Propagation of BOLD activity reveals task-dependent directed interactions across human visual cortex

It has recently been shown that large-scale propagation of blood-oxygen level dependent (BOLD) activity is constrained by anatomical connections and reflects transitions between behavioral states. It remains to be seen, however, if the propagation of BOLD activity can also relate to the brain anatomical structure at a more local scale. Here, we hypothesized that BOLD propagation reflects structured neuronal activity across early visual field maps. To explore this hypothesis, we characterize the propagation of BOLD activity across V1, V2 and V3 using a modeling approach that aims to disentangle the contributions of local activity and directed interactions in shaping BOLD propagation. It does so by estimating the effective connectivity (EC) and the excitability of a noise-diffusion network to reproduce the spatiotemporal covariance structure of the data. We apply our approach to 7T fMRI recordings acquired during resting state (RS) and visual field mapping (VFM). Our results reveal different EC interactions and changes in cortical excitability in RS and VFM, and point to a reconfiguration of feedforward and feedback interactions across the visual system. We conclude that the propagation of BOLD activity has functional relevance, as it reveals directed interactions and changes in cortical excitability in a task-dependent manner.

[1]  J. Cowan,et al.  Temporal oscillations in neuronal nets , 1979, Journal of mathematical biology.

[2]  Ravi S. Menon,et al.  High‐temporal‐resolution studies of the human primary visual cortex at 4 T: Teasing out the oxygenation contribution in FMRI , 1995, Int. J. Imaging Syst. Technol..

[3]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[4]  S. Kosslyn,et al.  Topographical representations of mental images in primary visual cortex , 1995, Nature.

[5]  D. Mumford.  Pattern theory: a unifying perspective , 1996 .

[6]  Guillermo Sapiro,et al.  Creating connected representations of cortical gray matter for functional MRI visualization , 1997, IEEE Transactions on Medical Imaging.

[7]  L. Kaufman,et al.  Study of human occipital alpha rhythm: the alphon hypothesis and alpha suppression. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[9]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[10]  M. D’Esposito,et al.  The variability of human BOLD hemodynamic responses , 1998, NeuroImage.

[11]  Victor A. F. Lamme,et al.  Feedforward, horizontal, and feedback processing in the visual cortex , 1998, Current Opinion in Neurobiology.

[12]  Rajesh P. N. Rao,et al.  Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .

[13]  M. Steriade.  Corticothalamic resonance, states of vigilance and mentation , 2000, Neuroscience.

[14]  M. Raichle.  A Brief History of Human Functional Brain Mapping , 2000 .

[15]  B. Wandell,et al.  Visualization and Measurement of the Cortical Surface , 2000, Journal of Cognitive Neuroscience.

[16]  D J Heeger,et al.  Robust multiresolution alignment of MRI brain volumes , 2000, Magnetic resonance in medicine.

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

[18]  O. Braddick,et al.  Brain Areas Sensitive to Coherent Visual Motion , 2001, Perception.

[19]  N. Harel,et al.  Blood capillary distribution correlates with hemodynamic-based functional imaging in cerebral cortex. , 2002, Cerebral cortex.

[20]  Karl J. Friston,et al.  Multivariate Autoregressive Modelling of fMRI time series , 2003 .

[21]  Rainer Goebel,et al.  Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.

[22]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

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

[25]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

[26]  Sean L. Hill,et al.  The Sleep Slow Oscillation as a Traveling Wave , 2004, The Journal of Neuroscience.

[27]  Karl J. Friston,et al.  A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

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

[29]  A. Angelucci,et al.  Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons. , 2006, Progress in brain research.

[30]  M. Tsodyks,et al.  Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back , 2006, Journal of Computational Neuroscience.

[31]  C. Julien.  The enigma of Mayer waves: Facts and models. , 2006, Cardiovascular research.

[32]  K. Obermayer,et al.  The Role of Feedback in Shaping the Extra-Classical Receptive Field of Cortical Neurons: A Recurrent Network Model , 2006, The Journal of Neuroscience.

[33]  Karl J. Friston,et al.  A free energy principle for the brain , 2006, Journal of Physiology-Paris.

[34]  Karl J. Friston,et al.  Extra-classical receptive field effects measured in striate cortex with fMRI , 2007, NeuroImage.

[35]  Justin L. Vincent,et al.  Intrinsic functional architecture in the anaesthetized monkey brain , 2007, Nature.

[36]  G. Buzsáki,et al.  Inhibition and Brain Work , 2007, Neuron.

[37]  C. Gilbert,et al.  Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.

[38]  Karl J. Friston.  CHAPTER 41 – Dynamic Causal Models for fMRI , 2007 .

[39]  Brian A. Wandell,et al.  Population receptive field estimates in human visual cortex , 2008, NeuroImage.

[40]  Biyu J. He,et al.  The Temporal Structures and Functional Significance of Scale-free Brain Activity , 2010, Neuron.

[41]  B. Wandell,et al.  Mapping Hv4 and Ventral Occipital Cortex: the Venous Eclipse , 2022 .

[42]  Alessandra Angelucci,et al.  Contrast-dependence of surround suppression in Macaque V1: Experimental testing of a recurrent network model , 2010, NeuroImage.

[43]  S. Dumoulin,et al.  The Relationship between Cortical Magnification Factor and Population Receptive Field Size in Human Visual Cortex: Constancies in Cortical Architecture , 2011, The Journal of Neuroscience.

[44]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[45]  Biyu J. He.  Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task , 2011, The Journal of Neuroscience.

[46]  Jakob Heinzle,et al.  Topographically specific functional connectivity between visual field maps in the human brain , 2011, NeuroImage.

[47]  R. Romo,et al.  α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking , 2011, Proceedings of the National Academy of Sciences.

[48]  R. Desimone,et al.  Laminar differences in gamma and alpha coherence in the ventral stream , 2011, Proceedings of the National Academy of Sciences.

[49]  O. Sporns,et al.  Functional connectivity between anatomically unconnected areas is shaped by collective network-level effects in the macaque cortex. , 2012, Cerebral cortex.

[50]  Michael Breakspear,et al.  Hemodynamic Traveling Waves in Human Visual Cortex , 2012, PLoS Comput. Biol..

[51]  A. Oeltermann,et al.  Hippocampal–cortical interaction during periods of subcortical silence , 2012, Nature.

[52]  L. Frank,et al.  Awake Hippocampal Sharp-Wave Ripples Support Spatial Memory , 2012, Science.

[53]  Chun-I Yeh,et al.  Laminar analysis of visually evoked activity in the primary visual cortex , 2012, Proceedings of the National Academy of Sciences.

[54]  P. Robinson,et al.  Human Cortical Traveling Waves: Dynamical Properties and Correlations with Responses , 2012, PloS one.

[55]  Nick F. Ramsey,et al.  Frequency specific spatial interactions in human electrocorticography: V1 alpha oscillations reflect surround suppression , 2013, NeuroImage.

[56]  Lars Muckli,et al.  Network interactions: non-geniculate input to V1 , 2013, Current Opinion in Neurobiology.

[57]  P. Roelfsema,et al.  Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex , 2014, Proceedings of the National Academy of Sciences.

[58]  N. Ramsey,et al.  Predictive coding for motion stimuli in human early visual cortex , 2016, Brain Structure and Function.

[59]  Gustavo Deco,et al.  Intra-cortical propagation of EEG alpha oscillations , 2014, NeuroImage.

[60]  Koen V. Haak,et al.  Cortical connective field estimates from resting state fMRI activity , 2014, Front. Neurosci..

[61]  Matthew B. Bouchard,et al.  Direct, intraoperative observation of ~0.1Hz hemodynamic oscillations in awake human cortex: Implications for fMRI , 2014, NeuroImage.

[62]  Yunjie Tong,et al.  Studying the Spatial Distribution of Physiological Effects on BOLD Signals Using Ultrafast fMRI , 2014, Front. Hum. Neurosci..

[63]  Lars Muckli,et al.  Contributions of cortical feedback to sensory processing in primary visual cortex , 2014, Front. Psychol..

[64]  Enzo Tagliazucchi,et al.  Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep , 2015, eLife.

[65]  Tatsuo K Sato,et al.  Imaging the Awake Visual Cortex with a Genetically Encoded Voltage Indicator , 2015, The Journal of Neuroscience.

[66]  H. Helmholtz.  Handbuch der physiologischen Optik , 2015 .

[67]  Bard Ermentrout,et al.  Synchrony, waves and ripple in spatially coupled Kuramoto oscillators with Mexican hat connectivity , 2015, Biological Cybernetics.

[68]  A. Seth,et al.  Granger Causality Analysis in Neuroscience and Neuroimaging , 2015, The Journal of Neuroscience.

[69]  Omar H. Butt,et al.  Hierarchical and homotopic correlations of spontaneous neural activity within the visual cortex of the sighted and blind , 2015, Front. Hum. Neurosci..

[70]  Klaas E. Stephan,et al.  Dynamic Causal Models for fMRI , 2015 .

[71]  Mohit H. Adhikari,et al.  Hippocampal Sharp-Wave Ripples Influence Selective Activation of the Default Mode Network , 2016, Current Biology.

[72]  K. Ohki,et al.  Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity , 2016, Proceedings of the National Academy of Sciences.

[73]  S. B. Erdoğan,et al.  Systemic Low-Frequency Oscillations in BOLD Signal Vary with Tissue Type , 2016, Front. Neurosci..

[74]  Matthieu Gilson,et al.  Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome , 2016, PLoS Comput. Biol..

[75]  M. Raichle,et al.  Human cortical–hippocampal dialogue in wake and slow-wave sleep , 2016, Proceedings of the National Academy of Sciences.

[76]  P. Kara,et al.  Neural correlates of single vessel hemodynamic responses in vivo , 2016, Nature.

[77]  H. Kennedy,et al.  Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas , 2016, Neuron.

[78]  Martin Vinck,et al.  More Gamma More Predictions: Gamma-Synchronization as a Key Mechanism for Efficient Integration of Classical Receptive Field Inputs with Surround Predictions , 2016, Front. Syst. Neurosci..

[79]  Pascal Fries,et al.  Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronization , 2015, Proceedings of the National Academy of Sciences.

[80]  Peter A. Robinson,et al.  Effects of astrocytic dynamics on spatiotemporal hemodynamics: Modeling and enhanced data analysis , 2017, NeuroImage.

[81]  Gustavo Deco,et al.  The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core , 2016, bioRxiv.

[82]  Lars Muckli,et al.  Cortical feedback signals generalise across different spatial frequencies of feedforward inputs , 2017, NeuroImage.

[83]  Karl J. Friston,et al.  Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions , 2017 .

[84]  Andreas Bartels,et al.  Connectivity Reveals Sources of Predictive Coding Signals in Early Visual Cortex During Processing of Visual Optic Flow , 2016, Cerebral cortex.

[85]  Jonathan R. Polimeni,et al.  Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex , 2018, NeuroImage.

[86]  Koen V. Haak,et al.  Connectopic mapping with resting-state fMRI , 2016, NeuroImage.