Journal of VisionNeurosciencePeer-reviewed journal article
Spatial Frequency Maps in Human Visual Cortex: A Replication and Extension
Jiyeong HaiD, William F. BroderickiD, Kendrick KayiD, Jonathan A. WinaweriD
Abstract
In a step toward developing a model of human primary visual cortex, a recent study introduced a model of spatial frequency tuning in V1 (Broderick et al., 2022). The model is compact, using just 9 parameters to predict BOLD response amplitude for locations across all of V1 as a function of stimulus orientation and spatial frequency. Here we replicated this analysis in a new dataset, the nsdsynthetic supplement to the Natural Scenes Dataset (Allen et al., 2022), to assess generalization of model parameters. Furthermore, we extended the analyses to extrastriate maps V2 and V3. For each retinotopic map in the 8 NSD subjects, we fit the 9-parameter model. Despite many experimental differences between NSD and the original study, including stimulus size, experimental design, and MR field strength, there was good agreement in most model parameters. The dependence of preferred spatial frequency on eccentricity in V1 was similar between NSD and Broderick et al. Moreover, the effect of absolute stimulus orientation on spatial frequency maps was similar: higher preferred spatial frequency for horizontal and cardinal orientations compared to vertical and oblique orientations in both studies. The extension to extrastriate maps revealed that the biggest change in tuning between maps was in bandwidth: the bandwidth in spatial frequency tuning increased by 70% from V1 to V2 and 100% from V1 to V3. This implies that higher visual areas are sensitive to a greater range of stimulus spatial frequencies, paralleling the fact that higher visual areas are sensitive to a greater range in location (i.e., larger receptive fields). Together, the results show robust reproducibility and bring us closer to a systematic characterization of spatial encoding in the human visual system.
- organism: human
- assay: imaging
- research focus: benchmarking
- research focus: spatial domain detection
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
- FWCI
- 0.45
- Field percentile
- top 42%
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