Can we predict real‐time fMRI neurofeedback learning success from pretraining brain activity?

Abstract Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs (i.e., self‐regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain‐based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

[1]  Michael Lührs,et al.  Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks in adolescent females , 2020, NeuroImage.

[2]  Sophia E. Pépés,et al.  Activity or connectivity? A randomized controlled feasibility study evaluating neurofeedback training in Huntington’s disease , 2020, Brain communications.

[3]  J. Molinuevo,et al.  Earliest amyloid and tau deposition modulate the influence of limbic networks during closed-loop hippocampal downregulation , 2020, Brain : a journal of neurology.

[4]  Dustin Scheinost,et al.  Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function , 2019, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[5]  E. Rolls,et al.  The Orbitofrontal Cortex , 2019 .

[6]  J. Molinuevo,et al.  The effect of APOE genotype and streamline density volume, on hippocampal CA1 down-regulation: a real-time fMRI virtual reality neurofeedback study , 2019, bioRxiv.

[7]  Thad A. Polk,et al.  Independent Components of Neural Activation Associated with 100 Days of Cognitive Training , 2019, Journal of Cognitive Neuroscience.

[8]  Frank Schneider,et al.  Neurofeedback of core language network nodes modulates connectivity with the default-mode network: A double-blind fMRI neurofeedback study on auditory verbal hallucinations , 2019, NeuroImage.

[9]  Kathrin Cohen Kadosh,et al.  A systematic review of the psychological factors that influence neurofeedback learning outcomes , 2019, NeuroImage.

[10]  Stavros Skouras,et al.  The effects of psychiatric history and age on self-regulation of the default mode network , 2018, NeuroImage.

[11]  F. Scharnowski,et al.  Self-regulation of the dopaminergic reward circuit in cocaine users with mental imagery and neurofeedback , 2018, bioRxiv.

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

[13]  G. Rees,et al.  Stimulating neural plasticity with real‐time fMRI neurofeedback in Huntington's disease: A proof of concept study , 2017, Human brain mapping.

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

[15]  Robert Suurmond,et al.  Introduction, comparison, and validation of Meta‐Essentials: A free and simple tool for meta‐analysis , 2017, Research synthesis methods.

[16]  C. Neuper,et al.  Ability to Gain Control Over One’s Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study , 2017, Front. Hum. Neurosci..

[17]  Albert H. van der Veer,et al.  Volitional regulation of brain responses to food stimuli in overweight and obese subjects: A real-time fMRI feedback study , 2017, Appetite.

[18]  D. Bassett,et al.  A network engineering perspective on probing and perturbing cognition with neurofeedback , 2017, Annals of the New York Academy of Sciences.

[19]  Jerzy Bodurka,et al.  Randomized Clinical Trial of Real-Time fMRI Amygdala Neurofeedback for Major Depressive Disorder: Effects on Symptoms and Autobiographical Memory Recall. , 2017, The American journal of psychiatry.

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

[21]  C. Kemner,et al.  Spatial Frequency Training Modulates Neural Face Processing: Learning Transfers from Low- to High-Level Visual Features , 2017, Front. Hum. Neurosci..

[22]  O. Shriki,et al.  Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning , 2017, Neuroscience.

[23]  Y. Koush,et al.  Continuous vs. intermittent neurofeedback to regulate auditory cortex activity of tinnitus patients using real-time fMRI - A pilot study , 2017, NeuroImage: Clinical.

[24]  R. Lanius,et al.  The neurobiology of emotion regulation in posttraumatic stress disorder: Amygdala downregulation via real‐time fMRI neurofeedback , 2017, Human brain mapping.

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

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

[27]  Bettina Sorger,et al.  When the Brain Takes ‘BOLD’ Steps: Real-Time fMRI Neurofeedback Can Further Enhance the Ability to Gradually Self-regulate Regional Brain Activation , 2016, Neuroscience.

[28]  Vince D. Calhoun,et al.  The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository , 2016, NeuroImage.

[29]  Nathan Intrator,et al.  Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging–Inspired Electroencephalography Improves Implicit Emotion Regulation , 2016, Biological Psychiatry.

[30]  Niels Birbaumer,et al.  Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback , 2016, Neurorehabilitation and neural repair.

[31]  Christo Pantev,et al.  Dissociation of Neural Networks for Predisposition and for Training-Related Plasticity in Auditory-Motor Learning. , 2016, Cerebral cortex.

[32]  Michelle Hampson,et al.  Real-Time fMRI Neurofeedback with War Veterans with Chronic PTSD: A Feasibility Study , 2016, Front. Psychiatry.

[33]  M. Ruf,et al.  Alterations of amygdala-prefrontal connectivity with real-time fMRI neurofeedback in BPD patients. , 2016, Social cognitive and affective neuroscience.

[34]  Nikolaus Weiskopf,et al.  Real-time fMRI neurofeedback , 2016 .

[35]  Peng Ren,et al.  Voluntary control of anterior insula and its functional connections is feedback-independent and increases pain empathy , 2016, NeuroImage.

[36]  D. Van de Ville,et al.  Active pain coping is associated with the response in real-time fMRI neurofeedback during pain , 2016, Brain Imaging and Behavior.

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

[38]  Nan-kuei Chen,et al.  Cognitive Neurostimulation: Learning to Volitionally Sustain Ventral Tegmental Area Activation , 2016, Neuron.

[39]  J. J. Cicchese,et al.  Hippocampal Non-Theta-Contingent Eyeblink Classical Conditioning: A Model System for Neurobiological Dysfunction , 2016, Front. Psychiatry.

[40]  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.

[41]  Roberto Lent,et al.  Enhancing Motor Network Activity Using Real-Time Functional MRI Neurofeedback of Left Premotor Cortex , 2015, Front. Behav. Neurosci..

[42]  S. W. Rieger,et al.  Learning Control Over Emotion Networks Through Connectivity‐Based Neurofeedback , 2015, Cerebral cortex.

[43]  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..

[44]  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.

[45]  Jong-Hwan Lee,et al.  The Inclusion of Functional Connectivity Information into fMRI-based Neurofeedback Improves Its Efficacy in the Reduction of Cigarette Cravings , 2015, Journal of Cognitive Neuroscience.

[46]  B. Ertl-Wagner,et al.  Modulation of Craving Related Brain Responses Using Real-Time fMRI in Patients with Alcohol Use Disorder , 2015, PloS one.

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

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

[49]  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..

[50]  Y. Koush,et al.  Upregulation of the Rostral Anterior Cingulate Cortex can Alter the Perception of Emotions: fMRI-Based Neurofeedback at 3 and 7 T , 2015, Brain Topography.

[51]  Roger Gassert,et al.  Improvement in precision grip force control with self-modulation of primary motor cortex during motor imagery , 2015, Front. Behav. Neurosci..

[52]  M. Lobo,et al.  Shining light on motivation, emotion, and memory processes , 2015, Front. Behav. Neurosci..

[53]  Dimitri Van De Ville,et al.  Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training , 2014, NeuroImage.

[54]  Sven Haller,et al.  Comparison of anterior cingulate vs. insular cortex as targets for real-time fMRI regulation during pain stimulation , 2014, Front. Behav. Neurosci..

[55]  Suzanne Wasylink,et al.  Resting state functional connectivity predicts neurofeedback response , 2014, Front. Behav. Neurosci..

[56]  Christian Paret,et al.  Down-regulation of amygdala activation with real-time fMRI neurofeedback in a healthy female sample , 2014, Front. Behav. Neurosci..

[57]  Y. Koush,et al.  Upregulation of the Rostral Anterior Cingulate Cortex can Alter the Perception of Emotions: fMRI-Based Neurofeedback at 3 and 7 T , 2014, Brain Topography.

[58]  Gary H. Glover,et al.  Control of nucleus accumbens activity with neurofeedback , 2014, NeuroImage.

[59]  G. Fisone,et al.  Acquisition and expression of conditioned taste aversion differentially affects extracellular signal regulated kinase and glutamate receptor phosphorylation in rat prefrontal cortex and nucleus accumbens , 2014, Front. Behav. Neurosci..

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

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

[62]  René J. Huster,et al.  Modulation of frontal-midline theta by neurofeedback , 2014, Biological Psychology.

[63]  K. Brady,et al.  Sustained reduction of nicotine craving with real-time neurofeedback: exploring the role of severity of dependence. , 2013, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[64]  S. Swinnen,et al.  Topological correlations of structural and functional networks in patients with traumatic brain injury , 2013, Front. Hum. Neurosci..

[65]  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).

[66]  Dimitri Van De Ville,et al.  Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI☆ , 2013, Neuroimage.

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

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

[69]  K. Brady,et al.  Reduction of cue-induced craving through realtime neurofeedback in nicotine users: The role of region of interest selection and multiple visits , 2013, Psychiatry Research: Neuroimaging.

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

[71]  Roger Gassert,et al.  WITHDRAWN: Neurofeedback-mediated self-regulation of the dopaminergic midbrain , 2013, NeuroImage.

[72]  Kevin A. Johnson,et al.  Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: a preliminary real‐time fMRI study , 2013, Addiction biology.

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

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

[75]  Hubert Preissl,et al.  The Obese Brain Athlete: Self-Regulation of the Anterior Insula in Adiposity , 2012, PloS one.

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

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

[78]  H. Sanaei-Zadeh Acute aluminium phosphide poisoning: Can we predict survival? , 2012, Indian journal of anaesthesia.

[79]  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.

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

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

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

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

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

[85]  Benedikt Zoefel,et al.  Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance , 2011, NeuroImage.

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

[87]  C. Spielberger State‐Trait Anxiety Inventory , 2010 .

[88]  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.

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

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

[91]  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.

[92]  D. Altman,et al.  Measuring inconsistency in meta-analyses , 2003, BMJ : British Medical Journal.

[93]  Francis J. Keefe,et al.  The use of coping strategies in chronic low back pain patients: Relationship to patient characteristics and current adjustment , 1983, Pain.

[94]  D vonZerssen,et al.  [A scale for the objective evaluation of the state of subjective well-being as a method for longitudinal studies]. , 1970 .

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

[96]  Michael P. Weisend,et al.  Enhanced control of dorsolateral prefrontal cortex neurophysiology with real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training and working memory practice , 2016, NeuroImage.

[97]  K. Brady,et al.  Individualized real-time fMRI neurofeedback to attenuate craving in nicotine-dependent smokers. , 2016, Journal of psychiatry & neuroscience : JPN.

[98]  Li Tong,et al.  Self-regulation of rACC activation in patients with Postherpetic Neuralgia : A preliminary study using Real-time fMRI neurofeedback , 2013 .

[99]  E. Seifritz,et al.  Real-time Neurofeedback Using Functional MRI Could Improve Down-Regulation of Amygdala Activity During Emotional Stimulation: A Proof-of-Concept Study , 2013, Brain Topography.

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

[101]  최은호 통신망 기술 체계 ( Network Engineering ) , 1994 .

[102]  T. Barber,et al.  The Barber Suggestibility Scale and the Creative Imagination Scale: experimental and clinical applications. , 1978, The American journal of clinical hypnosis.