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Cortese A, Kawato M. The cognitive reality monitoring network and theories of consciousness. Neurosci Res 2024; 201:31-38. [PMID: 38316366 DOI: 10.1016/j.neures.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 02/07/2024]
Abstract
Theories of consciousness abound. However, it is difficult to arbitrate reliably among competing theories because they target different levels of neural and cognitive processing or anatomical loci, and only some were developed with computational models in mind. In particular, theories of consciousness need to fully address the three levels of understanding of the brain proposed by David Marr: computational theory, algorithms and hardware. Most major theories refer to only one or two levels, often indirectly. The cognitive reality monitoring network (CRMN) model is derived from computational theories of mixture-of-experts architecture, hierarchical reinforcement learning and generative/inference computing modules, addressing all three levels of understanding. A central feature of the CRMN is the mapping of a gating network onto the prefrontal cortex, making it a prime coding circuit involved in monitoring the accuracy of one's mental states and distinguishing them from external reality. Because the CRMN builds on the hierarchical and layer structure of the cerebral cortex, it may connect research and findings across species, further enabling concrete computational models of consciousness with new, explicitly testable hypotheses. In sum, we discuss how the CRMN model can help further our understanding of the nature and function of consciousness.
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Affiliation(s)
- Aurelio Cortese
- Computational Neuroscience Labs, ATR Institute International, Kyoto 619-0228, Japan.
| | - Mitsuo Kawato
- Computational Neuroscience Labs, ATR Institute International, Kyoto 619-0228, Japan; XNef, Kyoto 619-0288, Japan.
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Margolles P, Elosegi P, Mei N, Soto D. Unconscious Manipulation of Conceptual Representations with Decoded Neurofeedback Impacts Search Behavior. J Neurosci 2024; 44:e1235232023. [PMID: 37985180 PMCID: PMC10866193 DOI: 10.1523/jneurosci.1235-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/04/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
The necessity of conscious awareness in human learning has been a long-standing topic in psychology and neuroscience. Previous research on non-conscious associative learning is limited by the low signal-to-noise ratio of the subliminal stimulus, and the evidence remains controversial, including failures to replicate. Using functional MRI decoded neurofeedback, we guided participants from both sexes to generate neural patterns akin to those observed when visually perceiving real-world entities (e.g., dogs). Importantly, participants remained unaware of the actual content represented by these patterns. We utilized an associative DecNef approach to imbue perceptual meaning (e.g., dogs) into Japanese hiragana characters that held no inherent meaning for our participants, bypassing a conscious link between the characters and the dogs concept. Despite their lack of awareness regarding the neurofeedback objective, participants successfully learned to activate the target perceptual representations in the bilateral fusiform. The behavioral significance of our training was evaluated in a visual search task. DecNef and control participants searched for dogs or scissors targets that were pre-cued by the hiragana used during DecNef training or by a control hiragana. The DecNef hiragana did not prime search for its associated target but, strikingly, participants were impaired at searching for the targeted perceptual category. Hence, conscious awareness may function to support higher-order associative learning. Meanwhile, lower-level forms of re-learning, modification, or plasticity in existing neural representations can occur unconsciously, with behavioral consequences outside the original training context. The work also provides an account of DecNef effects in terms of neural representational drift.
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Affiliation(s)
- Pedro Margolles
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
- Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Leioa, Bizkaia 48940, Spain
| | - Patxi Elosegi
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
- Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Leioa, Bizkaia 48940, Spain
| | - Ning Mei
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
| | - David Soto
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Bizkaia 48009, Spain
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Awasthi J, Harris-Starling C, Kalvin C, Pittman B, Park H, Bloch M, Fernandez TV, Sukhodolsky DG, Hampson M. Protocol description for a randomized controlled trial of fMRI neurofeedback for tics in adolescents with Tourette Syndrome. Psychiatry Res Neuroimaging 2023; 336:111692. [PMID: 37673711 PMCID: PMC10722977 DOI: 10.1016/j.pscychresns.2023.111692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 07/20/2023] [Indexed: 09/08/2023]
Abstract
This article describes the protocol for a randomized, controlled clinical trial of a neurofeedback (NF) intervention for Tourette Syndrome (TS) and chronic tic disorder. The intervention involves using functional magnetic resonance imaging (fMRI) to provide feedback regarding activity in the supplementary motor area: participants practice controlling this brain area while using the feedback as a training signal. The previous version of this NF protocol was tested in a small study (n = 21) training adolescents with TS that yielded clinically promising results. Therefore, we plan a larger trial. Here we describe the background literature that motivated this work, the design of our original neurofeedback study protocol, and adaptations of the research study protocol for the new trial. We focus on those ideas incorporated into our protocol that may be of interest to others designing and running NF studies. For example, we highlight our approach for defining an unrelated brain region to be trained in the control group that is based on identifying a region with low functional connectivity to the target area. Consistent with a desire for transparency and open science, the new protocol is described in detail here prior to conducting the trial.
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Affiliation(s)
- Jitendra Awasthi
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
| | - Cheyenne Harris-Starling
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
| | - Carla Kalvin
- Child Study Center, Yale University School of Medicine, New Haven, CT, United States of America
| | - Brian Pittman
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States of America
| | - Haesoo Park
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
| | - Michael Bloch
- Child Study Center, Yale University School of Medicine, New Haven, CT, United States of America
| | - Thomas V Fernandez
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States of America; Child Study Center, Yale University School of Medicine, New Haven, CT, United States of America
| | - Denis G Sukhodolsky
- Child Study Center, Yale University School of Medicine, New Haven, CT, United States of America
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States of America; Child Study Center, Yale University School of Medicine, New Haven, CT, United States of America; Department of Biomedical Engineering, Yale University School of Medicine, New Haven, CT, United States of America.
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Pamplona GSP, Heldner J, Langner R, Koush Y, Michels L, Ionta S, Salmon CEG, Scharnowski F. Preliminary findings on long-term effects of fMRI neurofeedback training on functional networks involved in sustained attention. Brain Behav 2023; 13:e3217. [PMID: 37594145 PMCID: PMC10570501 DOI: 10.1002/brb3.3217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/25/2023] [Accepted: 07/29/2023] [Indexed: 08/19/2023] Open
Abstract
INTRODUCTION Neurofeedback based on functional magnetic resonance imaging allows for learning voluntary control over one's own brain activity, aiming to enhance cognition and clinical symptoms. We previously reported improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network minus the default mode network (DMN). However, the long-term brain and behavioral effects of this training have not yet been studied. In general, despite their relevance, long-term learning effects of neurofeedback training remain under-explored. METHODS Here, we complement our previously reported results by evaluating the neurofeedback training effects on functional networks involved in sustained attention and by assessing behavioral and brain measures before, after, and 2 months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs from 15 healthy individuals. RESULTS Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training increased the functional connectivity between the DMN and the occipital gyrus, which was maintained during follow-up transfer runs but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. CONCLUSION The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior in the long term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool.
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Affiliation(s)
- Gustavo Santo Pedro Pamplona
- Sensory‐Motor Laboratory (SeMoLa), Jules‐Gonin Eye Hospital/Fondation Asile des AveuglesDepartment of Ophthalmology/University of LausanneLausanneSwitzerland
- InBrain Lab, Department of PhysicsUniversity of Sao PauloRibeirao PretoBrazil
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric HospitalUniversity of ZurichZurichSwitzerland
- Rehabilitation Engineering Laboratory (RELab), Department of Health Sciences and TechnologyETH ZurichZurichSwitzerland
| | - Jennifer Heldner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric HospitalUniversity of ZurichZurichSwitzerland
| | - Robert Langner
- Institute of Systems NeuroscienceHeinrich Heine University DusseldorfDusseldorfGermany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JulichJulichGermany
| | - Yury Koush
- Department of Radiology and Biomedical Imaging, Yale School of MedicineYale UniversityNew HavenConnecticutUSA
| | - Lars Michels
- Department of NeuroradiologyUniversity Hospital ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and Swiss Federal Institute of TechnologyZurichSwitzerland
| | - Silvio Ionta
- Sensory‐Motor Laboratory (SeMoLa), Jules‐Gonin Eye Hospital/Fondation Asile des AveuglesDepartment of Ophthalmology/University of LausanneLausanneSwitzerland
| | | | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric HospitalUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and Swiss Federal Institute of TechnologyZurichSwitzerland
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of PsychologyUniversity of ViennaViennaAustria
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Tene O, Bleich Cohen M, Helpman L, Fine N, Halevy A, Goldway N, Perry D, Bary P, Aisenberg Romano G, Ben-Zion Z, Hendler T, Bloch M. Limbic self-neuromodulation as a novel treatment option for emotional dysregulation in premenstrual dysphoric disorder (PMDD); a proof-of-concept study. Psychiatry Clin Neurosci 2023; 77:550-558. [PMID: 37354437 DOI: 10.1111/pcn.13574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/07/2023] [Accepted: 06/19/2023] [Indexed: 06/26/2023]
Abstract
AIM To assess the efficacy of a novel neurofeedback (NF) method, targeting limbic activity, to treat emotional dysregulation related to premenstrual dysphoric disorder (PMDD). METHODS We applied a NF probe targeting limbic activity using a functional magnetic resonance imaging-inspired electroencephalogram model (termed Amyg-EFP-NF) in a double-blind randomized controlled trial. A frontal alpha asymmetry probe (AAS-NF), served as active control. Twenty-seven participants diagnosed with PMDD (mean age = 33.57 years, SD = 5.67) were randomly assigned to Amyg-EFP-NF or AAS-NF interventions with a 2:1 ratio, respectively. The treatment protocol consisted of 11 NF sessions through three menstrual cycles, and a follow-up assessment 3 months thereafter. The primary outcome measure was improvement in the Revised Observer Version of the Premenstrual Tension Syndrome Rating Scale (PMTS-OR). RESULTS A significant group by time effect was observed for the core symptom subscale of the PMTS-OR, with significant improvement observed at follow-up for the Amyg-EFP group compared with the AAS group [F(1, 15)=4.968, P = 0.042]. This finding was specifically robust for reduction in anger [F(1, 15) = 22.254, P < 0.001]. A significant correlation was found between learning scores and overall improvement in core symptoms (r = 0.514, P = 0.042) suggesting an association between mechanism of change and clinical improvement. CONCLUSION Our preliminary findings suggest that Amyg-EFP-NF may serve as an affordable and accessible non-invasive treatment option for emotional dysregulation in women suffering from PMDD. Our main limitations were the relatively small number of participants and the lack of a sham-NF placebo arm.
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Affiliation(s)
- Oren Tene
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Maya Bleich Cohen
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Liat Helpman
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Department of Counseling and Human Development, Faculty of Education, University of Haifa, Haifa, Israel
| | - Naomi Fine
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Anat Halevy
- Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel
| | - Noam Goldway
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Department of Psychology, New York University, New York City, New York, USA
| | - Daniella Perry
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Plia Bary
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Gabi Aisenberg Romano
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Ziv Ben-Zion
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Departments of Comparative Medicine and Psychiatry, Yale School of Medicine, Yale University, New Haven, Connecticut, USA
- Clinical Neuroscience Division, US Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Talma Hendler
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Miki Bloch
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
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Vargas G, Araya D, Sepulveda P, Rodriguez-Fernandez M, Friston KJ, Sitaram R, El-Deredy W. Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task. Front Neurosci 2023; 17:1212549. [PMID: 37650101 PMCID: PMC10465165 DOI: 10.3389/fnins.2023.1212549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/12/2023] [Indexed: 09/01/2023] Open
Abstract
Introduction Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation. Methods We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning. Results Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning. Discussion The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.
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Affiliation(s)
- Gabriela Vargas
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile
| | - David Araya
- Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile
- Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar, Chile
| | - Pradyumna Sepulveda
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Maria Rodriguez-Fernandez
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | | | - Wael El-Deredy
- Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile
- Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
- Department of Electronic Engineering, School of Engineering, Universitat de València, Valencia, Spain
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Zhang J, Raya J, Morfini F, Urban Z, Pagliaccio D, Yendiki A, Auerbach RP, Bauer CCC, Whitfield-Gabrieli S. Reducing default mode network connectivity with mindfulness-based fMRI neurofeedback: a pilot study among adolescents with affective disorder history. Mol Psychiatry 2023; 28:2540-2548. [PMID: 36991135 PMCID: PMC10611577 DOI: 10.1038/s41380-023-02032-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 03/02/2023] [Accepted: 03/10/2023] [Indexed: 03/31/2023]
Abstract
Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed mindfulness-based fMRI neurofeedback (mbNF) for adolescents that aims to reduce default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and each participant's DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks associated with the emergence and persistence of depressive symptoms during adolescence.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA.
| | - Jovicarole Raya
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Francesca Morfini
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Zoi Urban
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - David Pagliaccio
- Department of Psychiatry, Columbia University, New York, NY, 10032, USA
- New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02129, USA
| | - Randy P Auerbach
- Department of Psychiatry, Columbia University, New York, NY, 10032, USA
- New York State Psychiatric Institute, New York, NY, 10032, USA
- Division of Clinical Developmental Neuroscience, Sackler Institute, New York, NY, 10032, USA
| | - Clemens C C Bauer
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02129, USA
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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Collin SHP, van den Broek PLC, van Mourik T, Desain P, Doeller CF. Inducing a mental context for associative memory formation with real-time fMRI neurofeedback. Sci Rep 2022; 12:21226. [PMID: 36481793 PMCID: PMC9731952 DOI: 10.1038/s41598-022-25799-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.
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Affiliation(s)
- Silvy H. P. Collin
- grid.12295.3d0000 0001 0943 3265Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
| | - Philip L. C. van den Broek
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Tim van Mourik
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Peter Desain
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Christian F. Doeller
- grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.5947.f0000 0001 1516 2393Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway ,grid.9647.c0000 0004 7669 9786Institute of Psychology-Wilhelm Wundt, Leipzig University, Leipzig, Germany
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Patil AU, Madathil D, Fan YT, Tzeng OJL, Huang CM, Huang HW. Neurofeedback for the Education of Children with ADHD and Specific Learning Disorders: A Review. Brain Sci 2022; 12:brainsci12091238. [PMID: 36138974 PMCID: PMC9497239 DOI: 10.3390/brainsci12091238] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/03/2022] [Accepted: 09/07/2022] [Indexed: 12/02/2022] Open
Abstract
Neurofeedback (NF) is a type of biofeedback in which an individual’s brain activity is measured and presented to them to support self-regulation of ongoing brain oscillations and achieve specific behavioral and neurophysiological outcomes. NF training induces changes in neurophysiological circuits that are associated with behavioral changes. Recent evidence suggests that the NF technique can be used to train electrical brain activity and facilitate learning among children with learning disorders. Toward this aim, this review first presents a generalized model for NF systems, and then studies involving NF training for children with disorders such as dyslexia, attention-deficit/hyperactivity disorder (ADHD), and other specific learning disorders such as dyscalculia and dysgraphia are reviewed. The discussion elaborates on the potential for translational applications of NF in educational and learning settings with details. This review also addresses some issues concerning the role of NF in education, and it concludes with some solutions and future directions. In order to provide the best learning environment for children with ADHD and other learning disorders, it is critical to better understand the role of NF in educational settings. The review provides the potential challenges of the current systems to aid in highlighting the issues undermining the efficacy of current systems and identifying solutions to address them. The review focuses on the use of NF technology in education for the development of adaptive teaching methods and the best learning environment for children with learning disabilities.
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Affiliation(s)
- Abhishek Uday Patil
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| | - Deepa Madathil
- Jindal Institute of Behavioural Sciences, O.P. Jindal Global University, Haryana 131001, India
| | - Yang-Tang Fan
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan 320315, Taiwan
| | - Ovid J. L. Tzeng
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Centre for Intelligent Drug Systems and Smart Bio-Devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- College of Humanities and Social Sciences, Taipei Medical University, Taipei 106339, Taiwan
- Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei 106308, Taiwan
- Hong Kong Institute for Advanced Studies, City University of Hong Kong, Hong Kong
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Centre for Intelligent Drug Systems and Smart Bio-Devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| | - Hsu-Wen Huang
- Department of Linguistics and Translation, City University of Hong Kong, Hong Kong
- Correspondence: ; Tel.: +852-3442-2579
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10
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Loriette C, Amengual JL, Ben Hamed S. Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior. Front Neurosci 2022; 16:811736. [PMID: 36161174 PMCID: PMC9492914 DOI: 10.3389/fnins.2022.811736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.
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Affiliation(s)
- Célia Loriette
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon 1, Bron, France
| | | | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon 1, Bron, France
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11
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Kvamme TL, Sarmanlu M, Overgaard M. Doubting the double-blind: Introducing a questionnaire for awareness of experimental purposes in neurofeedback studies. Conscious Cogn 2022; 104:103381. [PMID: 35947940 DOI: 10.1016/j.concog.2022.103381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/29/2022]
Abstract
Double-blinding subjects to the experiment's purpose is an important standard in neurofeedback studies. However, it is difficult to provide evidence that humans are entirely unaware of certain information. This study used insights from consciousness studies and neurophenomenology to develop a contingency awareness questionnaire for neurofeedback. We assessed whether participants had an awareness of experimental purposes to manipulate their attention and multisensory perception. A subset of subjects (5 out of 20) gained a degree of awareness of experimental purposes as evidenced by their correct guess about the purposes of the experiment to affect their attention and multisensory perceptions specific to their double-blinded group assignment. The results warrant replication before they are applied to clinical neurofeedback studies, given the considerable time taken to perform the questionnaire (∼25 min). We discuss the strengths and limitations of our contingency awareness questionnaire and the growing appeal of the double-blinded standard in clinical neurofeedback studies.
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Affiliation(s)
- Timo L Kvamme
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Aarhus, Denmark; Centre for Alcohol and Drug Research, Aarhus University, Aarhus, Denmark.
| | - Mesud Sarmanlu
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Aarhus, Denmark
| | - Morten Overgaard
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Aarhus, Denmark
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12
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Kvamme TL, Ros T, Overgaard M. Can neurofeedback provide evidence of direct brain-behavior causality? Neuroimage 2022; 258:119400. [PMID: 35728786 DOI: 10.1016/j.neuroimage.2022.119400] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 01/01/2023] Open
Abstract
Neurofeedback is a procedure that measures brain activity in real-time and presents it as feedback to an individual, thus allowing them to self-regulate brain activity with effects on cognitive processes inferred from behavior. One common argument is that neurofeedback studies can reveal how the measured brain activity causes a particular cognitive process. The causal claim is often made regarding the measured brain activity being manipulated as an independent variable, similar to brain stimulation studies. However, this causal inference is vulnerable to the argument that other upstream brain activities change concurrently and cause changes in the brain activity from which feedback is derived. In this paper, we outline the inference that neurofeedback may causally affect cognition by indirect means. We further argue that researchers should remain open to the idea that the trained brain activity could be part of a "causal network" that collectively affects cognition rather than being necessarily causally primary. This particular inference may provide a better translation of evidence from neurofeedback studies to the rest of neuroscience. We argue that the recent advent of multivariate pattern analysis, when combined with implicit neurofeedback, currently comprises the strongest case for causality. Our perspective is that although the burden of inferring direct causality is difficult, it may be triangulated using a collection of various methods in neuroscience. Finally, we argue that the neurofeedback methodology provides unique advantages compared to other methods for revealing changes in the brain and cognitive processes but that researchers should remain mindful of indirect causal effects.
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Affiliation(s)
- Timo L Kvamme
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Universitetsbyen 3, Aarhus, Denmark; Centre for Alcohol and Drug Research (CRF), Aarhus University, Aarhus, Denmark.
| | - Tomas Ros
- Departments of Neuroscience and Psychiatry, University of Geneva, Campus Biotech, Geneva, Switzerland
| | - Morten Overgaard
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Universitetsbyen 3, Aarhus, Denmark
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13
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Renton AI, Painter DR, Mattingley JB. Optimising the classification of feature-based attention in frequency-tagged electroencephalography data. Sci Data 2022; 9:296. [PMID: 35697741 PMCID: PMC9192640 DOI: 10.1038/s41597-022-01398-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/17/2022] [Indexed: 11/09/2022] Open
Abstract
Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (feature-based attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). These signals are reliably present in single-trial electroencephalography (EEG) data, are largely resilient to common EEG artifacts, and allow separation of neural responses to numerous concurrently presented visual stimuli. To date, efforts to use single-trial SSVEPs to classify visual attention for BCI control have largely focused on spatial attention rather than feature-based attention. Here, we present a dataset that allows for the development and benchmarking of algorithms to classify feature-based attention using single-trial EEG data. The dataset includes EEG and behavioural responses from 30 healthy human participants who performed a feature-based motion discrimination task on frequency tagged visual stimuli.
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Affiliation(s)
- Angela I Renton
- The University of Queensland, Queensland Brain Institute, St Lucia, 4072, Australia.
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia, Australia.
| | - David R Painter
- The University of Queensland, Queensland Brain Institute, St Lucia, 4072, Australia
| | - Jason B Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia, 4072, Australia
- The University of Queensland, School of Psychology, St Lucia, 4072, Australia
- Canadian Institute for Advanced Research (CIFAR), Toronto, Canada
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14
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Ramot M, Martin A. Closed-loop neuromodulation for studying spontaneous activity and causality. Trends Cogn Sci 2022; 26:290-299. [PMID: 35210175 PMCID: PMC9396631 DOI: 10.1016/j.tics.2022.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 01/01/2023]
Abstract
Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.
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15
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Taschereau-Dumouchel V, Cushing C, Lau H. Real-Time Functional MRI in the Treatment of Mental Health Disorders. Annu Rev Clin Psychol 2022; 18:125-154. [DOI: 10.1146/annurev-clinpsy-072220-014550] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Vincent Taschereau-Dumouchel
- Department of Psychiatry and Addictology, Université de Montréal, Montréal, Québec, Canada
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Québec, Canada
| | - Cody Cushing
- Department of Psychology, University of California, Los Angeles, California, USA
| | - Hakwan Lau
- RIKEN Center for Brain Science, Wakoshi, Saitama, Japan
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16
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Alba G, Terrasa JL, Vila J, Montoya P, Muñoz MA. EEG-heart rate connectivity changes after sensorimotor rhythm neurofeedback training: Ancillary study. Neurophysiol Clin 2021; 52:58-68. [PMID: 34906429 DOI: 10.1016/j.neucli.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES Neurofeedback can induce long-term changes in brain functional connectivity, but its influence on the connectivity between different physiological systems is unknown. The present paper is an ancillary study of a previous paper that confirmed the effect of neurofeedback on brain connectivity associated with chronic pain. We analysed the influence of neurofeedback on the connectivity between the electroencephalograph (EEG) and heart rate (HR). METHODS Seventeen patients diagnosed with fibromyalgia were divided into three groups: good sensorimotor rhythm (SMR) training responders (n = 4), bad SMR responders (n = 5) and fake training (SHAM, n = 8). Training consisted of six sessions in which participants learned to synchronize and desynchronize SMR power. Before the first training (pre-resting state) and sixth training (post-resting state) session, open-eye resting-state EEG and electrocardiograph signals were recorded. RESULTS Good responders reduced pain ratings after SMR neurofeedback training. This improvement in fibromyalgia symptoms was associated with a reduction of the connectivity between the central area and HR, between central and frontal areas, within the central area itself, and between central and occipital areas. The sham group and poor responders experienced no changes in their fibromyalgia symptoms. CONCLUSIONS Our results provide new evidence that neurofeedback is a promising tool that can be used to treat of chronic pain syndromes and to obtain a better understanding of the interactions between physiological networks. These findings are preliminary, but they may pave the way for future studies that are more methodologically robust. In addition, new research questions are raised: what is the role of the central-peripheral network in chronic pain and what is the effect of neurofeedback on this network.
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Affiliation(s)
- Guzmán Alba
- Brain, Mind and Behavior Research Center at University of Granada (CIMCYC-UGR), Spain
| | - Juan L Terrasa
- Research Institute of Health Sciences (IUNICS), University of Balearic Islands, Palma, Spain
| | - Jaime Vila
- Brain, Mind and Behavior Research Center at University of Granada (CIMCYC-UGR), Spain
| | - Pedro Montoya
- Research Institute of Health Sciences (IUNICS), University of Balearic Islands, Palma, Spain
| | - Miguel A Muñoz
- Brain, Mind and Behavior Research Center at University of Granada (CIMCYC-UGR), Spain.
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17
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Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks. Sci Rep 2021; 11:23363. [PMID: 34862407 PMCID: PMC8642545 DOI: 10.1038/s41598-021-02079-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/25/2021] [Indexed: 11/08/2022] Open
Abstract
Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training.
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18
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Neurofeedback for cognitive enhancement and intervention and brain plasticity. Rev Neurol (Paris) 2021; 177:1133-1144. [PMID: 34674879 DOI: 10.1016/j.neurol.2021.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/27/2021] [Indexed: 12/18/2022]
Abstract
In recent years, neurofeedback has been used as a cognitive training tool to improve brain functions for clinical or recreational purposes. It is based on providing participants with feedback about their brain activity and training them to control it, initiating directional changes. The overarching hypothesis behind this method is that this control results in an enhancement of the cognitive abilities associated with this brain activity, and triggers specific structural and functional changes in the brain, promoted by learning and neuronal plasticity effects. Here, we review the general methodological principles behind neurofeedback and we describe its behavioural benefits in clinical and experimental contexts. We review the non-specific effects of neurofeedback on the reinforcement learning striato-frontal networks as well as the more specific changes in the cortical networks on which the neurofeedback control is exerted. Last, we analyse the current challenges faces by neurofeedback studies, including the quantification of the temporal dynamics of neurofeedback effects, the generalisation of its behavioural outcomes to everyday life situations, the design of appropriate controls to disambiguate placebo from true neurofeedback effects and the development of more advanced cortical signal processing to achieve a finer-grained real-time modelling of cognitive functions.
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19
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Abstract
In several papers published in Biological Cybernetics in the 1980s and 1990s, Kawato and colleagues proposed computational models explaining how internal models are acquired in the cerebellum. These models were later supported by neurophysiological experiments using monkeys and neuroimaging experiments involving humans. These early studies influenced neuroscience from basic, sensory-motor control to higher cognitive functions. One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials. Consciousness and metacognition-the ability to monitor one's own thoughts, may be part of the solution to this enigma. Based on literature reviews of the past 20 years, here we propose a computational neuroscience model of metacognition. The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. In the prefrontal cortex, a distributed executive network called the "cognitive reality monitoring network" (CRMN) orchestrates conscious involvement of generative-inverse model pairs in perception and action. Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a "responsibility signal" that gates selection and learning of pairs in perception, action, and reinforcement learning. A high responsibility signal is given to the pairs that best capture the external world, that are competent in movements (small mismatch), and that are capable of reinforcement learning (small reward-prediction error). CRMN selects pairs with higher responsibility signals as objects of metacognition, and consciousness is determined by the entropy of responsibility signals across all pairs. This model could lead to new-generation AI, which exhibits metacognition, consciousness, dimension reduction, selection of modules and corresponding representations, and learning from small samples. It may also lead to the development of a new scientific paradigm that enables the causal study of consciousness by combining CRMN and decoded neurofeedback.
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Affiliation(s)
- Mitsuo Kawato
- ATR Brain Information Communication Research Group, Computational Neuroscience Laboratory, Hikaridai, Kyoto, 619-0288 Japan
| | - Aurelio Cortese
- ATR Brain Information Communication Research Group, Computational Neuroscience Laboratory, Hikaridai, Kyoto, 619-0288 Japan
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20
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Cortese A. Metacognitive resources for adaptive learning⋆. Neurosci Res 2021; 178:10-19. [PMID: 34534617 DOI: 10.1016/j.neures.2021.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 10/20/2022]
Abstract
Biological organisms display remarkably flexible behaviours. This is an area of active investigation, in particular in the fields of artificial intelligence, computational and cognitive neuroscience. While inductive biases and broader cognitive functions are undoubtedly important, the ability to monitor and evaluate one's performance or oneself -- metacognition -- strikes as a powerful resource for efficient learning. Often measured as decision confidence in neuroscience and psychology experiments, metacognition appears to reflect a broad range of abstraction levels and downstream behavioural effects. Within this context, the formal investigation of how metacognition interacts with learning processes is a recent endeavour. Of special interest are the neural and computational underpinnings of confidence and reinforcement learning modules. This review discusses a general hierarchy of confidence functions and their neuro-computational relevance for adaptive behaviours. It then introduces novel ways to study the formation and use of meta-representations and nonconscious mental representations related to learning and confidence, and concludes with a discussion on outstanding questions and wider perspectives.
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Affiliation(s)
- Aurelio Cortese
- Computational Neuroscience Labs, ATR Institute International, 619-0288 Kyoto, Japan.
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21
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Taschereau-Dumouchel V, Cortese A, Lau H, Kawato M. Conducting decoded neurofeedback studies. Soc Cogn Affect Neurosci 2021; 16:838-848. [PMID: 32367138 PMCID: PMC8343564 DOI: 10.1093/scan/nsaa063] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 02/13/2020] [Accepted: 04/27/2020] [Indexed: 12/20/2022] Open
Abstract
Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments. We hope that this will encourage more researchers to try out this powerful new intervention method.
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Affiliation(s)
- Vincent Taschereau-Dumouchel
- Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan
- Department of Psychology, UCLA, Los Angeles, CA 90095, USA
| | - Aurelio Cortese
- Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan
| | - Hakwan Lau
- Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan
- Department of Psychology, UCLA, Los Angeles, CA 90095, USA
- State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong
- Brain Research Institute, UCLA, Los Angeles, CA 90095, USA
- Department of Psychology, University of Hong Kong, Pokfulam, Hong Kong
| | - Mitsuo Kawato
- Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan
- RIKEN Center for Advanced Intelligence Project, ATR Institute International, Kyoto, Japan
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22
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Wang Z, Tamaki M, Frank SM, Shibata K, Worden MS, Yamada T, Kawato M, Sasaki Y, Watanabe T. Visual perceptual learning of a primitive feature in human V1/V2 as a result of unconscious processing, revealed by decoded functional MRI neurofeedback (DecNef). J Vis 2021; 21:24. [PMID: 34431964 PMCID: PMC8399321 DOI: 10.1167/jov.21.8.24] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Although numerous studies have shown that visual perceptual learning (VPL) occurs as a result of exposure to a visual feature in a task-irrelevant manner, the underlying neural mechanism is poorly understood. In a previous psychophysical study (Watanabe et al., 2002), subjects were repeatedly exposed to a task-irrelevant Sekuler motion display that induced the perception of not only the local motions, but also a global motionmoving in the direction of the spatiotemporal average of the local motion vectors. As a result of this exposure, subjects enhanced their sensitivity only to the local moving directions, suggesting that early visual areas (V1/V2) that process local motions are involved in task-irrelevant VPL. However, this hypothesis has never been tested directly using neuronal recordings. Here, we employed a decoded neurofeedback technique (DecNef) using functional magnetic resonance imaging in human subjects to examine the involvement of early visual areas (V1/V2) in task-irrelevant VPL of local motion within a Sekuler motion display. During the DecNef training, subjects were trained to induce the activity patterns in V1/V2 that were similar to those evoked by the actual presentation of the Sekuler motion display. The DecNef training was conducted with neither the actual presentation of the display nor the subjects’ awareness of the purpose of the experiment. After the experiment, subjects reported that they neither perceived nor imagined the trained motion during the DecNef training. As a result of DecNef training, subjects increased their sensitivity to the local motion directions, but not specifically to the global motion direction. Neuronal changes related to DecNef training were confined to V1/V2. These results suggest that V1/V2 are involved in exposure-based task-irrelevant VPL of local motion.
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Affiliation(s)
- Zhiyan Wang
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.,
| | - Masako Tamaki
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.,
| | - Sebastian M Frank
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.,
| | - Kazuhisa Shibata
- Riken Center for Brain Science, Wako, Saitama, Japan.,Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, Keihanna Science City, Kyoto, Japan.,
| | - Michael S Worden
- Department of Neuroscience, Brown University, Providence, RI, USA.,Carney Institute for Brain Science, Brown University, Providence, RI, USA.,
| | - Takashi Yamada
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.,
| | - Mitsuo Kawato
- Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, Keihanna Science City, Kyoto, Japan.,
| | - Yuka Sasaki
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.,Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, Keihanna Science City, Kyoto, Japan.,
| | - Takeo Watanabe
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.,Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, Keihanna Science City, Kyoto, Japan.,
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23
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Bu J, Liu C, Gou H, Gan H, Cheng Y, Liu M, Ni R, Liang Z, Cui G, Zeng GQ, Zhang X. A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction. Front Neurosci 2021; 15:647844. [PMID: 34295217 PMCID: PMC8290081 DOI: 10.3389/fnins.2021.647844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 05/27/2021] [Indexed: 11/26/2022] Open
Abstract
Compared with the traditional neurofeedback paradigm, the cognition-guided neurofeedback brain–computer interface (BCI) is a novel paradigm with significant effect on nicotine addiction. However, the cognition-guided neurofeedback BCI dataset is extremely lacking at present. This paper provides a BCI dataset based on a novel cognition-guided neurofeedback on nicotine addiction. Twenty-eight participants are recruited and involved in two visits of neurofeedback training. This cognition-guided neurofeedback includes two phases: an offline classifier construction and a real-time neurofeedback training. The original electroencephalogram (EEG) raw data of two phases are provided and evaluated in this paper. The event-related potential (ERP) amplitude and channel waveform suggest that our BCI dataset is of good quality and consistency. During neurofeedback training, the participants’ smoking cue reactivity patterns have a significant reduction. The mean accuracy of the multivariate pattern analysis (MVPA) classifier can reach approximately 70%. This novel cognition-guided neurofeedback BCI dataset can be used to develop comparisons with other neurofeedback systems and provide a reference for the development of other BCI algorithms and neurofeedback paradigms on addiction.
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Affiliation(s)
- Junjie Bu
- Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.,Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Chang Liu
- Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Huixing Gou
- Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Hefan Gan
- Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Yan Cheng
- Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
| | - Mengyuan Liu
- Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
| | - Rui Ni
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Zhen Liang
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Guanbao Cui
- Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
| | - Ginger Qinghong Zeng
- Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Xiaochu Zhang
- Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.,Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China.,Institute of Advanced Technology, University of Science and Technology of China, Hefei, China.,Hefei Medical Research Center on Alcohol Addiction, Anhui Mental Health Center, Hefei, China.,Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China
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24
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Oblak E, Lewis-Peacock J, Sulzer J. Differential neural plasticity of individual fingers revealed by fMRI neurofeedback. J Neurophysiol 2021; 125:1720-1734. [PMID: 33788634 DOI: 10.1152/jn.00509.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Previous work has shown that functional magnetic resonance imaging (fMRI) activity patterns associated with individual fingers can be shifted by temporary impairment of the hand. Here, we investigated whether these neural activity patterns could be modulated endogenously and whether any behavioral changes result from this modulation. We used decoded neurofeedback in healthy individuals to encourage participants to shift the neural activity pattern in sensorimotor cortex of the middle finger toward the index finger, and the ring finger toward the little finger. We first mapped the neural activity patterns for all fingers of the right hand in an fMRI pattern localizer session. Then, in three subsequent neurofeedback sessions, participants were rewarded after middle/ring finger presses according to their activity pattern overlap during each trial. A force-sensitive keyboard was used to ensure that participants were not altering their physical finger coordination patterns. We found evidence that participants could learn to shift the activity pattern of the ring finger but not of the middle finger. Increased variability of these activity patterns during the localizer session was associated with the ability of participants to modulate them using neurofeedback. Participants also showed an increased preference for the ring finger but not for the middle finger in a postneurofeedback motor task. Our results show that neural activity and behaviors associated with the ring finger are more readily modulated than those associated with the middle finger. These results have broader implications for rehabilitation of individual finger movements, which may be limited or enhanced by individual finger plasticity after neurological injury.NEW & NOTEWORTHY It may be possible to remobilize fingers after neurological injury by altering neural activity patterns. Toward this end, we examined whether finger-related neural activity patterns could be modified in healthy individuals without physical intervention, using fMRI neurofeedback. Our findings show that greater variability of neural patterns at baseline predicted a participant's ability to successfully shift these patterns. Because neural variability is common in individuals poststroke, this illustrates a potential clinical benefit of this procedure.
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Affiliation(s)
- Ethan Oblak
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas
| | | | - James Sulzer
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas
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Russo AG, Lührs M, Di Salle F, Esposito F, Goebel R. Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis. J Neural Eng 2021; 18. [PMID: 33684900 DOI: 10.1088/1741-2552/abecc3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/08/2021] [Indexed: 11/12/2022]
Abstract
Objective. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e.g. objects to imagine) via real-time representational similarity analysis of multi-voxel patterns of brain activity.Approach. In a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 T involving a single healthy participant imagining concrete objects. Based on this data and artificial data sets with similar (simulated) spatio-temporal structure and variable (injected) signal and noise, the dependence on noise is systematically assessed.Main results. The participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent.Significance. Our proof-of-concept study introduces a new NF method allowing the participant to navigate among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.
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Affiliation(s)
- Andrea G Russo
- Department of Political and Communication Sciences, University of Salerno, Fisciano (Salerno), Italy.,Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Michael Lührs
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Fabrizio Esposito
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy.,Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Department of Advanced Medical and Surgical Sciences,University of Campania 'Luigi Vanvitelli', Napoli,Italy
| | - Rainer Goebel
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
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26
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Saha S, Mamun KA, Ahmed K, Mostafa R, Naik GR, Darvishi S, Khandoker AH, Baumert M. Progress in Brain Computer Interface: Challenges and Opportunities. Front Syst Neurosci 2021; 15:578875. [PMID: 33716680 PMCID: PMC7947348 DOI: 10.3389/fnsys.2021.578875] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 01/06/2021] [Indexed: 12/13/2022] Open
Abstract
Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
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Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Khondaker A. Mamun
- Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Khawza Ahmed
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Raqibul Mostafa
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Sam Darvishi
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Ahsan H. Khandoker
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
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Cortese A, Tanaka SC, Amano K, Koizumi A, Lau H, Sasaki Y, Shibata K, Taschereau-Dumouchel V, Watanabe T, Kawato M. The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments. Sci Data 2021; 8:65. [PMID: 33623035 PMCID: PMC7902847 DOI: 10.1038/s41597-021-00845-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 01/29/2021] [Indexed: 12/12/2022] Open
Abstract
Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyse and investigate some of the factors enabling the manipulation of brain dynamics. We release here the data from published DecNef studies, consisting of 5 separate fMRI datasets, each with multiple sessions recorded per participant. For each participant the data consists of a session that was used in the main experiment to train the machine learning decoder, and several (from 3 to 10) closed-loop fMRI neural reinforcement sessions. The large dataset, currently comprising more than 60 participants, will be useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics. Finally, the data collection size will increase over time as data from newly run DecNef studies will be added. Measurement(s) | functional brain measurement | Technology Type(s) | 3 T MRI scanner • functional magnetic resonance imaging • Neurofeedback • machine learning | Factor Type(s) | type of task performed: visual, preference, perceptual, or memory task | Sample Characteristic - Organism | Homo sapiens |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13578008
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Affiliation(s)
- Aurelio Cortese
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.
| | - Saori C Tanaka
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan
| | - Kaoru Amano
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.,Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, 565-0871, Osaka, Japan
| | - Ai Koizumi
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.,Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, 565-0871, Osaka, Japan.,Sony Computer Science Laboratories, Inc., 141-0022, Tokyo, Japan
| | - Hakwan Lau
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.,Department of Psychology, UCLA, 90095, Los Angeles, CA, USA.,Brain Research Institute, UCLA, 90095, Los Angeles, CA, USA.,Department of Psychology, University of Hong Kong, Pok Fu Lam, Hong Kong.,State Key Laboratory for Brain and Cognitive Sciences, University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Yuka Sasaki
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.,Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 02912, Providence, RI, USA
| | - Kazuhisa Shibata
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.,RIKEN Center for Brain Science, 351-0198, Saitama, Japan
| | - Vincent Taschereau-Dumouchel
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.,Department of Psychology, UCLA, 90095, Los Angeles, CA, USA
| | - Takeo Watanabe
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan.,Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 02912, Providence, RI, USA
| | - Mitsuo Kawato
- Computational Neuroscience Labs, ATR Institute International, 619-0288, Kyoto, Japan. .,RIKEN Center for Advanced Intelligence Project, 619-0288, Kyoto, Japan.
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Gonzalez-Castillo J, Ramot M, Momenan R. Editorial: Towards Expanded Utility of Real Time fMRI Neurofeedback in Clinical Applications. Front Hum Neurosci 2020; 14:606868. [PMID: 33281590 PMCID: PMC7689151 DOI: 10.3389/fnhum.2020.606868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 10/12/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Michal Ramot
- Section on Cognitive Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.,Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute of Health (NIH), Bethesda, MD, United States
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29
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Muñoz-Moldes S, Cleeremans A. Delineating implicit and explicit processes in neurofeedback learning. Neurosci Biobehav Rev 2020; 118:681-688. [PMID: 32918947 PMCID: PMC7758707 DOI: 10.1016/j.neubiorev.2020.09.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 08/09/2020] [Accepted: 09/05/2020] [Indexed: 11/21/2022]
Abstract
Neurofeedback allows humans to self-regulate neural activity in specific brain regions and is considered a promising tool for psychiatric interventions. Recently, methods have been developed to use neurofeedback implicitly, prompting a theoretical debate on the role of awareness in neurofeedback learning. We offer a critical review of the role of awareness in neurofeedback learning, with a special focus on recently developed neurofeedback paradigms. We detail differences in instructions and propose a fine-grained categorization of tasks based on the degree of involvement of explicit and implicit processes. Finally, we review the methods used to measure awareness in neurofeedback and propose new candidate measures. We conclude that explicit processes cannot be eschewed in most current implicit tasks that have explicit goals, and suggest ways in which awareness could be better measured in the future. Investigating awareness during learning will help understand the learning mechanisms underlying neurofeedback learning and will help shape future tasks.
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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, 1050 Brussels, Belgium; Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
| | - Axel Cleeremans
- Consciousness, Cognition and Computation group, Center for Research in Cognition & Neuroscience, Faculty of Psychology and Education, Université Libre de Bruxelles, 1050 Brussels, Belgium.
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30
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Bagarinao E, Yoshida A, Terabe K, Kato S, Nakai T. Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training. Front Neurosci 2020; 14:623. [PMID: 32670011 PMCID: PMC7326956 DOI: 10.3389/fnins.2020.00623] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 05/19/2020] [Indexed: 11/24/2022] Open
Abstract
In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application.
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Affiliation(s)
| | - Akihiro Yoshida
- Neuroimaging and Informatics Group, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazunori Terabe
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Shohei Kato
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Toshiharu Nakai
- Neuroimaging and Informatics Group, National Center for Geriatrics and Gerontology, Obu, Japan.,Department of Radiology, Osaka University Graduate School of Dentistry, Osaka, Japan
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31
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Ito H, Fujiki S, Mori Y, Kansaku K. Self-reorganization of neuronal activation patterns in the cortex under brain-machine interface and neural operant conditioning. Neurosci Res 2020; 156:279-292. [DOI: 10.1016/j.neures.2020.03.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/23/2020] [Accepted: 02/29/2020] [Indexed: 10/24/2022]
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32
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Bu J, Young KD, Hong W, Ma R, Song H, Wang Y, Zhang W, Hampson M, Hendler T, Zhang X. Effect of deactivation of activity patterns related to smoking cue reactivity on nicotine addiction. Brain 2020; 142:1827-1841. [PMID: 31135053 DOI: 10.1093/brain/awz114] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/24/2019] [Accepted: 02/24/2019] [Indexed: 02/04/2023] Open
Abstract
With approximately 75% of smokers resuming cigarette smoking after using the Gold Standard Programme for smoking cessation, investigation into novel therapeutic approaches is warranted. Typically, smoking cue reactivity is crucial for smoking behaviour. Here we developed a novel closed-loop, smoking cue reactivity patterns EEG-based neurofeedback protocol and evaluated its therapeutic efficacy on nicotine addiction. During an evoked smoking cue reactivity task participants' brain activity patterns corresponding to smoking cues were obtained with multivariate pattern analysis of all EEG channels data, then during neurofeedback the EEG activity patterns of smoking cue reactivity were continuously deactivated with adaptive closed-loop training. In a double-blind, placebo-controlled, randomized clinical trial, 60 nicotine-dependent participants were assigned to receive two neurofeedback training sessions (∼1 h/session) either from their own brain (n = 30, real-feedback group) or from the brain activity pattern of a matched participant (n = 30, yoked-feedback group). Cigarette craving and craving-related P300 were assessed at pre-neurofeedback and post-neurofeedback. The number of cigarettes smoked per day was assessed at baseline, 1 week, 1 month, and 4 months following the final neurofeedback visit. In the real-feedback group, participants successfully deactivated EEG activity patterns of smoking cue reactivity. The real-feedback group showed significant decrease in cigarette craving and craving-related P300 amplitudes compared with the yoked-feedback group. The rates of cigarettes smoked per day at 1 week, 1 month and 4 months follow-up decreased 30.6%, 38.2%, and 27.4% relative to baseline in the real-feedback group, compared to decreases of 14.0%, 13.7%, and 5.9% in the yoked-feedback group. The neurofeedback effects on craving change and smoking amount at the 4-month follow-up were further predicted by neural markers at pre-neurofeedback. This novel neurofeedback training approach produced significant short-term and long-term effects on cigarette craving and smoking behaviour, suggesting the neurofeedback protocol described herein is a promising brain-based tool for treating addiction.
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Affiliation(s)
- Junjie Bu
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Kymberly D Young
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Wei Hong
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Ru Ma
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Hongwen Song
- School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
| | - Ying Wang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Wei Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Talma Hendler
- Functional Brain Center, Tel-Aviv University, Tel-Aviv, Israel
| | - Xiaochu Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.,School of Humanities and Social Science, University of Science and Technology of China, Hefei, China.,Hefei Medical Research Center on Alcohol Addiction, Anhui Mental Health Center, Hefei, China.,Academy of Psychology and Behaviour, Tianjin Normal University, Tianjin, China
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33
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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.
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Affiliation(s)
| | | | | | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States
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Hohenfeld C, Kuhn H, Müller C, Nellessen N, Ketteler S, Heinecke A, Goebel R, Shah NJ, Schulz JB, Reske M, Reetz K. Changes in brain activation related to visuo-spatial memory after real-time fMRI neurofeedback training in healthy elderly and Alzheimer's disease. Behav Brain Res 2019; 381:112435. [PMID: 31863845 DOI: 10.1016/j.bbr.2019.112435] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 12/11/2019] [Accepted: 12/13/2019] [Indexed: 12/12/2022]
Abstract
Cognitive decline is a symptom of healthy ageing and Alzheimer's disease. We examined the effect of real-time fMRI based neurofeedback training on visuo-spatial memory and its associated neuronal response. Twelve healthy subjects and nine patients of prodromal Alzheimer's disease were included. The examination spanned five days (T1-T5): T1 contained a neuropsychological pre-test, the encoding of an itinerary and a fMRI-based task related that itinerary. T2-T4 hosted the real-time fMRI neurofeedback training of the parahippocampal gyrus and on T5 a post-test session including encoding of another itinerary and a subsequent fMRI-based task were done. Scores from neuropsychological tests, brain activation and task performance during the fMRI-paradigm were compared between pre and post-test as well as between healthy controls and patients. Behavioural performance in the fMRI-task remained unchanged, while cognitive testing showed improvements in visuo-spatial memory performance. Both groups displayed task-relevant brain activation, which decreased in the right precentral gyrus and left occipital lobe from pre to post-test in controls, but increased in the right occipital lobe, middle frontal gyrus and left frontal lobe in the patient group. While results suggest that the training has affected brain activation differently between controls and patients, there are no pointers towards a behavioural manifestation of these changes. Future research is required on the effects that can be induced using real-time fMRI based neurofeedback training and the required training duration to elicit broad and lasting effects.
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Affiliation(s)
- Christian Hohenfeld
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany
| | - Hanna Kuhn
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany; University Hospital Bern, Emergency Department, Bern, Switzerland
| | - Christine Müller
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany; Bethesda Clinic, Department of Neurorehabilitation, Tschugg, Switzerland
| | - Nils Nellessen
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany; University of Cologne, Department of Neurology, Cologne, Germany
| | - Simon Ketteler
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany
| | | | - Rainer Goebel
- Netherlands Institute for Neuroscience, Department of Neuroimaging and Neuromodeling, Amsterdam, The Netherlands; Maastricht University, Department of Cognitive Neuroscience, Maastricht, The Netherlands; Brain Innovation, Maastricht, The Netherlands
| | - N Jon Shah
- Research Centre Jülich, Institute for Neuroscience and Medicine (INM-4/6), Jülich, Germany; RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany
| | - Jörg B Schulz
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany
| | - Martina Reske
- Research Centre Jülich, Institute for Neuroscience and Medicine (INM-4/6), Jülich, Germany
| | - Kathrin Reetz
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA Brain Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich and RWTH Aachen University, Aachen, Germany.
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Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements. Neuroimage 2019; 202:116107. [DOI: 10.1016/j.neuroimage.2019.116107] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/26/2019] [Accepted: 08/16/2019] [Indexed: 12/21/2022] Open
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A common probabilistic framework for perceptual and statistical learning. Curr Opin Neurobiol 2019; 58:218-228. [PMID: 31669722 DOI: 10.1016/j.conb.2019.09.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 08/24/2019] [Accepted: 09/09/2019] [Indexed: 11/20/2022]
Abstract
System-level learning of sensory information is traditionally divided into two domains: perceptual learning that focuses on acquiring knowledge suitable for fine discrimination between similar sensory inputs, and statistical learning that explores the mechanisms that develop complex representations of unfamiliar sensory experiences. The two domains have been typically treated in complete separation both in terms of the underlying computational mechanisms and the brain areas and processes implementing those computations. However, a number of recent findings in both domains call in question this strict separation. We interpret classical and more recent results in the general framework of probabilistic computation, provide a unifying view of how various aspects of the two domains are interlinked, and suggest how the probabilistic approach can also alleviate the problem of dealing with widely different types of neural correlates of learning. Finally, we outline several directions along which our proposed approach fosters new types of experiments that can promote investigations of natural learning in humans and other species.
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Chiba T, Kanazawa T, Koizumi A, Ide K, Taschereau-Dumouchel V, Boku S, Hishimoto A, Shirakawa M, Sora I, Lau H, Yoneda H, Kawato M. Current Status of Neurofeedback for Post-traumatic Stress Disorder: A Systematic Review and the Possibility of Decoded Neurofeedback. Front Hum Neurosci 2019; 13:233. [PMID: 31379538 PMCID: PMC6650780 DOI: 10.3389/fnhum.2019.00233] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 06/25/2019] [Indexed: 11/13/2022] Open
Abstract
Background: Post-traumatic stress disorder (PTSD) is a neuropsychiatric affective disorder that can develop after traumatic life-events. Exposure-based therapy is currently one of the most effective treatments for PTSD. However, exposure to traumatic stimuli is so aversive that a significant number of patients drop-out of therapy during the course of treatment. Among various attempts to develop novel therapies that bypass such aversiveness, neurofeedback appears promising. With neurofeedback, patients can unconsciously self-regulate brain activity via real-time monitoring and feedback of the EEG or fMRI signals. With conventional neurofeedback methods, however, it is difficult to induce neural representation related to specific trauma because the feedback is based on the neural signals averaged within specific brain areas. To overcome this difficulty, novel neurofeedback approaches such as Decoded Neurofeedback (DecNef) might prove helpful. Instead of the average BOLD signals, DecNef allows patients to implicitly regulate multivariate voxel patterns of the BOLD signals related with feared stimuli. As such, DecNef effects are postulated to derive either from exposure or counter-conditioning, or some combination of both. Although the exact mechanism is not yet fully understood. DecNef has been successfully applied to reduce fear responses induced either by fear-conditioned or phobic stimuli among non-clinical participants. Methods: Follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted to compare DecNef effect with those of conventional EEG/fMRI-based neurofeedback on PTSD amelioration. To elucidate the possible mechanisms of DecNef on fear reduction, we mathematically modeled the effects of exposure-based and counter conditioning separately and applied it to the data obtained from past DecNef studies. Finally, we conducted DecNef on four PTSD patients. Here, we review recent advances in application of neurofeedback to PTSD treatments, including the DecNef. This review is intended to be informative for neuroscientists in general as well as practitioners planning to use neurofeedback as a therapeutic strategy for PTSD. Results: Our mathematical model suggested that exposure is the key component for DecNef effects in the past studies. Following DecNef a significant reduction of PTSD severity was observed. This effect was comparable to those reported for conventional neurofeedback approach. Conclusions: Although a much larger number of participants will be needed in future, DecNef could be a promising therapy that bypasses the unpleasantness of conscious exposure associated with conventional therapies for fear related disorders, including PTSD.
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Affiliation(s)
- Toshinori Chiba
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Tetsufumi Kanazawa
- Department of Neuropsychiatry, Osaka Medical College, Osaka, Japan.,The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Ai Koizumi
- Sony Computer Science Laboratories, Inc., Tokyo, Japan
| | - Kentarou Ide
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Flower of Light Clinic for Mind and Body, Tokyo, Japan
| | - Vincent Taschereau-Dumouchel
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Shuken Boku
- Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Akitoyo Hishimoto
- Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | | | - Ichiro Sora
- Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Hakwan Lau
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychology, University of Hong Kong, Pokfulam, Hong Kong
| | - Hiroshi Yoneda
- Department of Neuropsychiatry, Osaka Medical College, Osaka, Japan
| | - Mitsuo Kawato
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
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Cowell RA, Barense MD, Sadil PS. A Roadmap for Understanding Memory: Decomposing Cognitive Processes into Operations and Representations. eNeuro 2019; 6:ENEURO.0122-19.2019. [PMID: 31189554 PMCID: PMC6620388 DOI: 10.1523/eneuro.0122-19.2019] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/03/2019] [Accepted: 06/03/2019] [Indexed: 11/21/2022] Open
Abstract
Thanks to patients Phineas Gage and Henry Molaison, we have long known that behavioral control depends on the frontal lobes, whereas declarative memory depends on the medial temporal lobes (MTL). For decades, cognitive functions-behavioral control, declarative memory-have served as labels for characterizing the division of labor in cortex. This approach has made enormous contributions to understanding how the brain enables the mind, providing a systems-level explanation of brain function that constrains lower-level investigations of neural mechanism. Today, the approach has evolved such that functional labels are often applied to brain networks rather than focal brain regions. Furthermore, the labels have diversified to include both broadly-defined cognitive functions (declarative memory, visual perception) and more circumscribed mental processes (recollection, familiarity, priming). We ask whether a process-a high-level mental phenomenon corresponding to an introspectively-identifiable cognitive event-is the most productive label for dissecting memory. For example, recollection conflates a neurocomputational operation (pattern completion-based retrieval) with a class of representational content (associative, high-dimensional memories). Because a full theory of memory must identify operations and representations separately, and specify how they interact, we argue that processes like recollection constitute inadequate labels for characterizing neural mechanisms. Instead, we advocate considering the component operations and representations of processes like recollection in isolation. For the organization of memory, the evidence suggests that pattern completion is recapitulated widely across the ventral visual stream and MTL, but the division of labor between sites within this pathway can be explained by representational content.
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Affiliation(s)
- Rosemary A Cowell
- Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts 01003
| | - Morgan D Barense
- Department of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada
| | - Patrick S Sadil
- Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts 01003
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Oblak EF, Sulzer JS, Lewis-Peacock JA. A simulation-based approach to improve decoded neurofeedback performance. Neuroimage 2019; 195:300-310. [DOI: 10.1016/j.neuroimage.2019.03.062] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 02/21/2019] [Accepted: 03/27/2019] [Indexed: 12/13/2022] Open
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deBettencourt MT, Turk-Browne NB, Norman KA. Neurofeedback helps to reveal a relationship between context reinstatement and memory retrieval. Neuroimage 2019; 200:292-301. [PMID: 31201985 DOI: 10.1016/j.neuroimage.2019.06.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 04/17/2019] [Accepted: 06/02/2019] [Indexed: 11/29/2022] Open
Abstract
Theories of mental context and memory posit that successful mental context reinstatement enables better retrieval of memories from the same context, at the expense of memories from other contexts. To test this hypothesis, we had participants study lists of words, interleaved with task-irrelevant images from one category (e.g., scenes). Following encoding, participants were cued to mentally reinstate the context associated with a particular list, by thinking about the images that had appeared between the words. We measured context reinstatement by applying multivariate pattern classifiers to fMRI, and related this to performance on a free recall test that followed immediately afterwards. To increase sensitivity, we used a closed-loop neurofeedback procedure, whereby higher classifier evidence for the cued category elicited increased visibility of the images from the studied context onscreen. Our goal was to create a positive feedback loop that amplified small fluctuations in mental context reinstatement, making it easier to experimentally detect a relationship between context reinstatement and recall. As predicted, we found that greater amounts of classifier evidence were associated with better recall of words from the reinstated context, and worse recall of words from a different context. In a second experiment, we assessed the role of neurofeedback in identifying this brain-behavior relationship by presenting context images again and manipulating whether their visibility depended on classifier evidence. When neurofeedback was removed, the relationship between classifier evidence and memory retrieval disappeared. Together, these findings demonstrate a clear effect of context reinstatement on memory recall and suggest that neurofeedback can be a useful tool for characterizing brain-behavior relationships.
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Affiliation(s)
- Megan T deBettencourt
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL, 60637, USA; Department of Psychology, University of Chicago, Chicago, IL, 60637, USA.
| | - Nicholas B Turk-Browne
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA; Department of Psychology, Princeton University, Princeton, NJ, 08540, USA; Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA; Department of Psychology, Princeton University, Princeton, NJ, 08540, USA
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41
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Sadil P, Potter KW, Huber DE, Cowell RA. Connecting the dots without top-down knowledge: Evidence for rapidly-learned low-level associations that are independent of object identity. J Exp Psychol Gen 2019; 148:1058-1070. [PMID: 31070394 PMCID: PMC6759832 DOI: 10.1037/xge0000607] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Knowing the identity of an object can powerfully alter perception. Visual demonstrations of this-such as Gregory's (1970) hidden Dalmatian-affirm the existence of both top-down and bottom-up processing. We consider a third processing pathway: lateral connections between the parts of an object. Lateral associations are assumed by theories of object processing and hierarchical theories of memory, but little evidence attests to them. If they exist, their effects should be observable even in the absence of object identity knowledge. We employed Continuous Flash Suppression (CFS) while participants studied object images, such that visual details were learned without explicit object identification. At test, lateral associations were probed using a part-to-part matching task. We also tested whether part-whole links were facilitated by prior study using a part-naming task, and included another study condition (Word), in which participants saw only an object's written name. The key question was whether CFS study (which provided visual information without identity) would better support part-to-part matching (via lateral associations) whereas Word study (which provided identity without the correct visual form) would better support part-naming (via top-down processing). The predicted dissociation was found and confirmed by state-trace analyses. Thus, lateral part-to-part associations were learned and retrieved independently of object identity representations. This establishes novel links between perception and memory, demonstrating that (a) lateral associations at lower levels of the object identification hierarchy exist and contribute to object processing and (b) these associations are learned via rapid, episodic-like mechanisms previously observed for the high-level, arbitrary relations comprising episodic memories. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Affiliation(s)
- Patrick Sadil
- Department of Psychological and Brain Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - Kevin W. Potter
- Department of Psychological and Brain Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - David E. Huber
- Department of Psychological and Brain Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - Rosemary A. Cowell
- Department of Psychological and Brain Sciences, University of Massachusetts, Amherst, MA 01003, USA
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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.
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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
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Shibata K, Lisi G, Cortese A, Watanabe T, Sasaki Y, Kawato M. Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. Neuroimage 2019; 188:539-556. [PMID: 30572110 PMCID: PMC6431555 DOI: 10.1016/j.neuroimage.2018.12.022] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 12/07/2018] [Accepted: 12/11/2018] [Indexed: 11/19/2022] Open
Abstract
Real-time functional magnetic resonance imaging (fMRI) neurofeedback is an experimental framework in which fMRI signals are presented to participants in a real-time manner to change their behaviors. Changes in behaviors after real-time fMRI neurofeedback are postulated to be caused by neural plasticity driven by the induction of specific targeted activities at the neuronal level (targeted neural plasticity model). However, some research groups argued that behavioral changes in conventional real-time fMRI neurofeedback studies are explained by alternative accounts, including the placebo effect and physiological artifacts. Recently, decoded neurofeedback (DecNef) has been developed as a result of adapting new technological advancements, including implicit neurofeedback and fMRI multivariate analyses. DecNef provides strong evidence for the targeted neural plasticity model while refuting the abovementioned alternative accounts. In this review, we first discuss how DecNef refutes the alternative accounts. Second, we propose a model that shows how targeted neural plasticity occurs at the neuronal level during DecNef training. Finally, we discuss computational and empirical evidence that supports the model. Clarification of the neural mechanisms of DecNef would lead to the development of more advanced fMRI neurofeedback methods that may serve as powerful tools for both basic and clinical research.
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Affiliation(s)
- Kazuhisa Shibata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Nagoya, 464-0814, Japan
| | - Giuseppe Lisi
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
| | - Aurelio Cortese
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
| | - Takeo Watanabe
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI, 02912, USA
| | - Yuka Sasaki
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI, 02912, USA
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
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Goldway N, Ablin J, Lubin O, Zamir Y, Keynan JN, Or-Borichev A, Cavazza M, Charles F, Intrator N, Brill S, Ben-Simon E, Sharon H, Hendler T. Volitional limbic neuromodulation exerts a beneficial clinical effect on Fibromyalgia. Neuroimage 2019; 186:758-770. [DOI: 10.1016/j.neuroimage.2018.11.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/03/2018] [Accepted: 11/01/2018] [Indexed: 12/18/2022] Open
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45
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Data-driven tensor independent component analysis for model-based connectivity neurofeedback. Neuroimage 2019; 184:214-226. [DOI: 10.1016/j.neuroimage.2018.08.067] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/21/2018] [Accepted: 08/28/2018] [Indexed: 12/26/2022] Open
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Ekanayake J, Ridgway GR, Winston JS, Feredoes E, Razi A, Koush Y, Scharnowski F, Weiskopf N, Rees G. Volitional modulation of higher-order visual cortex alters human perception. Neuroimage 2018; 188:291-301. [PMID: 30529174 DOI: 10.1016/j.neuroimage.2018.11.054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 11/28/2018] [Accepted: 11/29/2018] [Indexed: 01/03/2023] Open
Abstract
Can we change our perception by controlling our brain activation? Awareness during binocular rivalry is shaped by the alternating perception of different stimuli presented separately to each monocular view. We tested the possibility of causally influencing the likelihood of a stimulus entering awareness. To do this, participants were trained with neurofeedback, using realtime functional magnetic resonance imaging (rt-fMRI), to differentially modulate activation in stimulus-selective visual cortex representing each of the monocular images. Neurofeedback training led to altered bistable perception associated with activity changes in the trained regions. The degree to which training influenced perception predicted changes in grey and white matter volumes of these regions. Short-term intensive neurofeedback training therefore sculpted the dynamics of visual awareness, with associated plasticity in the human brain.
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Affiliation(s)
- Jinendra Ekanayake
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, London, UK; UCL Institute of Cognitive Neuroscience, University College London, UK.
| | - Gerard R Ridgway
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - Joel S Winston
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, London, UK; UCL Institute of Cognitive Neuroscience, University College London, UK
| | - Eva Feredoes
- School of Psychology and Language Sciences, University of Reading, UK
| | - Adeel Razi
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, London, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Yury Koush
- Department of Radiology and Biomedical Imaging, Yale University, 300 Cedar Street, New Haven, CT, 06519, USA
| | - Frank Scharnowski
- Psychiatric University Hospital, University of Zürich, Lenggstrasse 31, 8032, Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057, Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057, Zürich, Switzerland
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, London, UK; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Geraint Rees
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, London, UK; UCL Institute of Cognitive Neuroscience, University College London, UK
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Axonal plasticity associated with perceptual learning in adult macaque primary visual cortex. Proc Natl Acad Sci U S A 2018; 115:10464-10469. [PMID: 30262651 DOI: 10.1073/pnas.1812932115] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Perceptual learning is associated with changes in the functional properties of neurons even in primary sensory areas. In macaque monkeys trained to perform a contour detection task, we have observed changes in contour-related facilitation of neuronal responses in primary visual cortex that track their improvement in performance on a contour detection task. We have previously explored the anatomical substrate of experience-dependent changes in the visual cortex based on a retinal lesion model, where we find sprouting and pruning of the axon collaterals in the cortical lesion projection zone. Here, we attempted to determine whether similar changes occur under normal visual experience, such as that associated with perceptual learning. We labeled the long-range horizontal connections in visual cortex by virally mediated transfer of genes expressing fluorescent probes, which enabled us to do longitudinal two-photon imaging of axonal arbors over the period during which animals improve in contour detection performance. We found that there are substantial changes in the axonal arbors of neurons in cortical regions representing the trained part of the visual field, with sprouting of new axon collaterals and pruning of preexisting axon collaterals. Our findings indicate that changes in the structure of axonal arbors are part of the circuit-level mechanism of perceptual learning, and further support the idea that the learned information is encoded at least in part in primary visual cortex.
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48
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Fukuma R, Yanagisawa T, Yokoi H, Hirata M, Yoshimine T, Saitoh Y, Kamitani Y, Kishima H. Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements. Front Neurosci 2018; 12:478. [PMID: 30050405 PMCID: PMC6050372 DOI: 10.3389/fnins.2018.00478] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 06/25/2018] [Indexed: 11/21/2022] Open
Abstract
Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training. Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks. Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33). Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.
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Affiliation(s)
- Ryohei Fukuma
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Japan
| | - Takufumi Yanagisawa
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Japan.,Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan.,Institute for Advanced Co-Creation Studies, Osaka University, Suita, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan
| | - Hiroshi Yokoi
- Department of Mechanical Engineering and Intelligent Systems, University of Electro-Communications, Chofu, Japan
| | - Masayuki Hirata
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan
| | - Toshiki Yoshimine
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan
| | - Youichi Saitoh
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Department of Neuromodulation and Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Japan.,Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan
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49
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Rance M, Walsh C, Sukhodolsky DG, Pittman B, Qiu M, Kichuk SA, Wasylink S, Koller WN, Bloch M, Gruner P, Scheinost D, Pittenger C, Hampson M. Time course of clinical change following neurofeedback. Neuroimage 2018; 181:807-813. [PMID: 29729393 DOI: 10.1016/j.neuroimage.2018.05.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/01/2018] [Accepted: 05/01/2018] [Indexed: 11/26/2022] Open
Abstract
Neurofeedback - learning to modulate brain function through real-time monitoring of current brain state - is both a powerful method to perturb and probe brain function and an exciting potential clinical tool. For neurofeedback effects to be useful clinically, they must persist. Here we examine the time course of symptom change following neurofeedback in two clinical populations, combining data from two ongoing neurofeedback studies. This analysis reveals a shared pattern of symptom change, in which symptoms continue to improve for weeks after neurofeedback. This time course has several implications for future neurofeedback studies. Most neurofeedback studies are not designed to test an intervention with this temporal pattern of response. We recommend that new studies incorporate regular follow-up of subjects for weeks or months after the intervention to ensure that the time point of greatest effect is sampled. Furthermore, this time course of continuing clinical change has implications for crossover designs, which may attribute long-term, ongoing effects of real neurofeedback to the control intervention that follows. Finally, interleaving neurofeedback sessions with assessments and examining when clinical improvement peaks may not be an appropriate approach to determine the optimal number of sessions for an application.
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Affiliation(s)
- Mariela Rance
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Christopher Walsh
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Denis G Sukhodolsky
- Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Brian Pittman
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States
| | - Maolin Qiu
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Stephen A Kichuk
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States
| | - Suzanne Wasylink
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States
| | - William N Koller
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Michael Bloch
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States
| | - Patricia Gruner
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA; Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Christopher Pittenger
- Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States; Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA; Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States.
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50
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Thibault RT, MacPherson A, Lifshitz M, Roth RR, Raz A. Neurofeedback with fMRI: A critical systematic review. Neuroimage 2018; 172:786-807. [DOI: 10.1016/j.neuroimage.2017.12.071] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 12/18/2017] [Accepted: 12/21/2017] [Indexed: 10/18/2022] Open
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