How feedback, motor imagery, and reward influence brain self‐regulation using real‐time fMRI

The learning process involved in achieving brain self‐regulation is presumed to be related to several factors, such as type of feedback, reward, mental imagery, duration of training, among others. Explicitly instructing participants to use mental imagery and monetary reward are common practices in real‐time fMRI (rtfMRI) neurofeedback (NF), under the assumption that they will enhance and accelerate the learning process. However, it is still not clear what the optimal strategy is for improving volitional control. We investigated the differential effect of feedback, explicit instructions and monetary reward while training healthy individuals to up‐regulate the blood‐oxygen‐level dependent (BOLD) signal in the supplementary motor area (SMA). Four groups were trained in a two‐day rtfMRI‐NF protocol: GF with NF only, GF,I with NF + explicit instructions (motor imagery), GF,R with NF + monetary reward, and GF,I,R with NF + explicit instructions (motor imagery) + monetary reward. Our results showed that GF increased significantly their BOLD self‐regulation from day‐1 to day‐2 and GF,R showed the highest BOLD signal amplitude in SMA during the training. The two groups who were instructed to use motor imagery did not show a significant learning effect over the 2 days. The additional factors, namely motor imagery and reward, tended to increase the intersubject variability in the SMA during the course of training. Whole brain univariate and functional connectivity analyses showed common as well as distinct patterns in the four groups, representing the varied influences of feedback, reward, and instructions on the brain. Hum Brain Mapp 37:3153–3171, 2016. © 2016 Wiley Periodicals, Inc.

[1]  H. Siebner,et al.  Dissociating Parieto-Frontal Networks for Phonological and Semantic Word Decisions: A Condition-and-Perturb TMS Study. , 2016, Cerebral cortex.

[2]  Dimitri Van De Ville,et al.  Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated? , 2016, NeuroImage.

[3]  Andreas M. Ray,et al.  A subject-independent pattern-based Brain-Computer Interface , 2015, Front. Behav. Neurosci..

[4]  Jens Frahm,et al.  Training Efficiency and Transfer Success in an Extended Real-Time Functional MRI Neurofeedback Training of the Somatomotor Cortex of Healthy Subjects , 2015, Front. Hum. Neurosci..

[5]  Tilo Kircher,et al.  Self-Regulation of Anterior Insula with Real-Time fMRI and Its Behavioral Effects in Obsessive-Compulsive Disorder: A Feasibility Study , 2015, PloS one.

[6]  Bettina Sorger,et al.  fMRI neurofeedback facilitates anxiety regulation in females with spider phobia , 2015, Front. Behav. Neurosci..

[7]  Simon W. Bock,et al.  Manipulating motor performance and memory through real-time fMRI neurofeedback , 2015, Biological Psychology.

[8]  Usman Naseer A note on large gauge transformations in double field theory , 2015, 1504.05913.

[9]  M. Kawato,et al.  Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network , 2015, Front. Hum. Neurosci..

[10]  Jun Morimoto,et al.  Creating the brain and interacting with the brain: an integrated approach to understanding the brain , 2015, Journal of The Royal Society Interface.

[11]  Sarah E. Pekny,et al.  Reward-Dependent Modulation of Movement Variability , 2015, The Journal of Neuroscience.

[12]  Keum-Shik Hong,et al.  fNIRS-based brain-computer interfaces: a review , 2015, Front. Hum. Neurosci..

[13]  David E. J. Linden,et al.  Real-time fMRI brain-computer interface: development of a “motivational feedback” subsystem for the regulation of visual cue reactivity , 2014, Front. Behav. Neurosci..

[14]  U. Strehl,et al.  What learning theories can teach us in designing neurofeedback treatments , 2014, Front. Hum. Neurosci..

[15]  Niels Birbaumer,et al.  Volitional control of the anterior insula in criminal psychopaths using real-time fMRI neurofeedback: a pilot study , 2014, Front. Behav. Neurosci..

[16]  A. Guillot,et al.  The Neurofunctional Architecture of Motor Imagery , 2014 .

[17]  George I. Christopoulos,et al.  Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications , 2014 .

[18]  Geraint Rees,et al.  Connectivity Changes Underlying Neurofeedback Training of Visual Cortex Activity , 2014, PloS one.

[19]  Nick Medford,et al.  Self-regulation of the anterior insula: Reinforcement learning using real-time fMRI neurofeedback , 2014, NeuroImage.

[20]  Kymberly D. Young,et al.  Real-Time fMRI Neurofeedback Training of Amygdala Activity in Patients with Major Depressive Disorder , 2014, PloS one.

[21]  Niels Birbaumer,et al.  Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks , 2014, Biological Psychology.

[22]  C. Neuper,et al.  Neural substrates of cognitive control under the belief of getting neurofeedback training , 2013, Front. Hum. Neurosci..

[23]  Klaas E. Stephan,et al.  Neurofeedback-mediated self-regulation of the dopaminergic midbrain , 2013, NeuroImage.

[24]  E. Mayer,et al.  Posterior SMA Syndrome following subcortical stroke: Contralateral akinesia reversed by visual feedback , 2013, Neuropsychologia.

[25]  Niels Birbaumer,et al.  Volitional regulation of the supplementary motor area with fMRI-BCI neurofeedback in Parkinson's disease: A pilot study , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[26]  Dimitri Van De Ville,et al.  Dynamic reconfiguration of human brain functional networks through neurofeedback , 2013, NeuroImage.

[27]  C. Neuper,et al.  Learning to modulate one's own brain activity: the effect of spontaneous mental strategies , 2013, Front. Hum. Neurosci..

[28]  Sangkyun Lee,et al.  A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals , 2013, Front. Neurosci..

[29]  G. Rauchs,et al.  Retrieval of Recent Autobiographical Memories is Associated with Slow-Wave Sleep in Early AD , 2013, Front. Behav. Neurosci..

[30]  Christa Neuper,et al.  Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training , 2013, Front. Hum. Neurosci..

[31]  Sven Haller,et al.  Real-time fMRI neurofeedback: Progress and challenges , 2013, NeuroImage.

[32]  N. Birbaumer,et al.  Learned regulation of brain metabolism , 2013, Trends in Cognitive Sciences.

[33]  I. Miyai,et al.  Near-infrared Spectroscopy–mediated Neurofeedback Enhances Efficacy of Motor Imagery–based Training in Poststroke Victims: A Pilot Study , 2013, Stroke.

[34]  R T Constable,et al.  Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity , 2013, Translational Psychiatry.

[35]  Sven Bestmann,et al.  Punishment-Induced Behavioral and Neurophysiological Variability Reveals Dopamine-Dependent Selection of Kinematic Movement Parameters , 2013, The Journal of Neuroscience.

[36]  Manel Martínez-Ramón,et al.  Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas. , 2013, Magnetic resonance imaging.

[37]  Tilo Kircher,et al.  Acquired self‐control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia , 2013, Human brain mapping.

[38]  Niels Birbaumer,et al.  Abnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach , 2012, Front. Psychiatry.

[39]  Geraint Rees,et al.  Improving Visual Perception through Neurofeedback , 2012, The Journal of Neuroscience.

[40]  B. Goodyear,et al.  Origins of intersubject variability of blood oxygenation level dependent and arterial spin labeling fMRI: implications for quantification of brain activity. , 2012, Magnetic resonance imaging.

[41]  Nikolaus Weiskopf,et al.  Real-time fMRI and its application to neurofeedback , 2012, NeuroImage.

[42]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[43]  Niels Birbaumer,et al.  Using real-time fMRI to learn voluntary regulation of the anterior insula in the presence of threat-related stimuli. , 2012, Social cognitive and affective neuroscience.

[44]  Bettina Sorger,et al.  Real-Time Self-Regulation of Emotion Networks in Patients with Depression , 2012, PloS one.

[45]  Mark Chiew,et al.  Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery , 2012, NeuroImage.

[46]  Aaron C. Koralek,et al.  Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills , 2012, Nature.

[47]  Sheng Zhang,et al.  Functional connectivity mapping of the human precuneus by resting state fMRI , 2012, NeuroImage.

[48]  Jonathan R. Wolpaw,et al.  Brain–Computer Interfaces: Something New under the Sun , 2012 .

[49]  Kevin A. Johnson,et al.  Intermittent “Real‐time” fMRI Feedback Is Superior to Continuous Presentation for a Motor Imagery Task: A Pilot Study , 2012, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[50]  Takeo Watanabe,et al.  Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation , 2011, Science.

[51]  D. Wolpert,et al.  Principles of sensorimotor learning , 2011, Nature Reviews Neuroscience.

[52]  R. Goebel,et al.  Real-Time Functional Magnetic Resonance Imaging Neurofeedback for Treatment of Parkinson's Disease , 2011, The Journal of Neuroscience.

[53]  K. Uğurbil,et al.  Correction: Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2011, PLoS ONE.

[54]  C. Price,et al.  The neural correlates of inner speech defined by voxel-based lesion–symptom mapping , 2011, Brain : a journal of neurology.

[55]  W. K. Simmons,et al.  Self-Regulation of Amygdala Activation Using Real-Time fMRI Neurofeedback , 2011, PloS one.

[56]  Tirin Moore,et al.  Selective Attention from Voluntary Control of Neurons in Prefrontal Cortex , 2011, Science.

[57]  Niels Birbaumer,et al.  Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing , 2011, Brain and Language.

[58]  Karl J. Friston Functional and Effective Connectivity: A Review , 2011, Brain Connect..

[59]  Niels Birbaumer,et al.  Real-time support vector classification and feedback of multiple emotional brain states , 2011, NeuroImage.

[60]  Jan Peters,et al.  The neural mechanisms of inter-temporal decision-making: understanding variability , 2011, Trends in Cognitive Sciences.

[61]  Wolfgang Rosenstiel,et al.  Neural mechanisms of brain–computer interface control , 2011, NeuroImage.

[62]  J. Haynes Brain Reading: Decoding Mental States From Brain Activity In Humans , 2011 .

[63]  Niels Birbaumer,et al.  Detection of Cerebral Reorganization Induced by Real-Time fMRI Feedback Training of Insula Activation , 2011, Neurorehabilitation and neural repair.

[64]  Gary H Glover,et al.  Modulation of subgenual anterior cingulate cortex activity with real‐time neurofeedback , 2011, Human brain mapping.

[65]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[66]  Niels Birbaumer,et al.  Volitional Control of Anterior Insula Activity Modulates the Response to Aversive Stimuli. A Real-Time Functional Magnetic Resonance Imaging Study , 2010, Biological Psychiatry.

[67]  C. Price,et al.  Phonological decisions require both the left and right supramarginal gyri , 2010, Proceedings of the National Academy of Sciences.

[68]  Bart Rypma,et al.  Neural and vascular variability and the fMRI-BOLD response in normal aging. , 2010, Magnetic resonance imaging.

[69]  R. Vanwersch,et al.  Neurofeedback training on sensorimotor rhythmin marmoset monkeys , 2010, Neuroreport.

[70]  Sven Haller,et al.  Real-time fMRI feedback training may improve chronic tinnitus , 2010, European Radiology.

[71]  R. Veit,et al.  Self‐regulation of regional cortical activity using real‐time fMRI: The right inferior frontal gyrus and linguistic processing , 2009, Human brain mapping.

[72]  Han-Jeong Hwang,et al.  Neurofeedback-based motor imagery training for brain–computer interface (BCI) , 2009, Journal of Neuroscience Methods.

[73]  C. Kennard,et al.  Functional role of the supplementary and pre-supplementary motor areas , 2008, Nature Reviews Neuroscience.

[74]  Christa Neuper,et al.  Rehabilitation with Brain-Computer Interface Systems , 2008, Computer.

[75]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[76]  N. Logothetis What we can do and what we cannot do with fMRI , 2008, Nature.

[77]  Rupert Lanzenberger,et al.  The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling , 2008, NeuroImage.

[78]  J. O'Doherty,et al.  Direct Instrumental Conditioning of Neural Activity Using Functional Magnetic Resonance Imaging-Derived Reward Feedback , 2007, The Journal of Neuroscience.

[79]  Wolfgang Grodd,et al.  Regulation of anterior insular cortex activity using real-time fMRI , 2007, NeuroImage.

[80]  Niels Birbaumer,et al.  fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment , 2007, Comput. Intell. Neurosci..

[81]  Michael X. Cohen,et al.  Individual Differences and the Neural Representations of Reward Expectation and Reward Prediction Error , 2022 .

[82]  L. Cohen,et al.  Brain–computer interfaces: communication and restoration of movement in paralysis , 2007, The Journal of physiology.

[83]  E. Fetz Volitional control of neural activity: implications for brain–computer interfaces , 2007, The Journal of physiology.

[84]  Cuntai Guan,et al.  Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface , 2007, NeuroImage.

[85]  Daniel M. Corcos,et al.  Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: A meta-analysis , 2006, NeuroImage.

[86]  M. Lotze,et al.  Motor imagery , 2006, Journal of Physiology-Paris.

[87]  A. Cavanna,et al.  The precuneus: a review of its functional anatomy and behavioural correlates. , 2006, Brain : a journal of neurology.

[88]  Raymond J. Dolan,et al.  Contingency awareness in human aversive conditioning involves the middle frontal gyrus , 2006, NeuroImage.

[89]  John D E Gabrieli,et al.  Control over brain activation and pain learned by using real-time functional MRI. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[90]  Clifford R Jack,et al.  Functional heterogeneity of the supplementary motor area. , 2005, AJNR. American journal of neuroradiology.

[91]  Egill Rostrup,et al.  Motion or activity: their role in intra- and inter-subject variation in fMRI , 2005, NeuroImage.

[92]  S. Small,et al.  Fine modulation in network activation during motor execution and motor imagery. , 2004, Cerebral cortex.

[93]  R. Veit,et al.  Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI) , 2004, Journal of Physiology-Paris.

[94]  J. Baron,et al.  'In the course of time': a PET study of the cerebral substrates of autobiographical amnesia in Alzheimer's disease. , 2004, Brain : a journal of neurology.

[95]  Bernhard Schölkopf,et al.  Support vector channel selection in BCI , 2004, IEEE Transactions on Biomedical Engineering.

[96]  Niels Birbaumer,et al.  Self-Regulation of local brain activity and its behavioural consequences , 2004 .

[97]  Andrew R. A. Conway,et al.  Working memory capacity and its relation to general intelligence , 2003, Trends in Cognitive Sciences.

[98]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[99]  Michael Erb,et al.  Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data , 2003, NeuroImage.

[100]  David D. Cox,et al.  Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.

[101]  Alan C. Evans,et al.  Motor Learning Produces Parallel Dynamic Functional Changes during the Execution and Imagination of Sequential Foot Movements , 2002, NeuroImage.

[102]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[103]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[104]  Brian Knutson,et al.  Anticipation of Increasing Monetary Reward Selectively Recruits Nucleus Accumbens , 2001, The Journal of Neuroscience.

[105]  J B Poline,et al.  Partially overlapping neural networks for real and imagined hand movements. , 2000, Cerebral cortex.

[106]  S. Ikemoto,et al.  The role of nucleus accumbens dopamine in motivated behavior: a unifying interpretation with special reference to reward-seeking , 1999, Brain Research Reviews.

[107]  C. Marsden,et al.  Self-initiated versus externally triggered movements. I. An investigation using measurement of regional cerebral blood flow with PET and movement-related potentials in normal and Parkinson's disease subjects. , 1995, Brain : a journal of neurology.

[108]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[109]  S. Ogawa,et al.  Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields , 1990, Magnetic resonance in medicine.

[110]  I. Wickram Biofeedback: A Practitioner's Guide , 1987 .

[111]  M. Raichle,et al.  Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[112]  L. Birch,et al.  Eating as the "Means" Activity in a Contingency: Effects on Young Children's Food Preference. , 1984 .

[113]  M. Bouchard,et al.  Information and reward in voluntary heart rate control. , 1980, The Journal of general psychology.

[114]  D. S. Holmes,et al.  Effects of instructions, biofeedback, reward, and cognitive mediation on the control of heart rate and the application of that control in a stressful situation , 1978 .

[115]  C. Bruce,et al.  Operant Conditioning of Single-Unit Response Patterns in Visual Cortex , 1974, Science.

[116]  E. Blanchard,et al.  Differential Effects of Feedback and Reinforcement in Voluntary Acceleration of Human Heart Rate , 1974, Perceptual and motor skills.

[117]  E. Fetz,et al.  Operant Conditioning of Specific Patterns of Neural and Muscular Activity , 1971, Science.

[118]  M. Sterman,et al.  ELECTROPHYSIOLOGICAL CORRELATES AND NEURAL SUBSTRATES OF ALIMENTARY BEHAVIOR IN THE CAT * , 1969, Annals of the New York Academy of Sciences.

[119]  E. Fetz Operant Conditioning of Cortical Unit Activity , 1969, Science.

[120]  G. Bower,et al.  Effects of amount of reward on strength of approach in an approach-avoidance conflict. , 1960, Journal of comparative and physiological psychology.

[121]  G. Bower,et al.  Reward magnitude and learning in a single-presentation discrimination. , 1959, Journal of comparative and physiological psychology.

[122]  W. Kirchner Age differences in short-term retention of rapidly changing information. , 1958, Journal of experimental psychology.

[123]  Dimitri Van De Ville,et al.  Meta-analysis of real-time fMRI neurofeedback studies: how is brain regulation mediated? , 2018 .

[124]  S. Hallerb,et al.  Real-time fMRI neurofeedback : progress and challenges , 2017 .

[125]  Jong-Hwan Lee,et al.  Neurofeedback fMRI‐mediated learning and consolidation of regional brain activation during motor imagery , 2008, Int. J. Imaging Syst. Technol..

[126]  E. Deci,et al.  A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. , 1999, Psychological bulletin.

[127]  J. Hogg Magnetic resonance imaging. , 1994, Journal of the Royal Naval Medical Service.

[128]  J. Lacroix,et al.  Mechanisms of Biofeedback Control , 1986 .

[129]  P. Roland,et al.  Supplementary motor area and other cortical areas in organization of voluntary movements in man. , 1980, Journal of neurophysiology.

[130]  E. Deci Effects of Externally Mediated Rewards on Intrinsic Motivation. , 1971 .