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From spatial frequency contrast to edge preponderance: the differential modulation of early visual evoked potentials by natural scene stimuli

Published online by Cambridge University Press:  23 March 2011

BRUCE C. HANSEN*
Affiliation:
Departments of Psychology and Neuroscience Program, Colgate University, Hamilton, New York
THEODORE JACQUES
Affiliation:
Departments of Psychology and Neuroscience Program, Colgate University, Hamilton, New York
AARON P. JOHNSON
Affiliation:
Department of Psychology, Concordia University, Montréal, Quebec, Canada
DAVE ELLEMBERG
Affiliation:
Centre de recherche en neuropsychologie et cognition (CERNEC), Université de Montréal, Quebec, Canada
*
Address correspondence and reprint requests to: Bruce C. Hansen, Department of Psychology, Neuroscience Program, Colgate University, 107B Olin Hall, Hamilton, NY 13346. E-mail: [email protected]

Abstract

The contrast response function of early visual evoked potentials elicited by sinusoidal gratings is known to exhibit characteristic potentials closely associated with the processes of parvocellular and magnocellular pathways. Specifically, the N1 component has been linked with parvocellular processes, while the P1 component has been linked with magnocellular processes. However, little is known regarding the response properties of the N1 and P1 components during the processing and encoding of complex (i.e., broadband) stimuli such as natural scenes. Here, we examine how established physical characteristics of natural scene imagery modulate the N1 and P1 components in humans by providing a systematic investigation of component modulation as visual stimuli are gradually built up from simple sinusoidal gratings to highly complex natural scene imagery. The results suggest that the relative dominance in signal output of the N1 and P1 components is dependent on spatial frequency (SF) luminance contrast for simple stimuli up to natural scene imagery possessing few edges. However, such a dependency shifts to a dominant N1 signal for natural scenes possessing abundant edge content and operates independently of SF luminance contrast.

Type
Research Articles
Copyright
Copyright © Cambridge University Press 2011

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References

Adjamian, P., Holliday, I.E., Barnes, G.R., Hillebrand, A., Hadjipapas, A. & Singh, K.D. (2004). Induced visual illusions and gamma oscillations in human primary visual cortex. The European Journal of Neuroscience 20, 587592.CrossRefGoogle ScholarPubMed
Allison, J.D., Smith, K.R. & Bonds, A.B. (2001). Temporal-frequency tuning of cross-orientation suppression in the cat striate cortex. Visual Neuroscience 18, 941948.CrossRefGoogle ScholarPubMed
Anllo-Vento, L. & Hillyard, S.A. (1996). Selective attention to the color and direction of moving stimuli: Electrophysiological correlates of hierarchical feature selection. Perception and Psychophysics 58, 191206.CrossRefGoogle Scholar
Arakawa, K., Peachey, N.S., Celesia, G.G. & Rubboli, G. (1993). Component-specific effects of physostigmine on the cat visual-evoked potential. Experimental Brain Research 95, 271276.CrossRefGoogle ScholarPubMed
Bach, M. & Meigen, T. (1992). Electrophysiological correlates of texture segregation in the human visual evoked potential. Vision Research 32, 417424.CrossRefGoogle ScholarPubMed
Bach, M. & Meigen, T. (1997). Similar electrophysiological correlates of texture segregation induced by luminance, orientation, motion and stereo. Vision Research 37, 14091414.CrossRefGoogle ScholarPubMed
Bartels, A. & Zeki, S. (2004). Functional brain mapping during free viewing of natural scenes. Human Brain Mapping 21, 7585.CrossRefGoogle ScholarPubMed
Baseler, H.A. & Sutter, E.E. (1997). M and P components of the VEP and their visual field distribution. Vision Research 37, 675690.CrossRefGoogle ScholarPubMed
Bauman, L.A. & Bonds, A.B. (1991). Inhibitory refinement of spatial frequency selectivity in single cells of the cat striate cortex. Vision Research 31, 933944.Google Scholar
Benardete, E.A. & Kaplan, E. (1997 a). The receptive field of the primate P retinal ganglion cell, I: Linear dynamics. Visual Neuroscience 14, 169185.Google Scholar
Benardete, E.A. & Kaplan, E. (1997 b). The receptive field of the primate P retinal ganglion cell, II: Nonlinear dynamics. Visual Neuroscience 14, 187205.Google Scholar
Benardete, E.A., Kaplan, E. & Knight, B.W. (1992). Contrast gain control in the primate retina: P cells are not X-like, some M cells are. Visual Neuroscience 8, 483486.CrossRefGoogle ScholarPubMed
Bex, P.J. & Makous, W. (2002). Spatial frequency, phase, and the contrast of natural images. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 19, 10961106.CrossRefGoogle ScholarPubMed
Bex, P.J., Mareschal, I. & Dakin, S.C. (2007). Contrast gain control in natural scenes. Journal of Vision 7, 112.Google Scholar
Bex, P.J., Solomon, S.G. & Dakin, S.C. (2009). Contrast sensitivity in natural scenes depends on edge as well as spatial frequency structure. Journal of Vision 9, 1, 119.Google Scholar
Billock, V.A. (2000). Neural acclimation to 1/f spatial frequency spectra in natural images transduced by the human visual system. Physica D 137, 379391.CrossRefGoogle Scholar
Bodis-Wollner, I., Davis, J., Tzelepi, A. & Bezerianos, T. (2001). Wavelet transform of the EEG reveals differences in low and high gamma responses to elementary visual stimuli. Clinical EEG (Electroencephalography) 32, 139144.Google Scholar
Bonds, A.B. (1989). Role of inhibition in the specification of orientation selectivity of cells in the cat striate cortex. Visual Neuroscience 2, 4155.CrossRefGoogle ScholarPubMed
Bonin, V., Mante, V. & Carandini, M. (2006). The statistical computation underlying contrast gain control. The Journal of Neuroscience 26, 63466353.CrossRefGoogle ScholarPubMed
Burr, D.C. & Morrone, M.C. (1987). Inhibitory interactions in the human vision system revealed in pattern-evoked potentials. The Journal of Physiology 389, 121.CrossRefGoogle ScholarPubMed
Burton, G.J. & Moorhead, I.R. (1987). Color and spatial structure in natural scenes. Applied Optics 26, 157170.CrossRefGoogle ScholarPubMed
Carandini, M., Heeger, D.J. & Movshon, J.A. (1997). Linearity and normalization in simple cells of the macaque primary visual cortex. The Journal of Neuroscience 17, 86218644.Google Scholar
Celesia, G.G. (2005). Anatomy and physiology of the visual pathways and cortex. In Disorders of Visual Processing: Handbook of Clinical Neurophysiology, Vol. 5, ed. Celesia, G.G., Elsevier B.V. Newyork, NYGoogle Scholar
Clarencon, D., Renaudin, M., Gourmelon, P., Kerckhoeve, A., Caterini, R., Boivin, E., Ellis, P., Hille, B. & Fatôme, M. (1996). Real-time spike detection in EEG signals using the wavelet transform and a dedicated digital signal processor card. Journal of Neuroscience Methods 70, 514.Google Scholar
Dan, Y., Atick, J.J. & Reid, R.C. (1996). Efficient coding of natural scenes in the lateral geniculate nucleus: Experimental test of a computational theory. The Journal of Neuroscience 16, 33513362.Google Scholar
DeAngelis, G.C., Robson, J.G., Ohzawa, I. & Freeman, R.D. (1992). Organization of suppression in receptive fields of neurons in cat visual cortex. Journal of Neurophysiology 68, 144163.Google Scholar
Derrington, A.M. & Lennie, P. (1984). Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. The Journal of Physiology 357, 219240.CrossRefGoogle ScholarPubMed
Di Russo, F., Martinez, A., Sereno, M.I., Pitzalis, S. & Hillyard, S.A. (2002). Cortical sources of the early components of the visual evoked potential. Human Brain Mapping 15, 95111.CrossRefGoogle ScholarPubMed
Dong, D.W. & Atick, J.J. (1995). Statistics of natural time-varying images. Network: Computation in Neural Systems 6, 345358.Google Scholar
Ellemberg, D., Hammarrenger, B., Lepore, F., Roy, M.S. & Guillemot, J.P. (2001). Contrast dependency of VEPs as a function of spatial frequency: The parvocellular and magnocellular contributions to human VEPs. Spatial Vision 15, 99111.CrossRefGoogle ScholarPubMed
Fahle, M., Quenzer, T., Braun, C. & Spang, K. (2003). Feature-specific electrophysiological correlates of texture segregation. Vision Research 43, 719.CrossRefGoogle ScholarPubMed
Felsen, G., Touryan, J., Han, F. & Dan, Y. (2005). Cortical sensitivity to visual features in natural scenes. PLoS Biology 3, e342.CrossRefGoogle ScholarPubMed
Field, D.J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, Optics and Image Science 4, 23792394.Google Scholar
Field, D.J. (1993). Scale-invariance and self-similar ’wavelet’ transforms: An analysis of natural scenes and mammalian visual systems. In Wavelets, Fractals and Fourier Transforms: New Developments and New Applications, ed. Farge, M., Hunt, J.C.R. & Vassilicos, J.C., Oxford University Press, USA.Google Scholar
Field, D.J. & Brady, N. (1997). Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes. Vision Research 37, 33673383.CrossRefGoogle ScholarPubMed
Foxe, J.J., Strugstad, E.C., Sehatpour, P., Molholm, S., Pasieka, W., Schroeder, C.E. & McCourt, M.E. (2008). Parvocellular and magnocellular contributions to the initial generators of the visual evoked potential: High-density electrical mapping of the “C1” component. Brain Topography 21, 1121.Google Scholar
Freeman, T.C., Durand, S., Kiper, D.C. & Carandini, M. (2002). Suppression without inhibition in visual cortex. Neuron 35, 759771.Google Scholar
Fründ, I., Busch, N.A., Körner, U., Schadow, J. & Herrmann, C.S. (2007). EEG oscillations in the gamma and alpha range respond differently to spatial frequency. Vision Research 47, 20862098.Google Scholar
Garcia-Quispe, L.A., Gordon, J. & Zemon, V. (2009). Development of contrast mechanisms in humans: A VEP study. Optometry and Vision Science, 86(6), 708716.Google Scholar
Goupillaud, P.P., Grossmann, A. & Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23, 85102.CrossRefGoogle Scholar
Hansen, B.C. & Essock, E.A. (2004). A horizontal bias in human visual processing of orientation and its correspondence to the structural components of natural scenes. Journal of Vision 4, 10441060.Google Scholar
Hansen, B.C. & Essock, E.A. (2005). Influence of scale and orientation on the visual perception of natural scenes. Visual Cognition 12, 11991234.CrossRefGoogle Scholar
Hansen, B.C., Essock, E.A., Zheng, Y. & DeFord, J.K. (2003). Perceptual anisotropies in visual processing and their relation to natural image statistics. Network: Computation in Neural Systems 14, 501526.Google Scholar
Hansen, B.C. & Hess, R.F. (2006). Discrimination of amplitude spectrum slope in the fovea and parafovea and the local amplitude distributions of natural scene imagery. Journal of Vision 6, 696711.CrossRefGoogle ScholarPubMed
Hansen, B.C. & Hess, R.F. (2007). Structural sparseness and spatial phase alignment in natural scenes. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 24, 18731885.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1992 a). Half-squaring in responses of cat striate cells. Visual Neuroscience 9, 427443.Google Scholar
Heeger, D.J. (1992 b). Normalization of cell responses in cat striate cortex. Visual Neuroscience 9, 181197.Google Scholar
Herrmann, C.S., Grigutsch, M. & Busch, N.A. (2005). EEG oscillations and wavelet analysis. In Event-Related Potentials: A Methods Handbook, ed. Handy, T.C., Cambridge, MA: MIT Press.Google Scholar
Hubel, D.H. & Livingstone, M.S. (1987). Segregation of form, color, and stereopsis in primate area 18. The Journal of Neuroscience 7, 33783415.Google Scholar
Kaplan, E. (2003). The M, P, and K pathways of the primate visual system. In The Visual Neurosciences, ed. Chalupa, L.M. & Werner, J.S., Cambridge, MA: MIT Press.Google Scholar
Knill, D.C., Field, D. & Kersten, D. (1990). Human discrimination of fractal images. Journal of the Optical Society of America A, Optics and Image Science 7, 11131123.CrossRefGoogle ScholarPubMed
Kretzmer, E.R. (1952). The statistics of television signals. The Bell System Technical Journal 31, 751763.CrossRefGoogle Scholar
Kubová, Z., Kuba, M., Spekreijse, H. & Blakemore, C. (1995). Contrast dependence of motion-onset and pattern-reversal evoked-potentials. Vision Research 35, 197205.Google Scholar
Maldonado, P.E. & Babul, C.M. (2007). Neuronal activity in the primary visual cortex of the cat freely viewing natural images. Neuroscience 144, 15361543.Google Scholar
Mante, V., Frazor, R.A., Bonin, V., Geisler, W.S. & Carandini, M. (2005). Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neuroscience 8, 16901697.CrossRefGoogle ScholarPubMed
Mathes, B. & Fahle, M. (2007). The electrophysiological correlate of contour integration is similar for color and luminance mechanisms. Psychophysiology 44, 305322.CrossRefGoogle ScholarPubMed
Mecklinger, A. & Muller, N. (1996). Dissociations in the processing of ‘what’ and ‘where’ information in working memory: An event-related potential analysis. Journal of Cognitive Neuroscience 8, 453473.CrossRefGoogle ScholarPubMed
Merigan, W.H. & Eskin, T.A. (1986). Spatio-temporal vision of macaques with severe loss of P beta retinal ganglion cells. Vision Research 26, 17511761.Google Scholar
Merigan, W.H. & Maunsell, J.H. (1993). How parallel are the primate visual pathways? Annual Review of Neuroscience 16, 369402.CrossRefGoogle ScholarPubMed
Morrone, M.C. & Burr, D.C. (1988). Feature detection in human vision: A phase-dependent energy model. Proceedings of the Royal Society of London. Series B, Biological Sciences 235, 221245.Google Scholar
Morrone, M.C., Burr, D.C. & Maffei, L. (1982). Functional implications of cross-orientation inhibition of cortical visual cells. I. Neurophysiological evidence. Proceedings of the Royal Society of London. Series B, Biological Sciences 216, 335354.Google Scholar
Morrone, M.C. & Owens, R.A. (1987). Feature detection from local energy. Pattern Recognition Letters 6, 303313.CrossRefGoogle Scholar
Murray, I.J., Parry, N.R.A., Carden, D. & Kulikowski, J.J. (1987). Human visual evoked potentials to chromatic and achromatic gratings. Clinical Vision Science 1, 231244.Google Scholar
Odom, J.V., Bach, M., Brigell, M., Holder, G.E., McCulloch, D.L., Tormene, A.P., et al. (2010). ISCEV standard for clinical visual evoked potentials (2009 update). Documenta Ophthalmologica. Advances in Ophthalmology 120, 111119.CrossRefGoogle ScholarPubMed
Oliva, A. & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42, 145175.Google Scholar
Olman, C.A., Ugurbil, K., Schrater, P. & Kersten, D. (2004). BOLD fMRI and psychophysical measurements of contrast response to broadband images. Vision Research 44, 669683.CrossRefGoogle ScholarPubMed
Párraga, C.A. & Tolhurst, D.J. (2000). The effect of contrast randomisation on the discrimination of changes in the slopes of the amplitude spectra of natural scenes. Perception 29, 11011116.CrossRefGoogle ScholarPubMed
Párraga, C.A., Troscianko, T. & Tolhurst, D.J. (2000). The human visual system is optimised for processing the spatial information in natural visual images. Current Biology 10, 3538.Google Scholar
Párraga, C.A., Troscianko, T. & Tolhurst, D.J. (2005). The effects of amplitude-spectrum statistics on foveal and peripheral discrimination of changes in natural images, and a multi-resolution model. Vision Research 45, 31453168.CrossRefGoogle Scholar
Porciatti, V. & Sartucci, F. (1999). Normative data for onset VEPs to red-green and blue-yellow chromatic contrast. Clinical Neurophysiology 110, 772781.CrossRefGoogle ScholarPubMed
Previc, F.H. (1988). The neurophysiological significance of the N1 and P1 components of the visual evoked potential. Clinical Vision Science 3, 195202.Google Scholar
Rainer, G., Augath, M., Trinath, T. & Logothetis, N.K. (2001). Nonmonotonic noise tuning of BOLD fMRI signal to natural images in the visual cortex of the anesthetized monkey. Current Biology 11, 846854.Google Scholar
Ruderman, D.L. & Bialek, W. (1994). Statistics of natural images: Scaling in the woods. Physical Review Letters 73, 814817.Google Scholar
Rudvin, I., Valberg, A. & Kilavik, B.E. (2000). Visual evoked potentials and magnocellular and parvocellular segregation. Visual Neuroscience 17, 579590.Google Scholar
Samar, V.J., Bopardikar, A., Rao, R. & Swartz, K. (1999). Wavelet analysis of neuroelectric waveforms: A conceptual tutorial. Brain and Language 66, 760.Google Scholar
Scholte, H.S., Ghebreab, S., Waldorp, L., Smeulders, A.W.M. & Lamme, V.A.F. (2009). Brain responses strongly correlate with Weibull image statistics when processing natural images. Journal of Vision 9, 115.CrossRefGoogle ScholarPubMed
Schwartz, O. & Simoncelli, E.P. (2001). Natural signal statistics and sensory gain control. Nature Neuroscience 4, 819825.Google Scholar
Sengpiel, F. & Blakemore, C. (1994). Interocular control of neuronal responsiveness in cat visual cortex. Nature 368, 847850.Google Scholar
Simoncelli, E.P. & Olshausen, B.A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience 24, 11931216.Google Scholar
Solomon, S.G., White, A.J. & Martin, P.R. (2002). Extraclassical receptive field properties of parvocellular, magnocellular, and koniocellular cells in the primate lateral geniculate nucleus. The Journal of Neuroscience 22, 338349.CrossRefGoogle ScholarPubMed
Straube, S., Grimsen, C. & Fahle, M. (2010). Electrophysiological correlates of figure-ground segregation directly reflect perceptual saliency. Vision Research 50, 509521.Google Scholar
Tadmor, Y. & Tolhurst, D.J. (1994). Discrimination of changes in the second-order statistics of natural and synthetic images. Vision Research 34, 541554.CrossRefGoogle ScholarPubMed
Tallon-Baudry, C., Bertrand, O., Peronnet, F. & Pernier, J. (1998). Induced gamma-band activity during the delay of a visual short-term memory task in humans. The Journal of Neuroscience 18, 42444254.Google Scholar
Thomson, M.G.A. & Foster, D.H. (1997). Role of second- and third-order statistics in the discriminability of natural images. Journal of the Optical Society of America. A, Optics and Image Science 14, 20812090.CrossRefGoogle Scholar
Tobimatsu, S., Tomoda, H. & Kato, M. (1995). Parvocellular and magnocellular contributions to visual-evoked potentials in humans - stimulation with chromatic and achromatic gratings and apparent motion. Journal of the Neurological Sciences 134, 7382.CrossRefGoogle ScholarPubMed
Tolhurst, D.J. & Tadmor, Y. (1997). Band-limited contrast in natural images explains the detectability of changes in the amplitude spectra. Vision Research 37, 32033215.Google Scholar
Tolhurst, D.J. & Tadmor, Y. (2000). Discrimination of spectrally blended natural images: Optimisation of the human visual system for encoding natural images. Perception 29, 10871100.Google Scholar
Tolhurst, D.J., Smyth, D. & Thompson, I.D. (2009). The sparseness of neuronal responses in ferret primary visual cortex. The Journal of Neuroscience 29, 23552370.CrossRefGoogle ScholarPubMed
Tolhurst, D.J., Tadmor, Y. & Tang, C. (1992). Amplitude spectra of natural images. Ophthalmic and Physiological Optics 12, 229232.Google Scholar
Tootell, R.B., Hamilton, S.L. & Switkes, E. (1988). Functional anatomy of macaque striate cortex. IV. Contrast and magno-parvo streams. The Journal of Neuroscience 8, 15941609.Google Scholar
Torralba, A. & Oliva, A. (2003). Statistics of natural image categories. Network: Computation in Neural Systems 14, 391412.Google Scholar
Torrence, C. & Compo, G.P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79, 6178.2.0.CO;2>CrossRefGoogle Scholar
Tucker, D.M. (1993). Spatial sampling of head electrical fields - the geodesic sensor net. Electroencephalography and Clinical Neurophysiology 87, 154163.Google Scholar
Valberg, A. & Rudvin, I. (1997). Possible contributions of magnocellular- and parvocellular-pathway cells to transient VEPs. Visual Neuroscience 14, 111.Google Scholar
van der Schaaf, A. & van Hateren, J.H. (1996). Modeling the power spectra of natural images: Statistics and information. Vision Research 36, 27592770.CrossRefGoogle ScholarPubMed
Vassilev, A., Stomonyakov, V. & Manahilov, V. (1994). Spatial-frequency specific contrast gain and flicker masking of human transient VEP. Vision Research 34, 863872.Google Scholar
Vinje, W.E. & Gallant, J.L. (2000). Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 12731276.Google Scholar
Webster, M.A. & Miyahara, E. (1997). Contrast adaptation and the spatial structure of natural images. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 14, 23552366.Google Scholar
Weliky, M., Fiser, J., Hunt, R.H. & Wagner, D.N. (2003). Coding of natural scenes in primary visual cortex. Neuron 37, 703718.Google Scholar
Wilson, H.R. & Humanski, R. (1993). Spatial frequency adaptation and contrast gain control. Vision Research 33, 11331149.Google Scholar
Zemon, V. & Gordon, J. (2006). Luminance-contrast mechanisms in humans: Visual evoked potentials and a nonlinear model. Vision Research 46, 41634180.Google Scholar
Zemon, V., Kaplan, E. & Ratliff, F. (1980). Bicuculline enhances a negative component and diminishes a positive component of the visual evoked cortical potential in the cat. Proceedings of the National Academy of Sciences of the United States of America 77, 74767478.CrossRefGoogle ScholarPubMed
Zemon, V. & Ratliff, F. (1982). Visual evoked potentials: Evidence for lateral interactions. Proceedings of the National Academy of Sciences of the United States of America 79, 57235726.CrossRefGoogle ScholarPubMed
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