Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback

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. HIGHLIGHTSDecoded Neurofeedback (DecNef) leads to specific behavioral changes.We propose a targeted neural plasticity model that accounts for results by DecNef.Results of meta‐analyses based on DecNef results and simulations support the model.

[1]  Nikos K Logothetis,et al.  Interpreting the BOLD signal. , 2004, Annual review of physiology.

[2]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[3]  Jörn Diedrichsen,et al.  A multivariate method to determine the dimensionality of neural representation from population activity , 2013, NeuroImage.

[4]  Mitsuo Kawato,et al.  Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants , 2017, NeuroImage.

[5]  M. Kawato,et al.  Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance , 2016, Nature Communications.

[6]  G. Rees,et al.  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2005, Nature Neuroscience.

[7]  Response to Comment on 'Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation'; How can a decoded neurofeedback method (DecNef) lead to successful reinforcement and visual perceptual learning? , 2016, 1612.04234.

[8]  M. Kawato,et al.  Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning. , 2006, Journal of neurophysiology.

[9]  R. Sitaram,et al.  How feedback, motor imagery, and reward influence brain self‐regulation using real‐time fMRI , 2016, Human brain mapping.

[10]  H. Boyaci,et al.  Statistical Analysis Methods for the fMRI Data , 2011 .

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

[12]  Duncan L. Turner,et al.  Real-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation , 2016, Current opinion in neurology.

[13]  Peter Filzmoser,et al.  Introduction to Multivariate Statistical Analysis in Chemometrics , 2009 .

[14]  Jonathan D. Cohen,et al.  Closed-loop training of attention with real-time brain imaging , 2015, Nature Neuroscience.

[15]  Kenneth D Harris,et al.  Stochastic transitions into silence cause noise correlations in cortical circuits , 2015, Proceedings of the National Academy of Sciences.

[16]  D. Hubel,et al.  Anatomical demonstration of orientation columns in macaque monkey , 1978, The Journal of comparative neurology.

[17]  Robert T. Thibault,et al.  The self-regulating brain and neurofeedback: Experimental science and clinical promise , 2016, Cortex.

[18]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[19]  H. Guio,et al.  [Response to a comment on]. , 2016, Revista peruana de medicina experimental y salud publica.

[20]  P. Schnurr,et al.  Cognitive behavioral therapy for posttraumatic stress disorder in women: a randomized controlled trial. , 2007, JAMA.

[21]  G. Glover,et al.  Assessment of cerebral oxidative metabolism with breath holding and fMRI , 1999, Magnetic resonance in medicine.

[22]  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.

[23]  Rafael Malach,et al.  Covert neurofeedback without awareness shapes cortical network spontaneous connectivity , 2016, Proceedings of the National Academy of Sciences.

[24]  Takeo Watanabe,et al.  Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States , 2016, PLoS biology.

[25]  G. La Camera,et al.  Stimuli Reduce the Dimensionality of Cortical Activity , 2015, bioRxiv.

[26]  Regula S Briellmann,et al.  Brief breath holding may confound functional magnetic resonance imaging studies , 2005, Human brain mapping.

[27]  J. Kennedy,et al.  EXPERIMENTER EFFECTS IN PARAPSYCHOLOGICAL RESEARCH , 2013 .

[28]  P. Dayan,et al.  Supporting Online Material Materials and Methods Som Text Figs. S1 to S9 References the Asynchronous State in Cortical Circuits , 2022 .

[29]  Takeo Watanabe,et al.  Learning to Associate Orientation with Color in Early Visual Areas by Associative Decoded fMRI Neurofeedback , 2016, Current Biology.

[30]  S. S. Fox,et al.  Operant Control of Neural Events in Humans , 1969, Science.

[31]  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.

[32]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[33]  Byron M. Yu,et al.  Neural constraints on learning , 2014, Nature.

[34]  K. Doya,et al.  A Neural Correlate of Reward-Based Behavioral Learning in Caudate Nucleus: A Functional Magnetic Resonance Imaging Study of a Stochastic Decision Task , 2004, The Journal of Neuroscience.

[35]  Takashi Hanakawa,et al.  Neuroanatomical correlates of brain–computer interface performance , 2015, NeuroImage.

[36]  A. Grinvald,et al.  Spontaneously emerging cortical representations of visual attributes , 2003, Nature.

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

[38]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[39]  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.

[40]  Gary H. Glover,et al.  Learned regulation of spatially localized brain activation using real-time fMRI , 2004, NeuroImage.

[41]  R W Cox,et al.  Real‐Time Functional Magnetic Resonance Imaging , 1995, Magnetic resonance in medicine.

[42]  Michael Lifshitz,et al.  Neurofeedback with fMRI: A critical systematic review , 2017, NeuroImage.

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

[44]  Karl J. Friston,et al.  Functional MR imaging correlations with positron emission tomography. Initial experience using a cognitive activation paradigm on verbal working memory. , 1995, Neuroimaging clinics of North America.

[45]  Jarrod A. Lewis-Peacock,et al.  Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment , 2017, PLoS Comput. Biol..

[46]  József Fiser,et al.  Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment , 2011, Science.

[47]  Hebbian Plasticity for Improving Perceptual Decisions , 2016, 1612.03270.

[48]  K. Harris,et al.  Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations , 2009, Neuron.

[49]  Timothy E. J. Behrens,et al.  Learning the value of information in an uncertain world , 2007, Nature Neuroscience.

[50]  Dario L Ringach,et al.  Spontaneous and driven cortical activity: implications for computation , 2009, Current Opinion in Neurobiology.

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

[52]  Takeo Watanabe,et al.  Advances in fMRI Real-Time Neurofeedback , 2017, Trends in Cognitive Sciences.

[53]  T. D. Papageorgiou,et al.  Brain–computer interfaces increase whole-brain signal to noise , 2013, Proceedings of the National Academy of Sciences.

[54]  Mitsuo Kawato,et al.  Towards an unconscious neural reinforcement intervention for common fears , 2018, Proceedings of the National Academy of Sciences.

[55]  Kenji Doya,et al.  What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? , 1999, Neural Networks.

[56]  Robert T. Thibault,et al.  Neurofeedback or neuroplacebo? , 2017, Brain : a journal of neurology.

[57]  B. Seymour,et al.  Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure , 2016, Nature Human Behaviour.

[58]  Xiaoping P. Hu,et al.  Real‐time fMRI using brain‐state classification , 2007, Human brain mapping.

[59]  Invariant Object Identification A Neural Network Model of , 2010 .

[60]  Haim Sompolinsky,et al.  Patterns of Ongoing Activity and the Functional Architecture of the Primary Visual Cortex , 2004, Neuron.

[61]  Misha Tsodyks,et al.  From , 2020, Definitions.

[62]  Masa-aki Sato,et al.  Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.

[63]  Jarrod A. Lewis-Peacock,et al.  Closed-loop brain training: the science of neurofeedback , 2017, Nature Reviews Neuroscience.