Decorrelated Input Dissociates Narrow Band γ Power and BOLD in Human Visual Cortex

Although fMRI using the BOLD contrast is widely used for noninvasively mapping hemodynamic brain activity in humans, its exact link to underlying neural processing is poorly understood. Whereas some studies have reported that BOLD signals measured in visual cortex are tightly linked to neural activity in the narrow band γ (NBG) range, others have found a weak correlation between the two. To elucidate the mechanisms behind these conflicting findings, we hypothesized that BOLD reflects the strength of synaptic inputs to cortex, whereas NBG is more dependent on how well these inputs are correlated. To test this, we measured NBG, BOLD, and cerebral blood flow responses to stimuli that either correlate or decorrelate neural activity in human visual cortex. Next, we simulated a recurrent network model of excitatory and inhibitory neurons that reproduced in detail the experimental NBG and BOLD data. Results show that the visually evoked BOLD response was solely predicted by the sum of local inputs, whereas NBG was critically dependent on how well these inputs were correlated. In summary, the NBG-BOLD relationship strongly depends on the nature of sensory input to cortex: stimuli that increase the number of correlated inputs to visual cortex will increase NBG and BOLD in a similar manner, whereas stimuli that increase the number of decorrelated inputs will dissociate the two. The NBG-BOLD relationship is therefore not fixed but is rather highly dependent on input correlations that are both stimulus- and state-dependent. SIGNIFICANCE STATEMENT It is widely believed that γ oscillations in cortex are tightly linked to local hemodynamic activity. Here, we present experimental evidence showing how a stimulus can increase local blood flow to the brain despite suppressing γ power. Moreover, using a sophisticated model of cortical neurons, it is proposed that this occurs when synaptic input to cortex is strong yet decorrelated. Because input correlations are largely determined by the state of the brain, our results demonstrate that the relationship between γ and local hemodynamics is not fixed, but rather context dependent. This likely explains why certain neurodevelopmental disorders are characterized by weak γ activity despite showing normal blood flow.

[1]  D. Attwell,et al.  Synaptic Energy Use and Supply , 2012, Neuron.

[2]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[3]  D. Schwarzkopf,et al.  Larger Extrastriate Population Receptive Fields in Autism Spectrum Disorders , 2014, The Journal of Neuroscience.

[4]  Hiroshi Ban,et al.  fMRI evidence for areas that process surface gloss in the human visual cortex , 2015, Vision Research.

[5]  J. Maunsell,et al.  Different Origins of Gamma Rhythm and High-Gamma Activity in Macaque Visual Cortex , 2011, PLoS biology.

[6]  Jeffrey G. Ojemann,et al.  Power-Law Scaling in the Brain Surface Electric Potential , 2009, PLoS Comput. Biol..

[7]  H. Kennedy,et al.  Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels , 2014, Neuron.

[8]  K. D. Singh,et al.  BOLD Responses in Human Primary Visual Cortex are Insensitive to Substantial Changes in Neural Activity , 2013, Front. Hum. Neurosci..

[9]  Scott Makeig,et al.  High-frequency Broadband Modulations of Electroencephalographic Spectra , 2009, Front. Hum. Neurosci..

[10]  Paul Schrater,et al.  Shape perception reduces activity in human primary visual cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  R. Shapley,et al.  “Black” Responses Dominate Macaque Primary Visual Cortex V1 , 2009, The Journal of Neuroscience.

[12]  Karl J. Friston.  Modalities, Modes, and Models in Functional Neuroimaging , 2009, Science.

[13]  N. Logothetis,et al.  Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1 , 2006, Nature Neuroscience.

[14]  Xiao-Jing Wang,et al.  What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. , 2003, Journal of neurophysiology.

[15]  R. Shapley,et al.  Generation of Black-Dominant Responses in V1 Cortex , 2010, The Journal of Neuroscience.

[16]  I. Nelken,et al.  Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades , 2008, Neuron.

[17]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[18]  N. Logothetis,et al.  Neurophysiology of the BOLD fMRI Signal in Awake Monkeys , 2008, Current Biology.

[19]  T. L. Davis,et al.  Calibrated functional MRI: mapping the dynamics of oxidative metabolism. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[20]  S. Edelman,et al.  Human Brain Mapping 6:316–328(1998) � A Sequence of Object-Processing Stages Revealed by fMRI in the Human Occipital Lobe , 2022 .

[21]  Alexander S. Ecker,et al.  Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness , 2014, Nature Neuroscience.

[22]  Christopher F. Chabris,et al.  Early visual cortex organization in autism: an fMRI study , 2004, Neuroreport.

[23]  W. Singer,et al.  Hemodynamic Signals Correlate Tightly with Synchronized Gamma Oscillations , 2005, Science.

[24]  Louise S. Delicato,et al.  Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the blood oxygen level-dependent signal in macaque primary visual cortex , 2011, The European journal of neuroscience.

[25]  Yusuke Murayama,et al.  Attention But Not Awareness Modulates the BOLD Signal in the Human V1 During Binocular Suppression , 2011, Science.

[26]  Jonathan Winawer,et al.  Asynchronous Broadband Signals Are the Principal Source of the BOLD Response in Human Visual Cortex , 2013, Current Biology.

[27]  Alexander Thiele,et al.  Attention-Induced Variance and Noise Correlation Reduction in Macaque V1 Is Mediated by NMDA Receptors , 2013, Neuron.

[28]  Hellmuth Obrig,et al.  Stimulus-Induced and State-Dependent Sustained Gamma Activity Is Tightly Coupled to the Hemodynamic Response in Humans , 2009, The Journal of Neuroscience.

[29]  O. Kann,et al.  Energy substrates that fuel fast neuronal network oscillations , 2014, Front. Neurosci..

[30]  M. Scanziani,et al.  Instantaneous Modulation of Gamma Oscillation Frequency by Balancing Excitation with Inhibition , 2009, Neuron.

[31]  Michael W. Reimann,et al.  A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents , 2013, Neuron.

[32]  Klas H. Pettersen,et al.  Modeling the Spatial Reach of the LFP , 2011, Neuron.

[33]  M. Siegel,et al.  Dissociating neuronal gamma-band activity from cranial and ocular muscle activity in EEG , 2013, Front. Hum. Neurosci..

[34]  Melanie R. Bernard,et al.  Deconstruction of Spatial Integrity in Visual Stimulus Detected by Modulation of Synchronized Activity in Cat Visual Cortex , 2008, The Journal of Neuroscience.

[35]  Krish D. Singh,et al.  Functional decoupling of BOLD and gamma‐band amplitudes in human primary visual cortex , 2009, Human brain mapping.

[36]  G. Buzsáki,et al.  Gamma Oscillation by Synaptic Inhibition in a Hippocampal Interneuronal Network Model , 1996, The Journal of Neuroscience.

[37]  N. Logothetis,et al.  The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies , 2012, The Journal of Neuroscience.

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

[39]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[40]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[41]  N. Logothetis.  The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[42]  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.

[43]  T. Sejnowski,et al.  Cortical oscillations arise from contextual interactions that regulate sparse coding , 2014, Proceedings of the National Academy of Sciences.

[44]  Marc Benayoun,et al.  Emergent Oscillations in Networks of Stochastic Spiking Neurons , 2011, PloS one.

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

[46]  N. Logothetis,et al.  Frequency-Band Coupling in Surface EEG Reflects Spiking Activity in Monkey Visual Cortex , 2009, Neuron.

[47]  Kevin Whittingstall,et al.  Effects of neural synchrony on surface EEG. , 2014, Cerebral cortex.

[48]  P. König,et al.  A comparison of hemodynamic and neural responses in cat visual cortex using complex stimuli. , 2004, Cerebral cortex.

[49]  G. Buzsáki,et al.  Mechanisms of gamma oscillations. , 2012, Annual review of neuroscience.

[50]  A. Kohn,et al.  No Consistent Relationship between Gamma Power and Peak Frequency in Macaque Primary Visual Cortex , 2013, The Journal of Neuroscience.

[51]  Louise S. Delicato,et al.  Attention Reduces Stimulus-Driven Gamma Frequency Oscillations and Spike Field Coherence in V1 , 2010, Neuron.

[52]  P. Nunez,et al.  On the Relationship of Synaptic Activity to Macroscopic Measurements: Does Co-Registration of EEG with fMRI Make Sense? , 2004, Brain Topography.

[53]  Karl J. Friston,et al.  Hemodynamic correlates of EEG: A heuristic , 2005, NeuroImage.

[54]  D Hermes,et al.  Stimulus Dependence of Gamma Oscillations in Human Visual Cortex. , 2015, Cerebral cortex.

[55]  Christopher J. Aura,et al.  Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey , 2008, Nature Neuroscience.

[56]  P. Fries,et al.  Beta Oscillation Dynamics in Extrastriate Cortex after Removal of Primary Visual Cortex , 2014, The Journal of Neuroscience.

[57]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[58]  Chun-I Yeh,et al.  Cortical brightness adaptation when darkness and brightness produce different dynamical states in the visual cortex , 2014, Proceedings of the National Academy of Sciences.

[59]  J. Niven.  Neuronal energy consumption: biophysics, efficiency and evolution , 2016, Current Opinion in Neurobiology.

[60]  Branka Milivojevic,et al.  Atypical excitation–inhibition balance in autism captured by the gamma response to contextual modulation☆ , 2013, NeuroImage: Clinical.

[61]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.