Abstract
Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1-V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1-V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas.NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1-V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex.
Highlights
NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3
Consistent with previous studies (Clifford et al 2009; Freeman et al 2011, 2013; Sasaki et al 2006), we found a large-scale radial bias for orientation that could largely account for decoding of orientation in V1–V3
Whereas anatomical studies in nonhuman primates have found evidence for nasotemporal differences in eye preference (Horton and Hocking 1996; Tychsen and Burkhalter 1997), it has not been previously demonstrated in human visual cortex, nor has it been shown that such a bias can account for classification of eye of origin, the possibility that a nasotemporal bias might drive classification was suggested by Shmuel et al (2010)
Summary
NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3. It was originally proposed that the response biases driving classification are attributable to biased sampling of columnar structures at smaller scales than voxels (Haynes and Rees 2005; Kamitani and Tong 2005), this assumption has been challenged by more recent studies showing the existence of large-scale patterns of response bias for orientation (Freeman et al 2011, 2013) and motion direction (Beckett et al 2012), which can account for decoding these features It has been hypothesized (Shmuel et al 2010) that decoding of eye of origin could rely on a large-scale preference for the contralateral eye found in nonhuman primates (Horton and Hocking 1996; Tychsen and Burkhalter 1997), but this conjecture has not been explicitly tested. The problem of inferring neuronal response properties from blood oxygen level-dependent (BOLD) fMRI signals is not unique to MVPA approaches, quantitative interpretation of MVPA results is made difficult by the complex dependence of decoding performance on the spatial distribution of BOLD responses (Chaimow et al 2011)
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