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

Abstract

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 7T Retinotopy Dataset includes retinotopic measurements from 181 subjects, including many 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. This three-fold variation was little changed when normalizing visual area size by the surface area of the entire cerebral cortex. In addition to varying in size, we find that visual areas vary in how they sample the visual field. Specifically, the cortical magnification function differed substantially among individuals, with the relative amount of cortex devoted to central vision varying by more than a factor of 2. To complement the variability analysis, we examined the similarity of visual area size and structure across twins. Whereas the twin sample sizes are too small to make precise heritability estimates (50 monozygotic pairs, 34 dizygotic pairs), they nonetheless reveal high correlations, consistent with strong effects of the combination of shared genes and environment on visual area size. Collectively, these results provide the most comprehensive 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.

Comparing retinotopic maps of children and adults reveals a late-stage change in how V1 samples the visual field

“…Variability in the surface area of the maps was similar for adults and children. Notably, the surface area of adult V1 varied more than twofold, consistent with prior measurements 24 , 28 , 29 ; the smallest V1 was 1367 mm 2 and the largest was 3206 mm 2 . Similar variability in V1 surface area was found in children; the smallest V1 was 1257 mm 2 and the largest was 3312 mm 2 .…”

“…This is consistent with reports showing that the surface areas of V1, V2, and V3 are similar between adults and children 1 and that the total surface area of the cortical sheet is adultlike by age 10 [52][53][54] . Notably, the coefficients of variation reported for V1, V2, and V3 were similar to those reported from adult data from the HCP dataset 28 , all around 0.2. A coefficient of variation of 0.2 predicts a 2-fold range in map surface area when comparing the largest to smallest maps in sample sizes comparable to ours (20-30 participants, assuming approximately normal distributions), just as we found.…”

“…Prior work has shown that the left and right hemispheres of retinotopic maps are similar in size 28,29 . Thus, an additional metric of data quality is the correlation of the surface area of the left and right hemispheres of the maps.…”

Cortical magnification eliminates differences in contrast sensitivity across but not around the visual field

“…Cortical magnification –the amount of cortical surface area corresponding to one degree of visual angle (mm 2 /°) – declines with eccentricity ( Benson et al, 2022 ; Engel et al, 1994 ; Himmelberg et al, 2021 ; Horton and Hoyt, 1991 ; Van Essen et al, 1984 ) and has been used to link perceptual performance to brain structure ( Duncan and Boynton, 2003 ; Himmelberg et al, 2023a ; Himmelberg et al, 2022b ; Rovamo et al, 1978 ; Schwarzkopf et al, 2011 ; Schwarzkopf and Rees, 2013 ; Song et al, 2015 ). If performance differences as a function of stimulus location can be attributed to differences in cortical surface area, then performance should be equated when equating stimulus size to the amount of cortical area activated.…”

Functionally and structurally distinct fusiform face area(s) in over 1000 participants

“…“FFC” was used to refer to the fact that at the group level, the authors were unable to subparcellate the complex into more than one area likely due to spatial blurring that occurs with group analyses. Second, studies performing analyses within individual participants manually define the FFA in each hemisphere, which while an arduous process, is still the most accurate method for defining functional regions in individual participants – even for primary sensory areas given recent findings ( Benson et al, 2022 ) – compared to automated approaches. Consequently, given this manual and labor-intensive process, many studies interested in face processing at the level of individual participants suffer from relatively small sample sizes (typically in the ballpark between 10 and 50 participants; Çukur et al, 2013 ; Davidenko et al, 2012 ; Downing et al, 2006 ; Elbich and Scherf, 2017 ; Engell and McCarthy, 2013 ; Finzi et al, 2021 ; Gomez et al, 2015 , 2017 , 2018 ; Grill-Spector et al, 2004 ; Julian et al, 2012 ; Kay et al, 2015 ; Kietzmann et al, 2012 ; McGugin et al, 2014 , 2015 , 2016 ; Natu et al, 2016 , 2019 ; Nordt et al, 2021 ; Parvizi et al, 2012 ; Pitcher et al, 2011 ; Rosenke et al, 2020 , 2021 ; Scherf et al, 2017 ; Stigliani et al, 2015 , 2019 ; Weiner et al, 2010 , 2014 , 2016 , 2017 ; Weiner and Grill-Spector, 2010 ; countless others) because manually defining functional regions is time consuming.…”