Improving voltage-sensitive dye imaging: with a little help from computational approaches

Abstract. Voltage-sensitive dye imaging (VSDI) is a key neurophysiological recording tool because it reaches brain scales that remain inaccessible to other techniques. The development of this technique from in vitro to the behaving nonhuman primate has only been made possible thanks to the long-lasting, visionary work of Amiram Grinvald. This work has opened new scientific perspectives to the great benefit to the neuroscience community. However, this unprecedented technique remains largely under-utilized, and many future possibilities await for VSDI to reveal new functional operations. One reason why this tool has not been used extensively is the inherent complexity of the signal. For instance, the signal reflects mainly the subthreshold neuronal population response and is not linked to spiking activity in a straightforward manner. Second, VSDI gives access to intracortical recurrent dynamics that are intrinsically complex and therefore nontrivial to process. Computational approaches are thus necessary to promote our understanding and optimal use of this powerful technique. Here, we review such approaches, from computational models to dissect the mechanisms and origin of the recorded signal, to advanced signal processing methods to unravel new neuronal interactions at mesoscopic scale. Only a stronger development of interdisciplinary approaches can bridge micro- to macroscales.

[1]  Risto Miikkulainen,et al.  Cooperative self-organization of afferent and lateral connections in cortical maps , 1994, Biological Cybernetics.

[2]  Takusige Katura,et al.  Isolation of neural activities from respiratory and heartbeat noises for in vivo optical recording in guinea pigs using independent component analysis , 2003, Neuroscience Letters.

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

[4]  M. Carandini,et al.  Membrane Potential and Firing Rate in Cat Primary Visual Cortex , 2000, The Journal of Neuroscience.

[5]  A Grinvald,et al.  In-vivo Optical Imaging of Cortical Architecture and Dynamics , 1999 .

[6]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[7]  D. Heeger,et al.  In this issue , 2002, Nature Reviews Drug Discovery.

[8]  Olivier D. Faugeras,et al.  A Constructive Mean-Field Analysis of Multi-Population Neural Networks with Random Synaptic Weights and Stochastic Inputs , 2008, Front. Comput. Neurosci..

[9]  Rodrigo Quian Quiroga,et al.  Past, present and future of spike sorting techniques , 2015, Brain Research Bulletin.

[10]  Alain Destexhe,et al.  Conductance-Based Integrate-and-Fire Models , 1997, Neural Computation.

[11]  Adrian T. Lee,et al.  fMRI of human visual cortex , 1994, Nature.

[12]  G. Blasdel,et al.  Differential imaging of ocular dominance and orientation selectivity in monkey striate cortex , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[13]  Robert A. Frazor,et al.  Standing Waves and Traveling Waves Distinguish Two Circuits in Visual Cortex , 2007, Neuron.

[14]  Wen-Liang Hwang,et al.  Wavelet analysis for brain-function imaging , 1995, IEEE Trans. Medical Imaging.

[15]  E. Seidemann,et al.  Dynamics of Depolarization and Hyperpolarization in the Frontal Cortex and Saccade Goal , 2002, Science.

[16]  Michael Feldman,et al.  Hilbert Transform Applications in Mechanical Vibration: Feldman/Hilbert Transform Applications in Mechanical Vibration , 2011 .

[17]  Terrence J Sejnowski,et al.  Validation of independent component analysis for rapid spike sorting of optical recording data. , 2010, Journal of neurophysiology.

[18]  Helen H Yang,et al.  Genetically Encoded Voltage Indicators: Opportunities and Challenges , 2016, The Journal of Neuroscience.

[19]  K. Miller Understanding layer 4 of the cortical circuit: a model based on cat V1. , 2003, Cerebral cortex.

[20]  Dewen Hu,et al.  An Evaluation of Linear Model Analysis Techniques for Processing Images of Microcirculation Activity , 1998, NeuroImage.

[21]  Nikola T. Markov,et al.  Weight Consistency Specifies Regularities of Macaque Cortical Networks , 2010, Cerebral cortex.

[22]  Eero P. Simoncelli,et al.  How MT cells analyze the motion of visual patterns , 2006, Nature Neuroscience.

[23]  L. Sirovich,et al.  An Optimization Approach to Signal Extraction from Noisy Multivariate Data , 2001, NeuroImage.

[24]  T. Sejnowski,et al.  Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons , 2001, Neuroscience.

[25]  Richard B. Buxton,et al.  Dynamic models of BOLD contrast , 2012, NeuroImage.

[26]  Thomas Wennekers,et al.  2009 Special Issue: Spatiotemporal dynamics in the cortical microcircuit: A modelling study of primary visual cortex layer 2/3 , 2009 .

[27]  Frédéric Chavane,et al.  A biophysical cortical column model to study the multi-component origin of the VSDI signal , 2010, NeuroImage.

[28]  Amiram Grinvald,et al.  Removal of spatial biological artifacts in functional maps by local similarity minimization , 2009, Journal of Neuroscience Methods.

[29]  Amiram Grinvald,et al.  Temporally-structured acquisition of multidimensional optical imaging data facilitates visualization of elusive cortical representations in the behaving monkey , 2013, NeuroImage.

[30]  Jean-Luc R Stevens,et al.  Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex , 2013, The Journal of Neuroscience.

[31]  Mark W. Woolrich,et al.  Constrained linear basis sets for HRF modelling using Variational Bayes , 2004, NeuroImage.

[32]  Nicholas J. Priebe,et al.  Mechanisms of Neuronal Computation in Mammalian Visual Cortex , 2012, Neuron.

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

[34]  Dirk Jancke,et al.  Voltage-sensitive dye imaging of transcranial magnetic stimulation-induced intracortical dynamics , 2014, Proceedings of the National Academy of Sciences.

[35]  Pascale Pham,et al.  Probing the functional impact of sub-retinal prosthesis , 2016, eLife.

[36]  Risto Miikkulainen,et al.  A computational model of the signals in optical imaging with voltage-sensitive dyes , 2007, Neurocomputing.

[37]  Amiram Grinvald,et al.  Independent component analysis of high-resolution imaging data identifies distinct functional domains , 2007, NeuroImage.

[38]  Ying Zheng,et al.  Signal Source Separation in the Analysis of Neural Activity in Brain , 2001, NeuroImage.

[39]  Arthur W. Toga,et al.  The Evolution of Optical Signals in Human and Rodent Cortex , 1996, NeuroImage.

[40]  R. Frostig,et al.  Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[41]  T. Sejnowski,et al.  Independent component analysis at the neural cocktail party , 2001, Trends in Neurosciences.

[42]  F. Chavane,et al.  Imaging cortical correlates of illusion in early visual cortex , 2004, Nature.

[43]  P. Mitra,et al.  Analysis of dynamic brain imaging data. , 1998, Biophysical journal.

[44]  Ingo Schießl,et al.  Independent components of the haemodynamic response in intrinsic optical imaging , 2008, NeuroImage.

[45]  Frédéric Chavane,et al.  Effects of GABAA kinetics on cortical population activity: computational studies and physiological confirmations. , 2016, Journal of neurophysiology.

[46]  P. Adorján,et al.  Axonal topography of cortical basket cells in relation to orientation, direction, and ocular dominance maps , 2001, The Journal of comparative neurology.

[47]  Lawrence Sirovich,et al.  Separating spatially distributed response to stimulation from background. I. Optical imaging , 1997, Biological Cybernetics.

[48]  Sébastien Roux,et al.  Vobi One: a data processing software package for functional optical imaging , 2014, Front. Neurosci..

[49]  Maria V. Sanchez-Vives,et al.  Electrophysiological classes of cat primary visual cortical neurons in vivo as revealed by quantitative analyses. , 2003, Journal of neurophysiology.

[50]  L. Palmer,et al.  Response to Contrast of Electrophysiologically Defined Cell Classes in Primary Visual Cortex , 2003, The Journal of Neuroscience.

[51]  Kenneth D Harris,et al.  Improving data quality in neuronal population recordings , 2016, Nature Neuroscience.

[52]  Frank W. Ohl,et al.  Normalization of Voltage-Sensitive Dye Signal with Functional Activity Measures , 2008, PloS one.

[53]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[54]  K. Obermayer,et al.  Analysis of Calcium Imaging Signals from the Honeybee Brain by Nonlinear Models , 2001, NeuroImage.

[55]  Aaditya V. Rangan,et al.  Architectural and synaptic mechanisms underlying coherent spontaneous activity in V1. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[56]  Johanna Ruescher,et al.  Traveling waves and trial averaging: The nature of single-trial and averaged brain responses in large-scale cortical signals , 2013, NeuroImage.

[57]  Wen-Jie Song,et al.  Separation of signal and noise from in vivo optical recording in Guinea pigs using independent component analysis , 2001, Neuroscience Letters.

[58]  Gabriel Peyré,et al.  Spatially Structured Sparse Morphological Component Separation for voltage-sensitive dye optical imaging , 2016, Journal of Neuroscience Methods.

[59]  Gholam-Ali Hossein-Zadeh,et al.  A signal subspace approach for modeling the hemodynamic response function in fMRI. , 2003, Magnetic resonance imaging.

[60]  Christian Igel,et al.  A Dynamic Neural Field Model of Mesoscopic Cortical Activity Captured with Voltage-Sensitive Dye Imaging , 2010, PLoS Comput. Biol..

[61]  Jian-Young Wu,et al.  Compression and Reflection of Visually Evoked Cortical Waves , 2007, Neuron.

[62]  James Gordon,et al.  Entrainment to Video Displays in Primary Visual Cortex of Macaque and Humans , 2004, The Journal of Neuroscience.

[63]  D. Kleinfeld,et al.  Visual stimuli induce waves of electrical activity in turtle cortex. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[64]  Alain Destexhe,et al.  A Master Equation Formalism for Macroscopic Modeling of Asynchronous Irregular Activity States , 2009, Neural Computation.

[65]  David J Heeger,et al.  Rapid and precise retinotopic mapping of the visual cortex obtained by voltage-sensitive dye imaging in the behaving monkey. , 2007, Journal of neurophysiology.

[66]  Frank W. Ohl,et al.  Flow detection of propagating waves with temporospatial correlation of activity , 2011, Journal of Neuroscience Methods.

[67]  F. Chavane,et al.  The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave , 2014, Nature Communications.

[68]  Anton V. Chizhov Conductance-based refractory density model of primary visual cortex , 2013, Journal of Computational Neuroscience.

[69]  Aaditya V. Rangan,et al.  Modeling the spatiotemporal cortical activity associated with the line-motion illusion in primary visual cortex. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[70]  A. Grinvald,et al.  Long-term voltage-sensitive dye imaging reveals cortical dynamics in behaving monkeys. , 2002, Journal of neurophysiology.

[71]  Risto Miikkulainen,et al.  Computational Maps in the Visual Cortex , 2005 .

[72]  Benjamin F. Grewe,et al.  High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor , 2015, Science.

[73]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[74]  A. Grinvald,et al.  Dynamics and Constancy in Cortical Spatiotemporal Patterns of Orientation Processing , 2002, Science.

[75]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .

[76]  Michael T. Lippert,et al.  Methods for voltage-sensitive dye imaging of rat cortical activity with high signal-to-noise ratio. , 2007, Journal of neurophysiology.

[77]  J. Bullier Integrated model of visual processing , 2001, Brain Research Reviews.

[78]  R. Douglas,et al.  A functional microcircuit for cat visual cortex. , 1991, The Journal of physiology.

[79]  Ian Nauhaus,et al.  Robustness of Traveling Waves in Ongoing Activity of Visual Cortex , 2012, The Journal of Neuroscience.

[80]  N. Hatsopoulos,et al.  Propagating waves mediate information transfer in the motor cortex , 2006, Nature Neuroscience.

[81]  Peter König,et al.  Independent encoding of grating motion across stationary feature maps in primary visual cortex visualized with voltage-sensitive dye imaging , 2011, NeuroImage.

[82]  Akitoshi Hanazawa,et al.  Cortical Dynamics Subserving Visual Apparent Motion , 2008, Cerebral cortex.

[83]  Thierry Blu,et al.  Wavelet-based multi-resolution statistics for optical imaging signals: Application to automated detection of odour activated glomeruli in the mouse olfactory bulb , 2007, NeuroImage.

[84]  K. Obermayer,et al.  Principal Component Analysis and Blind Separation of Sources for Optical Imaging of Intrinsic Signals , 2000, NeuroImage.

[85]  François Grimbert,et al.  Neural Field Model of VSD Optical Imaging Signals , 2007 .

[86]  A. Sornborger,et al.  Spatiotemporal analysis of optical imaging data , 2003, NeuroImage.

[87]  Guillaume S. Masson,et al.  Linear model decomposition for voltage-sensitive dye imaging signals: Application in awake behaving monkey , 2011, NeuroImage.

[88]  E. Seidemann,et al.  Optimal temporal decoding of neural population responses in a reaction-time visual detection task. , 2008, Journal of neurophysiology.

[89]  V. Lamme,et al.  The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.

[90]  Lyle J. Graham,et al.  Orientation and Direction Selectivity of Synaptic Inputs in Visual Cortical Neurons A Diversity of Combinations Produces Spike Tuning , 2003, Neuron.

[91]  D. Gabor,et al.  Theory of communication. Part 1: The analysis of information , 1946 .

[92]  F. Chavane,et al.  Dynamics of Local Input Normalization Result from Balanced Short- and Long-Range Intracortical Interactions in Area V1 , 2012, The Journal of Neuroscience.

[93]  Michael P. Stryker,et al.  New Paradigm for Optical Imaging Temporally Encoded Maps of Intrinsic Signal , 2003, Neuron.

[94]  L. Cohen,et al.  Optical monitoring of activity from many areas of the in vitro and in vivo salamander olfactory bulb: a new method for studying functional organization in the vertebrate central nervous system , 1983, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[96]  A. Grinvald,et al.  Imaging Cortical Dynamics at High Spatial and Temporal Resolution with Novel Blue Voltage-Sensitive Dyes , 1999, Neuron.

[97]  Y. Frégnac,et al.  Visual input evokes transient and strong shunting inhibition in visual cortical neurons , 1998, Nature.

[98]  R. Douglas,et al.  A Quantitative Map of the Circuit of Cat Primary Visual Cortex , 2004, The Journal of Neuroscience.

[99]  V. Bringuier,et al.  Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons. , 1999, Science.

[100]  F. Chavane,et al.  Input-output transformation in the visuo-oculomotor loop: comparison of real-time optical imaging recordings in V1 to ocular following responses upon center-surround stimulation. , 2007, Archives italiennes de biologie.

[101]  Kenneth D Harris,et al.  Towards reliable spike-train recordings from thousands of neurons with multielectrodes , 2012, Current Opinion in Neurobiology.

[102]  Camilo La Rota,et al.  Analyse de l'activité électrique multi-sites du cortex auditif chez le cobaye , 2003 .

[103]  Amiram Grinvald,et al.  Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo , 2016, Nature Communications.

[104]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[105]  Alex S. Ferecskó,et al.  Model‐based analysis of excitatory lateral connections in the visual cortex , 2006, The Journal of comparative neurology.

[106]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[107]  F. Chavane,et al.  Lateral Spread of Orientation Selectivity in V1 is Controlled by Intracortical Cooperativity , 2011, Front. Syst. Neurosci..

[108]  J Anthony Movshon,et al.  Putting big data to good use in neuroscience , 2014, Nature Neuroscience.

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

[110]  E. Kaplan,et al.  A Principal Components-Based Method for the Detection of Neuronal Activity Maps: Application to Optical Imaging , 2000, NeuroImage.

[111]  Subhojit Chakraborty,et al.  Differential dynamics of transient neuronal assemblies in visual compared to auditory cortex , 2007, Experimental Brain Research.

[112]  Amiram Grinvald,et al.  VSDI: a new era in functional imaging of cortical dynamics , 2004, Nature Reviews Neuroscience.

[113]  James S. Schwaber,et al.  Scattered-Light Imaging in Vivo Tracks Fast and Slow Processes of Neurophysiological Activation , 2001, NeuroImage.

[114]  Gustavo Deco,et al.  The role of multi-area interactions for the computation of apparent motion , 2010, NeuroImage.

[115]  E. Seidemann,et al.  Complex Dynamics of V1 Population Responses Explained by a Simple Gain-Control Model , 2009, Neuron.

[116]  D. McVea,et al.  Spontaneous cortical activity alternates between motifs defined by regional axonal projections , 2013, Nature Neuroscience.