Closed-loop brain training: the science of neurofeedback

被引:0
|
作者
Ranganatha Sitaram
Tomas Ros
Luke Stoeckel
Sven Haller
Frank Scharnowski
Jarrod Lewis-Peacock
Nikolaus Weiskopf
Maria Laura Blefari
Mohit Rana
Ethan Oblak
Niels Birbaumer
James Sulzer
机构
[1] Institute for Biological and Medical Engineering,Department of Psychiatry
[2] and Section of Neuroscience,Department of Psychology
[3] Pontificia Universidad Católica de Chile,Department of Neurophysics
[4] Neurology and Imaging of Cognition Lab,Department of Mechanical Engineering
[5] University of Geneva,undefined
[6] National Institute of Diabetes,undefined
[7] Digestive and Kidney Diseases,undefined
[8] National Institutes of Health,undefined
[9] Affidea Centre Diagnostique Radiologique de Carouge CDRC,undefined
[10] Psychiatric University Hospital,undefined
[11] University of Zürich,undefined
[12] Imaging Research Center,undefined
[13] University of Texas at Austin,undefined
[14] Max Planck Institute for Human Cognitive and Brain Sciences,undefined
[15] Wellcome Trust Centre for Neuroimaging,undefined
[16] Institute of Neurology,undefined
[17] University College London,undefined
[18] Defitech Chair in Brain-Machine Interface,undefined
[19] Center for Neuroprosthetics,undefined
[20] École Polytechnique Fédérale de Lausanne,undefined
[21] University of Texas at Austin,undefined
[22] Wyss Center for Bio and Neuroengeneering,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Neurofeedback is a type of biofeedback in which neural activity is measured and presented through one or more sensory channels to the participant in real time to facilitate self-regulation of the putative neural substrates that underlie a particular behaviour or pathologyAnimal and human brain self-regulation has been demonstrated using various invasive and non-invasive recording methods and with different features of the brain signals, such as frequency spectra, functional connectivity or spatiotemporal patterns of brain activityNeurofeedback provides the possibility of endogenously manipulating brain activity as an independent variable, making it a powerful neuroscientific toolNeurofeedback training results in specific neural changes relevant to the trained brain circuit and the associated behavioural changes. These changes have been shown to last anywhere from hours to months after training and to correlate with changes in grey and white matter structureThe underlying neural circuitry relating to the process of brain self-regulation is becoming clearer. Accumulating evidence suggests the involvement of the thalamus and the dorsolateral prefrontal, posterior parietal and occipital cortices in neurofeedback control, and the dorsal and ventral striatum, anterior cingulate cortex and anterior insula in neurofeedback reward processingPsychological factors, such as the differential influence of feedback, reward and experimental instructions, and other factors, such as sense of agency and locus of control, are now being investigated for their effects on neurofeedbackThe demonstration of robust clinical effects remains a major hurdle in neurofeedback research. The results of randomized controlled trials in attention deficit and hyperactivity disorder and stroke rehabilitation have been mixed, and have been affected by differences in study design, difficulty of identifying responders and the scarcity of homogenous patient populationsFuture neurofeedback research will probably clarify the psychological and neural mechanisms that may help to address issues in clinical translation
引用
收藏
页码:86 / 100
页数:14
相关论文
共 50 条
  • [31] Advances in closed-loop deep brain stimulation devices
    Parastarfeizabadi, Mahboubeh
    Kouzani, Abbas Z.
    [J]. JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2017, 14 : 79
  • [32] Advances in closed-loop deep brain stimulation devices
    Mahboubeh Parastarfeizabadi
    Abbas Z. Kouzani
    [J]. Journal of NeuroEngineering and Rehabilitation, 14
  • [33] The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation
    Valenchon, Nicolas
    Bouteiller, Yann
    Jourde, Hugo R.
    L'Heureux, Xavier
    Sobral, Milo
    Coffey, Emily B. J.
    Beltrame, Giovanni
    [J]. PLOS ONE, 2022, 17 (08):
  • [34] A Miniaturized Closed-Loop Optogenetic Brain Stimulation Device
    Kumari, Lekshmy Sudha
    Kouzani, Abbas Z.
    [J]. ELECTRONICS, 2022, 11 (10)
  • [35] Modulation of brain criticality via suppression of EEG long-range temporal correlations (LRTCs) in a closed-loop neurofeedback stimulation
    Dimitriadis, Stavros I.
    Linden, David
    [J]. CLINICAL NEUROPHYSIOLOGY, 2016, 127 (08) : 2878 - 2881
  • [36] LEG EXERCISER FOR TRAINING OF PARALYZED MUSCLE BY CLOSED-LOOP CONTROL
    PETROFSKY, JS
    HEATON, HH
    PHILLIPS, CA
    [J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 1984, 22 (04) : 298 - 303
  • [37] Supporting hybrid courses with closed-loop adaptive training technology
    McCarthy, James E.
    Wayne, John L.
    Deters, Brian J.
    [J]. Smart Innovation, Systems and Technologies, 2013, 17 : 315 - 337
  • [38] A Closed-Loop Training Approach for Massive MIMO Beamforming Systems
    Love, David J.
    Choi, Junil
    Bidigare, Patrick
    [J]. 2013 47TH ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS (CISS), 2013,
  • [39] Closed-loop optimization
    Capdevila-Cortada, Marcal
    [J]. NATURE CATALYSIS, 2024, 7 (02) : 114 - 114
  • [40] Closed-loop ventilation
    Arnal, Jean-Michel
    Katayama, Shinshu
    Howard, Christopher
    [J]. CURRENT OPINION IN CRITICAL CARE, 2023, 29 (01) : 19 - 25