Cortical Magnification in Human Visual Cortex Parallels Task Performance around the Visual Field

Carrasco

et al. 2020

Preprint

Self Cite

Visual performance varies around the visual field. It is best near the fovea compared to the periphery, and at iso-eccentric locations it is best on the horizontal, intermediate on the lower, and poorest on the upper meridian. The fovea-to-periphery performance decline is linked to the decreases in cone density, retinal ganglion cell (RGC) density, and V1 cortical magnification factor (CMF) as eccentricity increases. The origins of radial asymmetries are not well understood. Optical quality and cone density vary across the retina, but recent computational modeling has shown that these factors can only account for a small percentage of behavior. Here, we investigate how visual processing beyond the cones contributes to radial asymmetries in performance. First, we quantify the extent of asymmetries in cone density, midget RGC density, and V1-V2 CMF. We find that both radial asymmetries and eccentricity gradients are amplified from cones to mRGCs, and from mRGCs to cortex. Second, we extend our previously published computational observer model to quantify the contribution of spatial filtering by mRGCs to behavioral asymmetries. Starting with photons emitted by a visual display, the model simulates the effect of human optics, fixational eye movements, cone isomerizations and mRGC spatial filtering. The model performs a forced choice orientation discrimination task on mRGC responses using a linear support vector machine classifier. The model shows radial asymmetries in performance that are larger than those from a model operating on the cone outputs, but considerably smaller than those observed from human performance. We conclude that spatial filtering properties of mRGCs contribute to radial performance differences, but that a full account of these differences will entail a large contribution from cortical representations.

Section: Discussionsupporting

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Asymmetries around the visual field: From retina to cortex to behavior

Kupers

Carrasco

et al. 2020

Preprint

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Variability of the Surface Area of the V1, V2, and V3 Maps in a Large Sample of Human Observers

Yoon

Forenzo

et al. 2021

Preprint

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How variable is the functionally-defined structure of early visual areas in human cortex and how much variability is shared between twins? Here we quantify individual differences in the best understood functionally-defined regions of cortex: V1, V2, V3. The Human Connectome Project includes retinotopic measurements from 181 subjects, most of whom are twins. We trained four "anatomists" to manually define V1-V3 using retinotopic features. These definitions were more accurate than automated anatomical templates and showed that surface areas for these maps varied more than three-fold across individuals. The cortical magnification function also differed substantially among individuals: the relative amount of cortex devoted to central vision varied by more than a factor of 2. Whereas our twin sample sizes were too small to make precise heritability estimates (50 monozygotic pairs, 34 dizygotic), they nonetheless reveal high correlations, consistent with strong effects of the combination of shared genes and environment on visual area size. In V1, intraclass correlations of surface area between twin pairs were 84% and 68% for monozygotic and dizygotic pairs, respectively. The correlations were also high for V2 (81%, 73%) and V3 (75%, 43%). A trend for higher monozygotic than dizygotic size correlations, as well as greater similarity in map properties amongst monozygotic twins, suggest that visual area size and topography are partly genetically determined. Collectively, these results comprise the most accurate account of individual variability in visual area structure to date, and provide a robust population benchmark against which new individuals and developmental and clinical populations can be compared.

Cross-dataset reproducibility of human retinotopic maps

Himmelberg

Kurzawski

et al. 2021

Preprint

Self Cite

Population receptive field (pRF) models fit to fMRI data are used to non-invasively measure retinotopic maps in human visual cortex, and these maps are a fundamental component of visual neuroscience experiments. We examined the reproducibility of retinotopic maps across two datasets: a newly acquired retinotopy dataset from New York University (NYU) (n=44) and a public dataset from the Human Connectome Project (HCP) (n=181). Our goal was to assess the degree to which pRF properties are similar across datasets, despite substantial differences in their experimental protocols. The two datasets differ in stimulus design, participant pool, fMRI protocol, MRI field strength, and preprocessing pipelines. We assessed cross-dataset reproducibility of the two datasets in terms of the similarity of vertex-wise pRF estimates and in terms of large-scale cortical magnification properties. Within V1, V2, V3, and hV4, the group-median NYU and HCP vertex-wise polar angle estimates were nearly identical. Both eccentricity and pRF size estimates were also strongly correlated between the two datasets, but with a slope different from 1; the eccentricity and pRF size estimates were systematically greater in the NYU data. Next, to compare large-scale map properties, we quantified two polar angle asymmetries in V1 cortical magnification previously identified in the HCP data. The prior work reported more cortical surface area representing the horizontal than vertical visual field meridian, and the lower than upper vertical visual field meridian. We confirm both of these results in the NYU dataset. Together, our findings show that the retinotopic properties of V1-hV4 can be reliably measured between two datasets, despite numerous differences in their experimental design. fMRI-derived retinotopic maps are reproducible because they rely on an explicit computational model that is grounded in physiological evidence of how visual receptive fields are organized, allowing one to quantitatively characterize the BOLD signal in terms of stimulus properties (i.e., location and size). The new NYU Retinotopy Dataset will serve as a useful benchmark for testing hypotheses about the organization of visual areas and for comparison to the HCP Retinotopy Dataset.