Feasibility of functional magnetic resonance imaging of ocular dominance and orientation preference in primary visual cortex

A recent hemodynamic model is extended and applied to simulate and explore the feasibility of detecting ocular dominance (OD) and orientation preference (OP) columns in primary visual cortex by means of functional magnetic resonance imaging (fMRI). The stimulation entails a short oriented bar stimulus being presented to one eye and mapped to cortical neurons with corresponding OD and OP selectivity. Activated neurons project via patchy connectivity to excite other neurons with similar OP in nearby visual fields located preferentially along the direction of stimulus orientation. The resulting blood oxygen level dependent (BOLD) response is estimated numerically via the model’s spatiotemporal hemodynamic response function. The results are then used to explore the feasibility of detecting spatial OD-OP modulation, either directly measuring BOLD or by using Wiener deconvolution to filter the image and estimate the underlying neural activity. The effect of noise is also considered and it is estimated that direct detection can be robust for fMRI resolution of around 0.5 mm, whereas detection with Wiener deconvolution is possible at a broader range from 0.125 mm to 1 mm resolution. The detection of OD-OP features is strongly dependent on hemodynamic parameters, such as low velocity and high damping reduce response spreads and result in less blurring. The short-bar stimulus that gives the most detectable response is found to occur when neural projections are at 45 relative to the edge of local OD boundaries, which provides a constraint on the OD-OP architecture even when it is not fully resolved.

[1]  V. Braitenberg,et al.  Geometry of orientation columns in the visual cortex , 1979, Biological Cybernetics.

[2]  W. Singer,et al.  Stimulus‐Dependent Neuronal Oscillations in Cat Visual Cortex: Inter‐Columnar Interaction as Determined by Cross‐Correlation Analysis , 1990, The European journal of neuroscience.

[3]  K. Obermayer,et al.  Geometry of orientation and ocular dominance columns in monkey striate cortex , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  P A Robinson,et al.  Shock-like haemodynamic responses induced in the primary visual cortex by moving visual stimuli , 2016, Journal of The Royal Society Interface.

[5]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[6]  Mark D. Bird,et al.  MRI and MRS of the human brain at magnetic fields of 14T to 20T: Technical feasibility, safety, and neuroscience horizons , 2017, NeuroImage.

[7]  Essa Yacoub,et al.  High-field fMRI unveils orientation columns in humans , 2008, Proceedings of the National Academy of Sciences.

[8]  P. A. Robinson,et al.  Biophysically based method to deconvolve spatiotemporal neurovascular signals from fMRI data , 2018, Journal of Neuroscience Methods.

[9]  P A Robinson,et al.  Spatiotemporal BOLD dynamics from a poroelastic hemodynamic model. , 2010, Journal of theoretical biology.

[10]  G. Blasdel,et al.  Orientation selectivity, preference, and continuity in monkey striate cortex , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[11]  P. A. Robinson,et al.  Gamma-Band Correlations in Primary Visual Cortex , 2018, bioRxiv.

[12]  A. Grinvald,et al.  Relationships between orientation-preference pinwheels, cytochrome oxidase blobs, and ocular-dominance columns in primate striate cortex. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[13]  P A Robinson,et al.  Response-mode decomposition of spatio-temporal haemodynamics , 2016, Journal of The Royal Society Interface.

[14]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

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

[16]  Peter A. Robinson,et al.  BOLD responses to stimuli: Dependence on frequency, stimulus form, amplitude, and repetition rate , 2006, NeuroImage.

[17]  Essa Yacoub,et al.  Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns , 2011, NeuroImage.

[18]  Michael Breakspear,et al.  Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function , 2014, NeuroImage.

[19]  G. Glover Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.

[20]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

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

[22]  Peter A. Robinson,et al.  Deconvolution analysis of target evoked potentials , 2009, Journal of Neuroscience Methods.

[23]  G. Glover,et al.  Physiological noise in oxygenation‐sensitive magnetic resonance imaging , 2001, Magnetic resonance in medicine.

[24]  K. Götz Do “d-blob” and “l-blob” hypercolumns tessellate the monkey visual cortex? , 1987, Biological Cybernetics.

[25]  Amir Shmuel,et al.  Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns , 2018, NeuroImage.

[26]  R. Buxton,et al.  Estimation of respiration‐induced noise fluctuations from undersampled multislice fMRI data † , 2001, Magnetic resonance in medicine.

[27]  Lawrence C. Sincich,et al.  Complete Pattern of Ocular Dominance Columns in Human Primary Visual Cortex , 2007, The Journal of Neuroscience.

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

[29]  D. Hubel,et al.  Uniformity of monkey striate cortex: A parallel relationship between field size, scatter, and magnification factor , 1974, The Journal of comparative neurology.

[30]  D. Hubel,et al.  Sequence regularity and geometry of orientation columns in the monkey striate cortex , 1974, The Journal of comparative neurology.

[31]  D. Hubel,et al.  Laminar and columnar distribution of geniculo‐cortical fibers in the macaque monkey , 1972, The Journal of comparative neurology.

[32]  P A Robinson,et al.  Spatiotemporal hemodynamic response functions derived from physiology. , 2014, Journal of theoretical biology.

[33]  Essa Yacoub,et al.  Robust detection of ocular dominance columns in humans using Hahn Spin Echo BOLD functional MRI at 7 Tesla , 2007, NeuroImage.

[34]  S. Treitel PREDICTIVE DECONVOLUTION: THEORY AND PRACTICE , 1969 .

[35]  J. Cowan,et al.  The functional geometry of local and horizontal connections in a model of V1 , 2003, Journal of Physiology-Paris.

[36]  Amiram Grinvald,et al.  Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns , 1991, Nature.

[37]  Essa Yacoub,et al.  Mechanisms underlying decoding at 7 T: Ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye , 2010, NeuroImage.

[38]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[39]  R. Shapley,et al.  Information Tuning of Populations of Neurons in Primary Visual Cortex , 2004, The Journal of Neuroscience.

[40]  Jeff H. Duyn,et al.  The future of ultra-high field MRI and fMRI for study of the human brain , 2012, NeuroImage.

[41]  D. Fitzpatrick,et al.  Orientation Selectivity and the Arrangement of Horizontal Connections in Tree Shrew Striate Cortex , 1997, The Journal of Neuroscience.

[42]  Lawrence L. Wald,et al.  Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters , 2005, NeuroImage.

[43]  Terrence J. Sejnowski,et al.  Self-Organizing Map Formation: Foundations of Neural Computation , 2001 .