Multiscale Pattern Analysis of Orientation-Selective Activity in the Primary Visual Cortex

Abstract

Although orientation columns are less than a millimeter in width, recent neuroimaging studies indicate that viewed orientations can be decoded from cortical activity patterns sampled at relatively coarse resolutions of several millimeters. One proposal is that these differential signals arise from random spatial irregularities in the columnar map. However, direct support for this hypothesis has yet to be obtained. Here, we used high-field, high-resolution functional magnetic resonance imaging (fMRI) and multivariate pattern analysis to determine the spatial scales at which orientation-selective information can be found in the primary visual cortex (V1) of cats and humans. We applied a multiscale pattern analysis approach in which fine-and coarse-scale signals were first removed by ideal spatial lowpass and highpass filters, and the residual activity patterns then analyzed by linear classifiers. Cat visual cortex, imaged at 0.3125 mm resolution, showed a strong orientation signal at the scale of individual columns. Nonetheless, reliable orientation bias could still be found at spatial scales of several millimeters. In the human visual cortex, imaged at 1 mm resolution, a majority of orientation information was found on scales of millimeters, with small contributions from global spatial biases exceeding ϳ1 cm. Our high-resolution imaging results demonstrate a reliable millimeters-scale orientation signal, likely emerging from irregular spatial arrangements of orientation columns and their supporting vasculature. fMRI pattern analysis methods are thus likely to be sensitive to signals originating from other irregular columnar structures elsewhere in the brain.

Prospective motion correction improves the sensitivity of fMRI pattern decoding

“…Post hoc multiple comparisons also showed that regions with radial bias generates the largest LDC, in line with studies by Freeman et al (2013) and Tong et al (2010) which suggested that decoding in the visual cortex is strongly driven by radial bias rather than more fine‐grained response patterns. There was also a numerical trend that improvements due to PMC were stronger in non‐radial‐bias V1 subregions, where the spatial activation patterns may have been expected to be more fine grained.…”

“…Visual gratings were chosen as stimuli because their encoding in visual cortical response patterns is reasonably well understood. In particular, we examined the accuracy of multivariate decoding in the primary visual cortex (V1), which is generally high (Alink, Krugliak, Walther, & Kriegeskorte, 2013; Kamitani & Tong, 2005; Tong et al, 2010). …”

“…Specifically, neurons responding to radial orientations with respect to the fixation point appear to be more frequent, creating a global areal map of radial orientation frequencies (Freeman, Heeger, & Merriam, 2013; Sasaki et al, 2006). Neurons responding independently of radial bias are organized in a much finer‐grained columnar map of orientation preference (Alink et al, 2013; Swindale, Grinvald, & Shmuel, 2003; Tong et al, 2010). The different spatial frequencies of these two nested organizations leads us to expect that that the effectiveness of PMC may vary with the spatial scale of the fMRI measurement, and with the visual field coverage of a given region of interest.…”

Population Response Profiles in Early Visual Cortex Are Biased in Favor of More Valuable Stimuli

“…Thus VTFs were used to evaluate the influence of value (low vs. high) on the orientation selectivity of individual voxels within early regions of visual cortex. This analysis rests on the assumption that the distribution of feature-selective neurons-in this case the distribution of orientation-selective columns-is not uniform across a given visual area (Boynton 2005a;Kamitani and Tong 2005;Swisher et al 2010). Due to this nonuniform distribution of neural selectivity, a given voxel may contain more neurons tuned to one particular orientation, giving rise to a Gaussian-shaped response profile across orientations, which we refer to as the VTF (Serences et al 2009; see also Kamitani and Tong 2005;Kay et al 2008;Miyawaki et al 2008).…”

From What Scale of Representation Does Multivariate Pattern Analysis Decode Information?

“…If orientation decoding really depends on fine-scale variability in the distribution of columns, it is unclear how this signal could still be detected when blurred via smoothing. Swisher et al (2010) attempted to resolve this question regarding the scale of representation that contributes to a successful orientation classification by testing classification performance with high-resolution fMRI in cats (9.4 T) and humans (7 T) who viewed oriented gratings. In the fMRI data, the authors considered classification performance by applying high-or low-pass spatial filters, enabling them to determine the contribution of different spatial scales to successful multivariate classification analysis.…”