Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex

Abstract

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification.

Distributed Patterns of Activity in Sensory Cortex Reflect the Precision of Multiple Items Maintained in Visual Short-Term Memory

“…To examine this question, we specifically focused on within-subject changes across load (see Methods), because there may be sources of error that affect between-subject classification performance that are unrelated to differences in behavior (e.g., differences in encoding strategies, Linke et al, 2011b; Vicente-Grabovetsky et al, 2012, or individual differences in signal-to-noise ratio of BOLD signal, Tong et al, 2012). This analysis revealed that, across loads, the observed decrease in peak classifier sensitivity is significantly correlated with an individual’s change in mnemonic precision, r = .58, p = .006.…”

What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis

“…For contexts in which there are voxel-level interactions with experimental variables (e.g., some voxels in an ROI do not activate), as would likely occur in many ROIs, increasing the between-subject variability in mean activation may also increase voxel-level variability and the between-subject variability of the voxel-level variances (the τ matrix). Thus, in cases of voxel-by-condition interactions, group-level MVPA may be affected by between-subject variability in activation (see e.g., LaRocque et al 2013; Smith et al 2011; Tong et al 2012). Because of the infinite ways that voxel-by-condition interactions can manifest, it is not possible to give precise a priori predictions for how they impact MVPA in all contexts.…”

fMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli

“…It is possible that differences in orientation decodability resulted from differences in the signal-to-noise ratio of V1 responses across stimulus types (Tong et al, 2012). To test for this possibility, we compared the average % signal change in V1 across all stimulus types.…”