Multivoxel fMRI analysis of color tuning in human primary visual cortex

Abstract

We use multivoxel pattern analysis (MVPA) to study the spatial clustering of color-selective neurons in the human brain. Our main objective was to investigate whether MVPA reveals the spatial arrangements of color-selective neurons in human primary visual cortex (V1). We measured the distributed fMRI activation patterns for different color stimuli (Experiment 1: cardinal colors (to which the LGN is known to be tuned), Experiment 2: perceptual hues) in V1. Our two main findings were that (i) cone-opponent cardinal color modulations produce highly reproducible patterns of activity in V1, but these were not unique to each color. This suggests that V1 neurons with tuning characteristics similar to those found in LGN are not spatially clustered. (ii) Unique activation patterns for perceptual hues in V1 support current evidence for a spatially clustered hue map. We believe that our work is the first to show evidence of spatial clustering of neurons with similar color preferences in human V1.

Transformation from image-based to perceptual representation of materials along the human ventral visual pathway

“…To this end, we used pattern-information analysis to investigate the representational content of a region by assessing the information carried in that region's pattern of activity (Edelman et al, 1998;Kriegeskorte et al, 2008;Mur et al, 2009). A number of studies have demonstrated that various basic categories of objects (e.g., Cox andNeuroImage 57 (2011) 482-494 Savoy, 2003;Haxby et al, 2001), scenes (Peelen et al, 2009;Walther et al, 2009), facial expressions (Said et al, 2010), vocal emotions (Ethofer et al, 2009), odors (Howard et al, 2009) etc., as well as simple visual features like orientation (e.g., Haynes and Rees, 2005;Kamitani and Tong, 2005) and color (Brouwer and Heeger, 2009;Parkes et al, 2009), can be decoded from the pattern of brain activity. Moreover, by analyzing the patterns of activity in various brain regions, recent studies have been able to show that higher-order visual areas represent objects categorically and hierarchically (Kriegeskorte et al, 2008) and represent objects within categories or with particular shapes in perceptually relevant ways (Haushofer et al, 2008;Op de Beeck et al, 2008Weber et al, 2009).…”

Detection of primary RGB colors projected on a screen using fNIRS

“…However, Brouwer and Heeger 5 found that the transition from cone-opponent color coding to perceptual color coding began at V1 and that the activations were strongest at V4 and VO1. Similarly, Parkes et al, 6 having measured the fMRI BOLD responses to°ickering radial patterns composed of perceptual colors (red, green, yellow, blue), found that the BOLD responses evoked were similar, even though the individual di®erences with respect to each color pattern were su±ciently reliable to predict the color viewed. Mullen et al, 7 having measured the fMRI BOLD response in the visual cortex to radial chromatic red/green and yellow/ blue grating patterns di®ering either in cone activation or in multiples of detection threshold, determined that the amplitude of the BOLD response was not linearly related to either measure.…”

“…It should come as no surprise, then, that color stimulation and detection in the visual cortex have been the subjects of long-standing study and debate. [1][2][3][4][5][6][7][8][9][10][11][12][13] The objective of the present study is to characterize appropriate features of the hemodynamic response (HR) signals obtained from the visual cortex upon color stimuli. Five di®erent features (i.e., mean, slope, peak, skewness, and kurtosis) of the obtained HR signals are examined in order to classify the three di®erent color stimuli (i.e., red, green, and blue (RGB)).…”

Decoding and encoding of visual patterns using magnetoencephalographic data represented in manifolds

“…When modeling this relationship, therefore, multivariate analysis of functional neuroimaging data can be adopted to ensure a comprehensive consideration of the information related to all voxels in the visual cortex. Multi-voxel pattern analysis (MVPA) was demonstrated to be capable of distinguishing among activity patterns revealed by multiple voxels and estimating the contribution of voxels to specific stimuli Haynes and Rees, 2006;Liang et al, 2013;Norman et al, 2006;Parkes et al, 2009). Voxels with high discriminative ability were regarded as important features spanning the subspace of task-related brain responses and were obtained by a supervised learning process in MVPA.…”