuxia Yao
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Benjamin Becker
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordEngland
| | - Renate Schweizer
- Functional Imaging LaboratoryGerman Primate CenterGöttingenGermany
| | - Gustavo Pamplona
- Hôpital and Ophtalmique Jules GoninUniversity of LausanneLausanneSwitzerland
| | - Kirsten Emmert
- Department of NeurologyUniversity Medical Center Schleswig‐Holstein, Kiel UniversityKielGermany
| | - Sven Haller
- Radiology‐Department of Surgical SciencesUppsala UniversityUppsalaSweden
| | - Dimitri Van De Ville
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
- Department of Radiology and Medical Informatics, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
| | - Maria‐Laura Blefari
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
| | - Dong‐Youl Kim
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR)Rio de JaneiroBrazil
| | - Megumi Fukuda
- School of Fundamental Science and EngineeringWaseda UniversityTokyoJapan
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Tabea Kamp
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Sook‐Lei Liew
- Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Ralf Veit
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center MunichUniversity of TübingenTübingenGermany
| | - Maartje Spetter
- School of PsychologyUniversity of BirminghamBirminghamEngland
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Frank Scharnowski
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
| |
Collapse
|
9
|
Heunis S, Lamerichs R, Zinger S, Caballero‐Gaudes C, Jansen JFA, Aldenkamp B, Breeuwer M. Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Hum Brain Mapp 2020; 41:3439-3467. [PMID: 32333624 PMCID: PMC7375116 DOI: 10.1002/hbm.25010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/03/2020] [Indexed: 01/31/2023] Open
Abstract
Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.
Collapse
Affiliation(s)
- Stephan Heunis
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | - Rolf Lamerichs
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- Philips ResearchEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | | | - Jacobus F. A. Jansen
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
| | - Bert Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and NeuropsychologyGhent University HospitalGhentBelgium
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marcel Breeuwer
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Philips HealthcareBestThe Netherlands
| |
Collapse
|
10
|
Klöbl M, Michenthaler P, Godbersen GM, Robinson S, Hahn A, Lanzenberger R. Reinforcement and Punishment Shape the Learning Dynamics in fMRI Neurofeedback. Front Hum Neurosci 2020; 14:304. [PMID: 32792929 PMCID: PMC7393482 DOI: 10.3389/fnhum.2020.00304] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/08/2020] [Indexed: 12/22/2022] Open
Abstract
Introduction Neurofeedback (NF) using real-time functional magnetic resonance imaging (fMRI) has proven to be a valuable neuroscientific tool for probing cognition and promising therapeutic approach for several psychiatric disorders. Even though learning constitutes an elementary aspect of NF, the question whether certain training schemes might positively influence its dynamics has largely been neglected. Methods To address this issue, participants were trained to exert control on their subgenual anterior cingulate cortex (sgACC) blood-oxygenation-level-dependent signal, receiving either exclusively positive reinforcement (PR, “positive feedback”) or also positive punishment (PP, “negative feedback”). The temporal dynamics of the learning process were investigated by individually modeling the feedback periods and trends, offering the possibility to assess activation changes within and across blocks, runs and sessions. Results The results show faster initial learning of the PR + PP group by significantly lower deactivations of the sgACC in the first session and stronger regulation trends during the first runs. Independent of the group, significant control over the sgACC could further be shown with but not without feedback. Conclusion The beneficial effect of PP is supported by previous findings of multiple research domains suggesting that error avoidance represents an important motivational factor of learning, which complements the reward spectrum. This hypothesis warrants further investigation with respect to NF, as it could offer a way to generally facilitate the process of gaining volitional control over brain activity.
Collapse
Affiliation(s)
- Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Simon Robinson
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia.,Department of Neurology, Medical University of Graz, Graz, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
11
|
Mehler DMA, Williams AN, Whittaker JR, Krause F, Lührs M, Kunas S, Wise RG, Shetty HGM, Turner DL, Linden DEJ. Graded fMRI Neurofeedback Training of Motor Imagery in Middle Cerebral Artery Stroke Patients: A Preregistered Proof-of-Concept Study. Front Hum Neurosci 2020; 14:226. [PMID: 32760259 PMCID: PMC7373077 DOI: 10.3389/fnhum.2020.00226] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 05/20/2020] [Indexed: 02/04/2023] Open
Abstract
Ischemic stroke of the middle cerebral artery (MCA), a major brain vessel that supplies the primary motor and premotor cortex, is one of the most common causes for severe upper limb impairment. Currently available motor rehabilitation training largely lacks satisfying efficacy with over 70% of stroke survivors showing residual upper limb dysfunction. Motor imagery-based functional magnetic resonance imaging neurofeedback (fMRI-NF) has been suggested as a potential therapeutic technique to improve motor impairment in stroke survivors. In this preregistered proof-of-concept study (https://osf.io/y69jc/), we translated graded fMRI-NF training, a new paradigm that we have previously studied in healthy participants, to first-time MCA stroke survivors with residual mild to severe impairment of upper limb motor function. Neurofeedback was provided from the supplementary motor area (SMA) targeting two different neurofeedback target levels (low and high). We hypothesized that MCA stroke survivors will show (1) sustained SMA-region of interest (ROI) activation and (2) a difference in SMA-ROI activation between low and high neurofeedback conditions during graded fMRI-NF training. At the group level, we found only anecdotal evidence for these preregistered hypotheses. At the individual level, we found anecdotal to moderate evidence for the absence of the hypothesized graded effect for most subjects. These null findings are relevant for future attempts to employ fMRI-NF training in stroke survivors. The study introduces a Bayesian sequential sampling plan, which incorporates prior knowledge, yielding higher sensitivity. The sampling plan was preregistered together with a priori hypotheses and all planned analysis before data collection to address potential publication/researcher biases. Unforeseen difficulties in the translation of our paradigm to a clinical setting required some deviations from the preregistered protocol. We explicitly detail these changes, discuss the accompanied additional challenges that can arise in clinical neurofeedback studies, and formulate recommendations for how these can be addressed. Taken together, this work provides new insights about the feasibility of motor imagery-based graded fMRI-NF training in MCA stroke survivors and serves as a first example for comprehensive study preregistration of an (fMRI) neurofeedback experiment.
Collapse
Affiliation(s)
- David M. A. Mehler
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Angharad N. Williams
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
- Max Planck Adaptive Memory Research Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Joseph R. Whittaker
- School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Florian Krause
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
- Research Department, Brain Innovation B.V., Maastricht, Netherlands
| | - Stefanie Kunas
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Richard G. Wise
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, D'Annunzio University of Chieti–Pescara, Chieti, Italy
| | | | - Duncan L. Turner
- School of Health, Sport and Bioscience, University of East London, London, United Kingdom
| | - David E. J. Linden
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
12
|
Ros T, Enriquez-Geppert S, Zotev V, Young KD, Wood G, Whitfield-Gabrieli S, Wan F, Vuilleumier P, Vialatte F, Van De Ville D, Todder D, Surmeli T, Sulzer JS, Strehl U, Sterman MB, Steiner NJ, Sorger B, Soekadar SR, Sitaram R, Sherlin LH, Schönenberg M, Scharnowski F, Schabus M, Rubia K, Rosa A, Reiner M, Pineda JA, Paret C, Ossadtchi A, Nicholson AA, Nan W, Minguez J, Micoulaud-Franchi JA, Mehler DMA, Lührs M, Lubar J, Lotte F, Linden DEJ, Lewis-Peacock JA, Lebedev MA, Lanius RA, Kübler A, Kranczioch C, Koush Y, Konicar L, Kohl SH, Kober SE, Klados MA, Jeunet C, Janssen TWP, Huster RJ, Hoedlmoser K, Hirshberg LM, Heunis S, Hendler T, Hampson M, Guggisberg AG, Guggenberger R, Gruzelier JH, Göbel RW, Gninenko N, Gharabaghi A, Frewen P, Fovet T, Fernández T, Escolano C, Ehlis AC, Drechsler R, Christopher deCharms R, Debener S, De Ridder D, Davelaar EJ, Congedo M, Cavazza M, Breteler MHM, Brandeis D, Bodurka J, Birbaumer N, Bazanova OM, Barth B, Bamidis PD, Auer T, Arns M, Thibault RT. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). Brain 2020; 143:1674-1685. [PMID: 32176800 PMCID: PMC7296848 DOI: 10.1093/brain/awaa009] [Citation(s) in RCA: 160] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 10/10/2019] [Accepted: 10/28/2020] [Indexed: 02/02/2023] Open
Abstract
Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.
Collapse
Affiliation(s)
- Tomas Ros
- Departments of Neuroscience and Psychiatry, University of Geneva; Campus Biotech, Geneva, Switzerland
| | - Stefanie Enriquez-Geppert
- Department of Clinical Neuropsychology, University of Groningen, Groningen, The Netherlands
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, The Netherlands
| | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Kymberly D Young
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Guilherme Wood
- Institute of Psychology, University of Graz, Graz, Austria
| | - Susan Whitfield-Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Northeastern University, Boston, MA, USA
| | - Feng Wan
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | | | | | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL); Campus Biotech, Geneva, Switzerland
| | - Doron Todder
- Faculty of Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Beer-Sheva Mental Health Center, Israel Ministry of Health, Beer-Sheva, Israel
| | - Tanju Surmeli
- Living Health Center for Research and Education, Istanbul, Turkey
| | - James S Sulzer
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Ute Strehl
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Maurice Barry Sterman
- Neurobiology and Biobehavioral Psychiatry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Naomi J Steiner
- Boston University School of Medicine, Department of Pediatrics, Boston, MA, USA
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy (CCM), Charité - University Medicine Berlin, Berlin, Germany
| | - Ranganatha Sitaram
- Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Macul, Santiago, Chile
| | | | | | - Frank Scharnowski
- Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland
| | - Manuel Schabus
- University of Salzburg, Centre for Cognitive Neuroscience and Department of Psychology, Salzburg, Austria
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Miriam Reiner
- Technion, Israel Institute of Technology, Haifa, Israel
| | - Jaime A Pineda
- Cognitive Science Department, University of California, San Diego, CA, USA
| | - Christian Paret
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Germany
| | - Alexei Ossadtchi
- National Research University Higher School of Economics, Moscow, Russia
| | - Andrew A Nicholson
- Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland
| | - Wenya Nan
- Department of Psychology, Shanghai Normal University, Shanghai, China
| | | | | | - David M A Mehler
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Joel Lubar
- Department of Psychology, University of Tennessee, Knoxville, USA
| | - Fabien Lotte
- Inria Bordeaux Sud-Ouest/LaBRI University of Bordeaux - CNRS-Bordeaux INP, Bordeaux, France
| | - David E J Linden
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | | | - Mikhail A Lebedev
- Center for Bioelectric Interfaces of the Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- Department of Information and Internet Technologies of Digital Health Institute; I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Duke Center for Neuroengineering, Duke University, Durham, NC, USA
| | - Ruth A Lanius
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Andrea Kübler
- Department of Psychology I, Psychological Intervention, Behavior Analysis and Regulation of Behavior, University of Würzburg
| | - Cornelia Kranczioch
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenberg, Germany
| | - Yury Koush
- Magnetic Resonance Research Center (MRRC), Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Lilian Konicar
- Medical University of Vienna, Department of Child and Adolescent Psychiatry, Vienna, Austria
| | - Simon H Kohl
- JARA-Institute Molecular neuroscience and neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany
| | | | - Manousos A Klados
- Department of Psychology, The University of Sheffield International Faculty, City College, Thessaloniki, Greece
| | - Camille Jeunet
- CLLE Lab, CNRS, Université Toulouse Jean Jaurès, Toulouse, France
| | - T W P Janssen
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Rene J Huster
- Multimodal imaging and Cognitive Control Lab, Department of Psychology, University of Olso, Norway
| | - Kerstin Hoedlmoser
- University of Salzburg, Centre for Cognitive Neuroscience and Department of Psychology, Salzburg, Austria
| | | | - Stephan Heunis
- Electrical Engineering Department, Eindhoven University of Technology, The Netherlands
| | - Talma Hendler
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Adrian G Guggisberg
- Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital Geneva, Geneva, Switzerland
| | - Robert Guggenberger
- Division of Functional and Restorative Neurosurgery, University of Tübingen, Tübingen, Germany
| | - John H Gruzelier
- Department of Psychology, Goldsmiths, University of London, London, UK
| | - Rainer W Göbel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Nicolas Gninenko
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL); Campus Biotech, Geneva, Switzerland
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery, University of Tübingen, Tübingen, Germany
| | - Paul Frewen
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Thomas Fovet
- Univ. Lille, INSERM U1172, CHU LILLE, Centre Lille Neuroscience & Cognition, Pôle de Psychiatrie, F-59000, Lille, France
| | - Thalía Fernández
- UNAM Institute of Neurobiology, National Autonomous University of Mexico, Juriquilla, Mexico
| | | | - Ann-Christine Ehlis
- Psychophysiology and Optical Imaging, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Renate Drechsler
- Department of Child and Adolescent, Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zürich, Zürich, Switzerland
| | | | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenberg, Germany
| | - Dirk De Ridder
- Department of Surgery, Section of Neurosurgery, University of Otago, Dunedin, New Zealand
| | - Eddy J Davelaar
- Department of Psychological Sciences Birkbeck, University of London, Bloomsbury, London, UK
| | - Marco Congedo
- GIPSA-lab, CNRS, University Grenoble Alpes, Grenoble-INP, Grenoble, France
| | - Marc Cavazza
- School of Computing and Mathematical Sciences, University of Greenwich, London, UK
| | - Marinus H M Breteler
- Radboud University Nijmegen, Department of Clinical Psychology, Nijmegen, The Netherlands
| | - Daniel Brandeis
- Department of Child and Adolescent, Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zürich, Zürich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Niels Birbaumer
- Institute for Medical Psychology and Behavioural Neurobiology, University of Tübingen, Tübingen, Germany
| | - Olga M Bazanova
- State Research Institute of Physiology and Basic Medicine, Novosibirsk, Russia
| | - Beatrix Barth
- Psychophysiology and Optical Imaging, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | | | - Tibor Auer
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Martijn Arns
- Brainclinics Foundation, Research Institute Brainclinics, Nijmegen, The Netherlands
| | - Robert T Thibault
- School of Psychological Science, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| |
Collapse
|
13
|
Nan W, Yang L, Wan F, Zhu F, Hu Y. Alpha down-regulation neurofeedback training effects on implicit motor learning and consolidation. J Neural Eng 2020; 17:026014. [PMID: 32126528 DOI: 10.1088/1741-2552/ab7c1b] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Implicit motor learning, which is a non-conscious form of learning characterized by motor performance improvement with practice, plays an essential role in various daily activities. Earlier study using neurofeedback training (NFT), a type of brain-computer interaction that enables the user to learn self-regulating his/her own brain activity, demonstrated that down-regulating alpha over primary motor cortex by NFT could immediately facilitate the implicit motor learning in a relatively simple motor task. However, detailed effects on EEG and implicit motor learning due to NFT especially in a more complex motor task are still unclear. APPROACH We designed a single-blind sham-controlled between-subject study to examine whether alpha down-regulation NFT could facilitate implicit motor learning and also its consolidation in a more difficult and motor predominant task. At left primary motor cortex (C3) in two days, the alpha NFT group received alpha down-regulation training through auditory feedback while the sham-control group received random beta NFT. At the end of NFT, all participants performed the continuous tracking task with their dominant (right) hand to evaluate the implicit motor learning immediately. Finally, the continuous tracking task was performed again on the next day to assess consolidation effects. MAIN RESULTS The alpha NFT group successfully decreased alpha amplitude during NFT, whereas the sham-control group maintained alpha at a relatively stable level. There was unfortunately no statistical evidence proving that the alpha NFT group significantly enhanced the implicit motor learning at the end of NFT and the consolidation on the next day compared to the sham-control group. Nevertheless, a significant correlation was found between the alpha change trend during NFT and the implicit motor learning for all participants, suggesting that faster alpha down-regulation was associated with better implicit motor learning. SIGNIFICANCE The findings suggested a close link between implicit motor learning and alpha change induced by NFT.
Collapse
Affiliation(s)
- Wenya Nan
- Department of Psychology, Shanghai Normal University, Shanghai, People's Republic of China. Department of Electrical and Computer Engineering, University of Macau, Macau. Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau
| | | | | | | | | |
Collapse
|
14
|
Shiyam Sundar LK, Baajour S, Beyer T, Lanzenberger R, Traub-Weidinger T, Rausch I, Pataraia E, Hahn A, Rischka L, Hienert M, Klebermass EM, Muzik O. Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate. Front Neurosci 2020; 14:252. [PMID: 32269510 PMCID: PMC7111429 DOI: 10.3389/fnins.2020.00252] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 03/06/2020] [Indexed: 01/06/2023] Open
Abstract
In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity. METHODS We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated. RESULTS The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks. DISCUSSION Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.
Collapse
Affiliation(s)
- Lalith Kumar Shiyam Sundar
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Shahira Baajour
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Marius Hienert
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Eva-Maria Klebermass
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Otto Muzik
- Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, United States
| |
Collapse
|
15
|
Fede SJ, Dean SF, Manuweera T, Momenan R. A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review. Front Hum Neurosci 2020; 14:60. [PMID: 32161529 PMCID: PMC7052377 DOI: 10.3389/fnhum.2020.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/07/2020] [Indexed: 11/26/2022] Open
Abstract
Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.
Collapse
Affiliation(s)
| | | | | | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
16
|
Lioi G, Butet S, Fleury M, Bannier E, Lécuyer A, Bonan I, Barillot C. A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients. Front Hum Neurosci 2020; 14:37. [PMID: 32132910 PMCID: PMC7040168 DOI: 10.3389/fnhum.2020.00037] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 01/27/2020] [Indexed: 01/08/2023] Open
Abstract
Traditional rehabilitation techniques present limitations and the majority of patients show poor 1-year post-stroke recovery. Thus, Neurofeedback (NF) or Brain-Computer-Interface applications for stroke rehabilitation purposes are gaining increased attention. Indeed, NF has the potential to enhance volitional control of targeted cortical areas and thus impact on motor function recovery. However, current implementations are limited by temporal, spatial or practical constraints of the specific imaging modality used. In this pilot work and for the first time in literature, we applied bimodal EEG-fMRI NF for upper limb stroke recovery on four stroke-patients with different stroke characteristics and motor impairment severity. We also propose a novel, multi-target training approach that guides the training towards the activation of the ipsilesional primary motor cortex. In addition to fMRI and EEG outcomes, we assess the integrity of the corticospinal tract (CST) with tractography. Preliminary results suggest the feasibility of our approach and show its potential to induce an augmented activation of ipsilesional motor areas, depending on the severity of the stroke deficit. Only the two patients with a preserved CST and subcortical lesions succeeded in upregulating the ipsilesional primary motor cortex and exhibited a functional improvement of upper limb motricity. These findings highlight the importance of taking into account the variability of the stroke patients' population and enabled to identify inclusion criteria for the design of future clinical studies.
Collapse
Affiliation(s)
- Giulia Lioi
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France
| | - Simon Butet
- Departement of Physical and Rehabilitation Medicine, Centre Hospitalier Universitaire (CHU) Rennes, Rennes, France
| | - Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France
| | - Elise Bannier
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France
- Departement of Radiology, CHU Rennes, Rennes, France
| | | | - Isabelle Bonan
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France
- Departement of Physical and Rehabilitation Medicine, Centre Hospitalier Universitaire (CHU) Rennes, Rennes, France
| | | |
Collapse
|
17
|
Abstract
Brain-computer interfaces (BCIs) based on functional magnetic resonance imaging (fMRI) provide an important complement to other noninvasive BCIs. While fMRI has several disadvantages (being nonportable, methodologically challenging, costly, and noisy), it is the only method providing high spatial resolution whole-brain coverage of brain activation. These properties allow relating mental activities to specific brain regions and networks providing a transparent scheme for BCI users to encode information and for real-time fMRI BCI systems to decode the intents of the user. Various mental activities have been used successfully in fMRI BCIs so far that can be classified into the four categories: (a) higher-order cognitive tasks (e.g., mental calculation), (b) covert language-related tasks (e.g., mental speech and mental singing), (c) imagery tasks (motor, visual, auditory, tactile, and emotion imagery), and (d) selective attention tasks (visual, auditory, and tactile attention). While the ultimate spatial and temporal resolution of fMRI BCIs is limited by the physiologic properties of the hemodynamic response, technical and analytical advances will likely lead to substantially improved fMRI BCIs in the future using, for example, decoding of imagined letter shapes at 7T as the basis for more "natural" communication BCIs.
Collapse
Affiliation(s)
- Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (M-BIC), Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (M-BIC), Maastricht, The Netherlands.
| |
Collapse
|
18
|
Kaas A, Goebel R, Valente G, Sorger B. Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing. Front Hum Neurosci 2019; 13:427. [PMID: 31920588 PMCID: PMC6915074 DOI: 10.3389/fnhum.2019.00427] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 11/18/2019] [Indexed: 11/23/2022] Open
Abstract
Real-time functional magnetic resonance imaging (fMRI) is a promising non-invasive method for brain-computer interfaces (BCIs). BCIs translate brain activity into signals that allow communication with the outside world. Visual and motor imagery are often used as information-encoding strategies, but can be challenging if not grounded in recent experience in these modalities, e.g., in patients with locked-in-syndrome (LIS). In contrast, somatosensory imagery might constitute a more suitable information-encoding strategy as the somatosensory function is often very robust. Somatosensory imagery has been shown to activate the somatotopic cortex, but it has been unclear so far whether it can be reliably detected on a single-trial level and successfully classified according to specific somatosensory imagery content. Using ultra-high field 7-T fMRI, we show reliable and high-accuracy single-trial decoding of left-foot (LF) vs. right-hand (RH) somatosensory imagery. Correspondingly, higher decoding accuracies were associated with greater spatial separation of hand and foot decoding-weight patterns in the primary somatosensory cortex (S1). Exploiting these novel neuroscientific insights, we developed-and provide a proof of concept for-basic BCI communication by showing that binary (yes/no) answers encoded by somatosensory imagery can be decoded with high accuracy in simulated real-time (in 7 subjects) as well as in real-time (1 subject). This study demonstrates that body part-specific somatosensory imagery differentially activates somatosensory cortex in a topographically specific manner; evidence which was surprisingly still lacking in the literature. It also offers proof of concept for a novel somatosensory imagery-based fMRI-BCI control strategy, with particularly high potential for visually and motor-impaired patients. The strategy could also be transferred to lower MRI field strengths and to mobile functional near-infrared spectroscopy. Finally, given that communication BCIs provide the BCI user with a form of feedback based on their brain signals and can thus be considered as a specific form of neurofeedback, and that repeated use of a BCI has been shown to enhance underlying representations, we expect that the current BCI could also offer an interesting new approach for somatosensory rehabilitation training in the context of stroke and phantom limb pain.
Collapse
Affiliation(s)
- Amanda Kaas
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Giancarlo Valente
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
19
|
Sreedharan S, Arun KM, Sylaja PN, Kesavadas C, Sitaram R. Functional Connectivity of Language Regions of Stroke Patients with Expressive Aphasia During Real-Time Functional Magnetic Resonance Imaging Based Neurofeedback. Brain Connect 2019; 9:613-626. [PMID: 31353935 PMCID: PMC6798872 DOI: 10.1089/brain.2019.0674] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Stroke lesions in the language centers of the brain impair the language areas and their connectivity. This article describes the dynamics of functional connectivity (FC) of language areas (FCL) during real-time functional magnetic resonance imaging (RT-fMRI)-based neurofeedback training for poststroke patients with expressive aphasia. The hypothesis is that FCL increases during the upregulation of language areas during neurofeedback training and that the training improves FCL with an increasing number of sessions and restores it toward normalcy. Four test and four control patients with expressive aphasia were recruited for the study along with four healthy volunteers termed as the normal group. The test and normal groups were administered four neurofeedback training sessions in between two test sessions, whereas the control group underwent only the two test sessions. The training session requires the subject to exercise language activity covertly so that it upregulates the feedback signal obtained from the Broca's area (in left inferior frontal gyrus) and amplifies the feedback when it is correlated with the Wernicke's area (in left superior temporal gyrus) using RT-fMRI. FC was measured by Pearson's correlation coefficient. The results indicate that the FC of the test group was weaker in the left hemisphere than that of the normal group, and post-training the connections have strengthened (correlation coefficient increases) in the left hemisphere when compared with the control group. The connections of language areas strengthened in both hemispheres during neurofeedback-based upregulation, and multiple training sessions strengthened new pathways and restored left hemispheric connections toward normalcy.
Collapse
Affiliation(s)
- Sujesh Sreedharan
- Division of Artificial Internal Organs, Department of Medical Devices Engineering, Biomedical Technology Wing, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - K M Arun
- Department of Imaging Sciences and Intervention Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - P N Sylaja
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Intervention Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - Ranganatha Sitaram
- Institute for Biological and Medical Engineering, Center for Brain-Machine Interfaces and Neuromodulation, and Department of Psychiatry and Division of Neuroscience, Faculties of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
20
|
Self-regulation of language areas using real-time functional MRI in stroke patients with expressive aphasia. Brain Imaging Behav 2019; 14:1714-1730. [DOI: 10.1007/s11682-019-00106-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
|
21
|
Skottnik L, Sorger B, Kamp T, Linden D, Goebel R. Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum. Brain Behav 2019; 9:e01240. [PMID: 30790474 PMCID: PMC6422826 DOI: 10.1002/brb3.1240] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/24/2019] [Accepted: 01/25/2019] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Over the last decades, neurofeedback has been applied in variety of research contexts and therapeutic interventions. Despite this extensive use, its neural mechanisms are still under debate. Several scientific advances have suggested that different networks become jointly active during neurofeedback, including regions generally involved in self-regulation, regions related to the specific mental task driving the neurofeedback and regions generally involved in feedback learning (Sitaram et al., 2017, Nature Reviews Neuroscience, 18, 86). METHODS To investigate the neural mechanisms specific to neurofeedback but independent from general effects of self-regulation, we compared brain activation as measured with functional magnetic resonance imaging (fMRI) across different mental tasks involving gradual self-regulation with and without providing neurofeedback. Ten participants freely chose one self-regulation task and underwent two training sessions during fMRI scanning, one with and one without receiving neurofeedback. During neurofeedback sessions, feedback signals were provided in real-time based on activity in task-related, individually defined target regions. In both sessions, participants aimed at reaching and holding low, medium, or high brain-activation levels in the target region. RESULTS During gradual self-regulation with neurofeedback, a network of cortical control regions as well as regions implicated in reward and feedback processing were activated. Self-regulation with feedback was accompanied by stronger activation within the striatum across different mental tasks. Additional time-resolved single-trial analysis revealed that neurofeedback performance was positively correlated with a delayed brain response in the striatum that reflected the accuracy of self-regulation. CONCLUSION Overall, these findings support that neurofeedback contributes to self-regulation through task-general regions involved in feedback and reward processing.
Collapse
Affiliation(s)
- Leon Skottnik
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, Netherlands.,Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.,Brain Innovation BV, Maastricht, Netherlands
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Tabea Kamp
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - David Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom.,School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.,Brain Innovation BV, Maastricht, Netherlands.,Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, Netherlands
| |
Collapse
|
22
|
Sorger B, Scharnowski F, Linden DEJ, Hampson M, Young KD. Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies. Neuroimage 2019; 186:256-265. [PMID: 30423429 PMCID: PMC6338498 DOI: 10.1016/j.neuroimage.2018.11.004] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 10/31/2018] [Accepted: 11/05/2018] [Indexed: 12/31/2022] Open
Abstract
fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factors.
Collapse
Affiliation(s)
- Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Zürich, Switzerland
| | - David E J Linden
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Psychiatry and the Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Kymberly D Young
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
| |
Collapse
|
23
|
Valente G, Kaas AL, Formisano E, Goebel R. Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs. Neuroimage 2019; 186:369-381. [DOI: 10.1016/j.neuroimage.2018.10.080] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 09/21/2018] [Accepted: 10/30/2018] [Indexed: 11/25/2022] Open
|
24
|
Mehler DMA, Williams AN, Krause F, Lührs M, Wise RG, Turner DL, Linden DEJ, Whittaker JR. The BOLD response in primary motor cortex and supplementary motor area during kinesthetic motor imagery based graded fMRI neurofeedback. Neuroimage 2019; 184:36-44. [PMID: 30205210 PMCID: PMC6264383 DOI: 10.1016/j.neuroimage.2018.09.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 08/02/2018] [Accepted: 09/04/2018] [Indexed: 01/28/2023] Open
Abstract
There is increasing interest in exploring the use of functional MRI neurofeedback (fMRI-NF) as a therapeutic technique for a range of neurological conditions such as stroke and Parkinson's disease (PD). One main therapeutic potential of fMRI-NF is to enhance volitional control of damaged or dysfunctional neural nodes and networks via a closed-loop feedback model using mental imagery as the catalyst of self-regulation. The choice of target node/network and direction of regulation (increase or decrease activity) are central design considerations in fMRI-NF studies. Whilst it remains unclear whether the primary motor cortex (M1) can be activated during motor imagery, the supplementary motor area (SMA) has been robustly activated during motor imagery. Such differences in the regulation potential between primary and supplementary motor cortex are important because these areas can be differentially affected by a stroke or PD, and the choice of fMRI-NF target and grade of self-regulation of activity likely have substantial influence on the clinical effects and cost effectiveness of NF-based interventions. In this study we therefore investigated firstly whether healthy subjects would be able to achieve self-regulation of the hand-representation areas of M1 and the SMA using fMRI-NF training. There was a significant decrease in M1 neural activity during fMRI-NF, whereas SMA neural activity was increased, albeit not with the predicated graded effect. This study has important implications for fMRI-NF protocols that employ motor imagery to modulate activity in specific target regions of the brain and to determine how they may be tailored for neurorehabilitation.
Collapse
Affiliation(s)
- David M A Mehler
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
| | - Angharad N Williams
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
| | - Florian Krause
- Donders Institute for Brain, Cognition and Behaviour Radboud University Medical Center, 6500 HB, Nijmegen, The Netherlands
| | - Michael Lührs
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands
| | - Richard G Wise
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
| | - Duncan L Turner
- Neurorehabilitation Unit, School of Health, Sport and Bioscience, University of East London, London, E15 4LZ, United Kingdom
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Joseph R Whittaker
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom; School of Physics and Astronomy, Cardiff University, Cardiff, CF24 3AA, United Kingdom.
| |
Collapse
|
25
|
Hong KS, Zafar A. Existence of Initial Dip for BCI: An Illusion or Reality. Front Neurorobot 2018; 12:69. [PMID: 30416440 PMCID: PMC6212489 DOI: 10.3389/fnbot.2018.00069] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 10/03/2018] [Indexed: 01/21/2023] Open
Abstract
A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.
Collapse
Affiliation(s)
- Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
| | - Amad Zafar
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| |
Collapse
|
26
|
Riemenschneider B, LeVan P, Hennig J. Targeted partial reconstruction for real-time fMRI with arbitrary trajectories. Magn Reson Med 2018; 81:1118-1129. [PMID: 30230016 DOI: 10.1002/mrm.27478] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 06/19/2018] [Accepted: 07/11/2018] [Indexed: 11/10/2022]
Abstract
PURPOSE A partial image reconstruction formalism is introduced for the targeted extraction of real-time feedback from arbitrary trajectories when full image reconstruction in real time is computationally too demanding. METHODS Explicit calculation and storage of linear combinations of lines of the reconstruction matrix by an incomplete basis change in spatial coordinates lead to translation of the expensive full reconstruction from a frame-wise application to a region of interest (ROI)-wise application. This step is independent from signal data and can be executed before the experiment. Subsequently, the results of the sum over fully reconstructed voxels can be evaluated directly. Data from a high-speed fMRI acquisition was used to investigate the targeted partial reconstruction of a functional ROI atlas, incorporating an intravolume dephasing correction. The same data and ROIs were used for a comparison of the time series obtained with those obtained from already existing methods for compartment-wise reconstruction. To examine real-time feasibility, the reconstruction was implemented and tested for online reconstruction performance. RESULTS The reconstruction yields results that are virtually identical to the standard reconstruction (i.e., the magnitude sums over the ROIs), with negligible discrepancies even after termination of the conjugate gradient algorithm at a feasible number of iterations. Notably, more discrepancies arise with existing compartment-wise reconstructions. The online real-time implementation evaluated 1 ROI within 2.8 ms in the case of a highly parallel 3D whole brain acquisition. CONCLUSION The high reconstruction fidelity and speed are satisfying for the exemplary application of real-time functional feedback using a highly parallel 3D whole brain acquisition.
Collapse
Affiliation(s)
- Bruno Riemenschneider
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Germany
| |
Collapse
|
27
|
|
28
|
Bassett DS, Khambhati AN. A network engineering perspective on probing and perturbing cognition with neurofeedback. Ann N Y Acad Sci 2017; 1396:126-143. [PMID: 28445589 PMCID: PMC5446287 DOI: 10.1111/nyas.13338] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition.
Collapse
Affiliation(s)
- Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Ankit N. Khambhati
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
| |
Collapse
|
29
|
Krause F, Benjamins C, Lührs M, Eck J, Noirhomme Q, Rosenke M, Brunheim S, Sorger B, Goebel R. Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations. BRAIN-COMPUTER INTERFACES 2017. [DOI: 10.1080/2326263x.2017.1307096] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Florian Krause
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
| | - Caroline Benjamins
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
| | - Judith Eck
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
| | - Quentin Noirhomme
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
| | - Mona Rosenke
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Sascha Brunheim
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Research and Development, Brain Innovation B.V., Maastricht, The Netherlands
- Department of Neuroimaging and Neuromodeling, Royal Netherlands Academy of Arts and Sciences (KNAW), Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| |
Collapse
|
30
|
Zilverstand A, Sorger B, Slaats-Willemse D, Kan CC, Goebel R, Buitelaar JK. fMRI Neurofeedback Training for Increasing Anterior Cingulate Cortex Activation in Adult Attention Deficit Hyperactivity Disorder. An Exploratory Randomized, Single-Blinded Study. PLoS One 2017; 12:e0170795. [PMID: 28125735 PMCID: PMC5270326 DOI: 10.1371/journal.pone.0170795] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 01/10/2017] [Indexed: 11/19/2022] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is characterized by poor cognitive control/attention and hypofunctioning of the dorsal anterior cingulate cortex (dACC). In the current study, we investigated for the first time whether real-time fMRI neurofeedback (rt-fMRI) training targeted at increasing activation levels within dACC in adults with ADHD leads to a reduction of clinical symptoms and improved cognitive functioning. An exploratory randomized controlled treatment study with blinding of the participants was conducted. Participants with ADHD (n = 7 in the neurofeedback group, and n = 6 in the control group) attended four weekly MRI training sessions (60-min training time/session), during which they performed a mental calculation task at varying levels of difficulty, in order to learn how to up-regulate dACC activation. Only neurofeedback participants received continuous feedback information on actual brain activation levels within dACC. Before and after the training, ADHD symptoms and relevant cognitive functioning was assessed. Results showed that both groups achieved a significant increase in dACC activation levels over sessions. While there was no significant difference between the neurofeedback and control group in clinical outcome, neurofeedback participants showed stronger improvement on cognitive functioning. The current study demonstrates the general feasibility of the suggested rt-fMRI neurofeedback training approach as a potential novel treatment option for ADHD patients. Due to the study’s small sample size, potential clinical benefits need to be further investigated in future studies. Trial Registration: ISRCTN12390961
Collapse
Affiliation(s)
- Anna Zilverstand
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- * E-mail:
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Dorine Slaats-Willemse
- Karakter University Centre for Child and Adolescent Psychiatry, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Cornelis C. Kan
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Jan K. Buitelaar
- Karakter University Centre for Child and Adolescent Psychiatry, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| |
Collapse
|