Recent studies suggested that fMRI voxel patterns can convey information represented in columnar-scale neuronal population codes, even when spatial resolution is insufficient to directly image the patterns of columnar selectivity (Kamitani and Tong, 2005; Haynes and Rees, 2005). Sensitivity to subvoxel-scale pattern information, or "fMRI hyperacuity," would greatly enhance the power of fMRI when combined with pattern information analysis techniques (Kriegeskorte and Bandettini, 2007). An individual voxel might weakly reflect columnar-level information if the columns within its boundaries constituted a slightly unbalanced sample of columnar selectivities (Kamitani and Tong, 2005), providing a possible mechanism for fMRI hyperacuity. However, Op de Beeck (2009) suggests that a coarse-scale neuronal organization rather than fMRI hyperacuity may explain the presence of the information in the fMRI patterns. Here we argue (a) that the present evidence does not rule out fMRI hyperacuity, (b) that the mechanism originally suggested for fMRI hyperacuity (biased sampling by averaging within each voxel's boundaries; Kamitani and Tong, 2005) will only produce very weak sensitivity to fine-grained pattern information, and (c) that an alternative mechanism (voxel as complex spatiotemporal filter) is physiologically more accurate and promises stronger sensitivity to fine-grained pattern information: We know that each voxel samples the neuronal activity pattern through a unique fine-grained structure of venous vessels that supply its blood oxygen level-dependent signal. At the simplest level, the drainage domain of a venous vessel may sample the neuronal pattern with a selectivity bias (Gardner, 2009; Shmuel et al., 2009). Beyond biased drainage domains, we illustrate with a simple simulation how temporal properties of the hemodynamics (e.g., the speed of the blood in the capillary bed) can shape spatial properties of a voxel's filter (e.g., how finely structured it is). This suggests that a voxel, together with its signal-supplying vasculature, may best be thought of as a complex spatiotemporal filter. Such a filter may well have greater sensitivity to high spatial frequencies than the Gaussian or averaging-box kernels typically invoked to characterize voxel sampling (compact kernels, both of which would act like anti-aliasing filters that minimize such sensitivity). Importantly, the complex-spatiotemporal-filter hypothesis of fMRI hyperacuity can account for the observed robustness to slight shifts of the voxel grid caused by head motion: Because the fine-grained components of the filter are vascular, they will remain in a constant relationship to the neuronal patterns sampled as the voxel grid is slightly shifted.
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