Connectivity Changes Underlying Neurofeedback Training of Visual Cortex Activity

Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows training of voluntary control over regionally specific brain activity. However, the neural basis of successful neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with neurofeedback training of visual cortex activity. Using dynamic causal modeling (DCM), we found that training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.

[1]  Justin L. Vincent,et al.  Intrinsic Fluctuations within Cortical Systems Account for Intertrial Variability in Human Behavior , 2007, Neuron.

[2]  R. DeCharms,et al.  Reading and controlling human brain activation using real-time functional magnetic resonance imaging , 2007, Trends in Cognitive Sciences.

[3]  Karl J. Friston,et al.  Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution , 2003, NeuroImage.

[4]  B. Postle,et al.  Superior Parietal Cortex Is Critical for the Manipulation of Information in Working Memory , 2009, The Journal of Neuroscience.

[5]  Karl J. Friston,et al.  Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.

[6]  S. Kosslyn,et al.  Neural foundations of imagery , 2001, Nature Reviews Neuroscience.

[7]  C. Beevers,et al.  Neural mechanisms of the cognitive model of depression , 2011, Nature Reviews Neuroscience.

[8]  Karl J. Friston,et al.  Comparing Families of Dynamic Causal Models , 2010, PLoS Comput. Biol..

[9]  Karl J. Friston,et al.  Dynamic causal modeling: A generative model of slice timing in fMRI , 2007, NeuroImage.

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

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

[12]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[13]  Christophe Phillips,et al.  Depression alters “top-down” visual attention: A dynamic causal modeling comparison between depressed and healthy subjects , 2011, NeuroImage.

[14]  T. A. Kelley,et al.  Cortical mechanisms for shifting and holding visuospatial attention. , 2008, Cerebral cortex.

[15]  Karl J. Friston,et al.  Dynamic causal models of neural system dynamics: current state and future extensions , 2007, Journal of Biosciences.

[16]  Karl J. Friston,et al.  Nonlinear Dynamic Causal Models for Fmri Nonlinear Dynamic Causal Models for Fmri Nonlinear Dynamic Causal Models for Fmri , 2022 .

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

[18]  W. James,et al.  The Principles of Psychology. , 1983 .

[19]  Andreas Kleinschmidt,et al.  Ongoing Activity Fluctuations in hMT+ Bias the Perception of Coherent Visual Motion , 2008, The Journal of Neuroscience.

[20]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[21]  Karl J. Friston,et al.  Bayesian model selection for group studies , 2009, NeuroImage.

[22]  Andreas Kleinschmidt,et al.  Spontaneous local variations in ongoing neural activity bias perceptual decisions , 2008, Proceedings of the National Academy of Sciences.

[23]  S. Hayes,et al.  Quantitative meta-analysis of neural activity in posttraumatic stress disorder , 2012, Biology of Mood & Anxiety Disorders.

[24]  Daryl E. Wilson,et al.  Control of Spatial and Feature-Based Attention in Frontoparietal Cortex , 2010, The Journal of Neuroscience.

[25]  Leslie G. Ungerleider,et al.  Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. , 1998, Science.

[26]  Karl J. Friston,et al.  Ten simple rules for dynamic causal modeling , 2010, NeuroImage.

[27]  S. Kosslyn,et al.  Visual mental imagery induces retinotopically organized activation of early visual areas. , 2005, Cerebral cortex.

[28]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[29]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[30]  R Turner,et al.  Optimisation of the 3D MDEFT sequence for anatomical brain imaging: technical implications at 1.5 and 3 T , 2004, NeuroImage.

[31]  Karl J. Friston,et al.  Variational free energy and the Laplace approximation , 2007, NeuroImage.

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

[33]  R. N. Spreng,et al.  Neuroscience and Biobehavioral Reviews Neurocircuitry Models of Posttraumatic Stress Disorder and Beyond: a Meta-analysis of Functional Neuroimaging Studies , 2022 .

[34]  M. Boly,et al.  Baseline brain activity fluctuations predict somatosensory perception in humans , 2007, Proceedings of the National Academy of Sciences.

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

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

[37]  S. Yantis,et al.  A Domain-Independent Source of Cognitive Control for Task Sets: Shifting Spatial Attention and Switching Categorization Rules , 2009, The Journal of Neuroscience.

[38]  Jong-Hwan Lee,et al.  Real-time fMRI-based neurofeedback reinforces causality of attention networks , 2012, Neuroscience Research.

[39]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[40]  Robert Turner,et al.  Image Distortion Correction in fMRI: A Quantitative Evaluation , 2002, NeuroImage.

[41]  Takeo Watanabe,et al.  Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation , 2012 .

[42]  Oliver Speck,et al.  The impact of physiological noise correction on fMRI at 7 T , 2011, NeuroImage.

[43]  S. Yantis,et al.  Transient neural activity in human parietal cortex during spatial attention shifts , 2002, Nature Neuroscience.

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

[45]  Barbara A Dosher,et al.  Blood oxygenation level-dependent contrast response functions identify mechanisms of covert attention in early visual areas , 2008, Proceedings of the National Academy of Sciences.

[46]  Leslie G. Ungerleider,et al.  Increased Activity in Human Visual Cortex during Directed Attention in the Absence of Visual Stimulation , 1999, Neuron.

[47]  J. Duncan,et al.  Top-Down Activation of Shape-Specific Population Codes in Visual Cortex during Mental Imagery , 2009, The Journal of Neuroscience.

[48]  E. DeYoe,et al.  A physiological correlate of the 'spotlight' of visual attention , 1999, Nature Neuroscience.

[49]  Karl J. Friston,et al.  Where bottom-up meets top-down: neuronal interactions during perception and imagery. , 2004, Cerebral cortex.

[50]  J. Driver,et al.  Perceptual awareness and its loss in unilateral neglect and extinction , 2001, Cognition.

[51]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

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

[53]  W. Drevets.  Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders , 2001, Current Opinion in Neurobiology.

[54]  G. Mangun,et al.  The neural mechanisms of top-down attentional control , 2000, Nature Neuroscience.

[55]  Karl J. Friston,et al.  Deconstructing the Architecture of Dorsal and Ventral Attention Systems with Dynamic Causal Modeling , 2012, The Journal of Neuroscience.

[56]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.