The organization of orientation and spatial frequency in primary visual cortex

Abstract

Many studies have demonstrated that the primary visual cortex contains multiple functional maps of visual properties (e.g., ocular dominance, orientation preference, and spatial-frequency preference), but as yet no consistent picture has emerged as to how these maps are related to one another. Three divergent, prior opticalimaging studies of spatial frequency are reanalyzed and critiqued in this article. Evidence is presented that a nonstimulus-specific response biased the interpretation of results in previous studies. In addition to reexamining four prior cat experiments, we carried out one new experiment. Through the use of different methods and a careful removal of the nonspecific response, we are led in all instances to a unique view of cortical organization for spatialfrequency preference. In particular, we find little apparent evidence for a columnar organization for spatial frequency. The response recorded by each image pixel may be viewed as arising from an admixture of low-and high-spatial-frequency populations. For most pixels, the ratio of these populations is 1:1.optical imaging ͉ nonspecific response ͉ generalized indicator ͉ function analysis T he retinotopic map relates an element of visual space to an element of cortical tissue. Neurons in the primary visual cortex are known to be differentially sensitive to orientation, , and spatial frequency (SF), k, of corresponding image elements that appear in visual space. ‡ Thus, it may be said that the primary visual cortex maps visual space through a local Fourier analysis (1, 2). The process by which this mapping takes place, although not fully understood, is known to involve information processing by the retina, lateral geniculate nucleus (LGN), and primary visual cortex area V1 (V1) and feedback from higher areas.Casting aside the nature of neuronal circuitry, the functional layout of the primary visual cortex is well established for orientation preference and ocular dominance. Anatomical (3-5) and optical-imaging (6-9) studies have elaborated and extended the columnar view of V1 that emerged from electrophysiology (10, 11). Less can be said of the representation of SF. Extensive electrophysiological recordings (12-17) and optical-imaging experiments have not provided a consistent picture of SF layout. The situation can be further complicated if variations in the temporal properties of the stimuli are included (18).One series of optical-imaging studies (7,(19)(20)(21)(22) advanced the viewpoint that the orientation layout of V1 is organized into high-and low-SF preference columns. From this view, it follows that only a single map is needed to characterize SF. This map contains regions corresponding to high and low SF with opposite signs. Thus high-and low-SF stimuli result in responses of opposite signatures. An obvious shortcoming is that intermediate SF would lead to null or near-null results. On the other hand, Everson et al. (23) reported that SF preference is continuously distributed and that at least two maps are required to depict SF represen...

Functional Imaging of Primary Visual Cortex Using Flavoprotein Autofluorescence

“…5). A recent reanalysis of some ISI images suggested that certain cortical structures identified by ISI, like specific spatial frequency domains, tend to align with blood vessels (Sirovich and Uglesich, 2004), but because ISI relies on hemodynamic changes it is not clear whether these structures have a neural or vascular basis. Because flavoprotein autofluorescence is virtually independent of hemodynamics, AFI maps would be able to distinguish whether such correlations between functional structures and blood vessels are artifactual or genuine.…”

The Representation of Complex Images in Spatial Frequency Domains of Primary Visual Cortex

“…An analysis by Sirovich and Uglesich (2004) suggested that SF domains might be artifacts of intrinsic signal imaging and that SF preference does not vary with position along the cortical surface. However, a subsequent study using flavoprotein autofluorescence imaging, a metabolic but nonhemodynamic mapping signal, produced SF maps with statistically similar structure to those of intrinsic signal imaging, suggesting that the variations in SF preference measured optically are likely genuine (Husson et al, 2006).…”

Quantification of Optical Images of Cortical Responses for Inferring Functional Maps

“…The main aim of this study was to empirically determine the general scope of applicability of these methods to optical images and their limitations thereof. We also tested the resolving power of these methods by applying them to the problem of mapping spatiotemporal frequency preferences across the surface of the primary visual cortex (Basole et al 2003;Bonhoeffer et al 1995;Hubener et al 1997;Issa et al 2000;Khaytin et al 2008;Shoham et al 1997;Sirovich and Uglesich 2004;Xu et al 2007).…”

“…The main aim of this study was to empirically determine the general scope of applicability of these methods to optical images and their limitations thereof. We also tested the resolving power of these methods by applying them to the problem of mapping spatiotemporal frequency preferences across the surface of the primary visual cortex (Basole et al 2003;Bonhoeffer et al 1995;Hubener et al 1997;Issa et al 2000;Khaytin et al 2008;Shoham et al 1997;Sirovich and Uglesich 2004;Xu et al 2007).Optical imaging of intrinsic signals and voltage-sensitive dyes in vivo has made it possible to test models of cortical organization that could not be easily tested earlier (reviewed by Bonhoeffer and Grinvald 1996). For instance, computational modeling of the results of Hubel and Wiesel (1963) suggested a "pinwheel" pattern of arrangement for orientation maps (Braitenberg and Braitenberg 1979).…”

“…Building on past successes, recent studies have used optical imaging to investigate finer and more complex aspects of cortical feature maps. This has led to an increased recognition that the low signal-to-noise ratio (SNR) of optical images can be a major limiting factor (Everson et al 1998;Issa et al 2000;Khaytin et al 2008;Sirovich and Uglesich 2004). We applied methods based on signal-detection theory to analyze and interpret the weak signals captured by optical imaging.…”