Cortical magnification in human visual cortex parallels task performance around the visual field

  1. Noah C Benson
  2. Eline R Kupers
  3. Antoine Barbot
  4. Marisa Carrasco
  5. Jonathan Winawer

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    Evaluation Summary:

    This manuscript is of broad interest to readers in the field of human vision and its cortical topography as well as behavioral and genetic links. The investigation of the neurobiological basis of visual task performance asymmetries represents an important contribution to our understanding of how visual system architecture shapes perception. The key claims of the manuscript are well supported by the data, and the approaches used are thoughtful and rigorous.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewers #1, #2, and #3 agreed to share their names with the authors.)

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Abstract

Human vision has striking radial asymmetries, with performance on many tasks varying sharply with stimulus polar angle. Performance is generally better on the horizontal than vertical meridian, and on the lower than upper vertical meridian, and these asymmetries decrease gradually with deviation from the vertical meridian. Here, we report cortical magnification at a fine angular resolution around the visual field. This precision enables comparisons between cortical magnification and behavior, between cortical magnification and retinal cell densities, and between cortical magnification in twin pairs. We show that cortical magnification in the human primary visual cortex, measured in 163 subjects, varies substantially around the visual field, with a pattern similar to behavior. These radial asymmetries in the cortex are larger than those found in the retina, and they are correlated between monozygotic twin pairs. These findings indicate a tight link between cortical topography and behavior, and suggest that visual field asymmetries are partly heritable.

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  1. Version published to 10.7554/elife.67685 on eLife
  2. eLife

    Author Response

    Reviewer #1 (Public Review):

    [...] My main suggestion would be to expand discussions on the results of twins. To me that is the most interesting part of the present study, which contributed further from previous findings such as Silva et al., 2018.

    We have expanded the discussion of the twins results in the text in both the Results section and the Discussion section [P13❡2]. (We note that while we, too, find the twins results compelling, there are other aspects of the results that are novel, including the tight link to behavioral results.)

    Also, as the authors noted too, "behavioral pattern may vary with task". It would be helpful if the relationship between the present cortical magnification finding and behavioral results could be discussed with further details.

    We have elaborated on this point in the Results section [P7❡2].

    Reviewer #2 (Public Review):

    1. Representation of 45 degrees. The results demonstrate that 45{degree sign} angles are relatively under-represented on the cortical surface, but without a concomitant decline in perceptual performance (Figure 3). While a prior study demonstrated radial asymmetries in cortical magnification (Silva 2018), that prior study did not have the power and resolution to show the clear reduction in surface area around 45{degree sign} that is shown here. This result is one of the more novel findings of the current study, but is not discussed. I looked at the Barbot (2021) paper, and I gather that acuity was tested with a grating that was oriented at 45{degree sign}. Could this property of the stimulus interact with the radial orientation bias that has been shown in perception and cortical response (e.g., Sasaki 2006).

    The cortical surface areas corresponding to the 45° angles reported in the original submission were indeed puzzling and suggestive of some bias in the data or method. We have performed a substantial re-analysis of the results using a now-published dataset of manually-drawn V1, V2, and V3 boundaries (Benson et al., 2021; DOI:10.1101/2020.12.30.424856); our method section was also substantially updated to accommodate this dataset and some new calculations [P16❡3–P18]. Specifically, there are two main changes in the method. First, we now use hand-drawn boundaries of the lower, upper, and horizontal meridians, and of several iso-eccentricity contours, to identify sectors of the V1 map, rather relying on the boundaries found by an automated atlas fit. Second, we now employ a new and more robust method to carve up these sectors into fine-grained regions. The manually labeled boundaries have high inter-rater reliability and their inclusion eliminates any biases that could have derived from the automated boundary-finding method we had previously employed. This re-analysis leaves our previous findings intact and largely unchanged, and it eliminates the apparent mystery of the surface area of the 45° angle, which is no longer under-represented on cortex relative to behavior (Fig. 3).

    Regarding the Barbot et al. study, the 45 deg orientation was chosen so that it would not contaminate the psychophysical measures at the vertical and the horizontal meridians, as discriminability would be better along the vertical meridian for orientations off vertical and along the horizontal meridian for orientations off horizontal; it is possible that the performance at the intercardinal locations is better than if they had used 0° or 90° orientation.

    1. While the correlation in the MZ twins is impressive, I am not sure that it is an independent source of information. One would not want to conclude, for example, that there is a genetic influence specifically for radial asymmetry of the visual cortex. Instead, there may be genetic influences upon the general shape, folding, and functional organization of the cortex as a whole, of which the visual cortex is just one part. It would be informative, for example, if the correlation in MZ twins for visual cortex radial asymmetry is GREATER than the correlation that is observed for any other cortical property (Chen 2013). It would also be informative to examine perceptual data from these twin pairs, but I understand why this is not available.

    Our intention in analyzing the asymmetry correlations between twins was not to suggest that there is a genetic influence limited only to asymmetry, and we have reworded the discussion to further clarify this point [P13❡2–P14]. For context, we have added reports of correlations between other cortical properties [Fig. 4; P8❡1].

    1. I know that these authors have thought carefully about how cortical curvature might influence their measurements. There is the obvious confound that the horizontal meridian is represented in the depth of a sulcus, while the vertical meridian is represented close to the gyral crowns. I would appreciate some consideration in the methods or discussion of why cortical folding can't account for the current results.

    In the original submission we reported only the surface areas of the midgray surface (i.e., the halfway point between pial and white surfaces) as a way to minimize bias of the cortical curvature that might arise on the pial surface (where the gyral crown is expected to have a larger surface area than the sulcal valley) or the white surface (where the opposite is expected). We have now included Figure S1 as a supplement to Figure 2 a re-analysis of the data using both the white and pial surface areas as well as the midgray surface area. Whereas surface areas and their ratios vary numerically depending on which surface is used for analysis, the main trends hold for all 3 analyses (white, midgray, pial): there is more surface area for the horizontal than vertical and for the lower vertical than upper vertical. Unsurprisingly, this re-analysis substantially affects the HVA, which depends on the gyral and sulcal surface areas, but only slightly the VMA, which depends only on gyral surface areas. We have also added text in the Results to address this topic [P7❡1] .

    1. The Silva 2018 paper included a more "fine scale" analysis of cortical magnification as a function of polar angle (Figure 4B). The error bars in this prior report are an order of magnitude larger than in the current measurements, but I would appreciate an evaluation of the degree to which the current measures agree with this prior work.

    We have expanded our discussion of this paper in the text [P12❡1] and have included a supplemental comparison of their data (as digitized from their Fig. 4) with our data (Fig. S4, P34).

    1. The cortical surface representation is described as an "amplification" of asymmetries that are present in the retina. Looking at Figure 5, however, it doesn't appear to me that a multiplicative scaling of the cone or midget RF functions would fit the cortical data. The cortical asymmetries are certainly larger, but they are of a different form with eccentricity. This might be worth acknowledging, and perhaps considering that perceptual measures as a function of eccentricity and polar angle could deepen the correspondence with the cortical data.

    In this instance we used the word “amplification” loosely to mean that the asymmetries in cortex were consistently higher than the asymmetries in the retina, not in the mathematical sense of a multiplicative scale factor. We have now clarified this in the text, and we have expanded the discussion of this point [P10❡1].

    Reviewer #3 (Public Review):

    [...] The conclusions of this paper are mostly well supported by data, but some aspects of data analysis and statistics need to be clarified and extended.

    1. The statistical model on repeated measurements: in the present work, there are lots of repeated measurements recorded (e.g., Figure 1, across angular distance and meridian). It is a need of clear and comprehensive description on the statistical methods to be reported in the method part.

    The data referenced in Figure 1, and, in fact, all psychophysical data we analyzed, are from previous publications in which these details were reported, including analysis using linear mixed models. We have now duplicated the relevant details from these publications in the Methods along with relevant reports of the inter-rater reliability of the V1-V2 boundaries on which the surface area calculations were based. This subsection of the Methods is now titled Statistical Analysis and Measurement Reliability [P21–22].

    1. Measurement reliability: this is a fundamental concept of individual differences, which the present work is based on to assess the link between brain, behavior and genetics. The reliability levels of these measurements should be reported due to the importance of understanding the correlational outcomes. For example, In Figure 3, a surprisingly high correlation was reported (r = 0.96). How we interpret this correlation in terms of the psychometric theory of individual differences. Again, how this correlation was derived from such a setting on the repeated measurements.

    We have added a section on Statistical Analysis and Measurement Reliability in the Methods section to address the topic of reliability [P21–22]. Additionally, we note that the correlation from Figure 3 is a correlation of mean values across subjects using different subject groups for the x and y axes and thus should not be interpreted as a finding about individual differences. We have clarified this fact in the text [P7❡1].

    1. ICC: should be non-negative. In Figure 4, the negative ICCs appeared for DZ twins for some polar angle widths. Please clarify the reason.

    We have clarified our use of an unbiased estimate of the ICC in the Methods and have provided the formulae for our calculations [Eqs. 1–2; P20].

    1. Credit HCP data use: Please visit https://www.humanconnectome.org/study/hcp-young-adult/document/hcp-citations

    We thank the reviewer for catching this oversight and have included the relevant text in the Acknowledgements [P23].

    1. A systems-neuroscience perspective: These is an interesting way of discussing the present findings of the human vision system by looking them at the level of the global brain system (e.g., connectomics), for example, how these vision-related heritable features are related to or implicated for their connectome-level findings (https://pubmed.ncbi.nlm.nih.gov/26891986)?

    We have expanded the Discussion and have included text regarding the previous findings of connectome-level heritability in the visual cortex [P13❡2–P14].

  3. eLife

    Evaluation Summary:

    This manuscript is of broad interest to readers in the field of human vision and its cortical topography as well as behavioral and genetic links. The investigation of the neurobiological basis of visual task performance asymmetries represents an important contribution to our understanding of how visual system architecture shapes perception. The key claims of the manuscript are well supported by the data, and the approaches used are thoughtful and rigorous.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewers #1, #2, and #3 agreed to share their names with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    The article Cortical magnification in human visual cortex parallels task performance around the visual field used the HCP public data and reported the asymmetries of cortical magnification in the human visual cortex. The 7T data source enabled the analysis to be conducted with high resolution, and the results can be compared with behavioral patterns. Given the HCP data coverage, the article also analyzed the radial asymmetries in different participant groups, including monozygotic twins, dizygotic twins and unrelated pairs, and showed the contribution of genetic factors in this functional asymmetry. In general, I think this article is a good example of utilizing public data for further analysis for new research question.

    My main suggestion would be to expand discussions on the results of twins. To me that is the most interesting part of the present study, which contributed further from previous findings such as Silva et al., 2018.

    Also, as the authors noted too, "behavioral pattern may vary with task". It would be helpful if the relationship between the present cortical magnification finding and behavioral results could be discussed with further details.

  5. eLife

    Reviewer #2 (Public Review):

    In this study Benson and colleagues measure the radial anisotropy of human visual cortex. They relate the observed pattern of cortical magnification to radial variation in perceptual performance and retinal organization. The study is motivated by many years of careful measurement of visual performance that demonstrates overall improved perception along the horizontal as compared to vertical meridian, and relatively better performance in the inferior as compared to superior visual field along the vertical meridian. The authors fit 7T retinotopic mapping data from 181 people and measured the surface area of strips of cortex in V1/V2 corresponding to different polar angle representations. They find a clear over-representation of the horizontal as compared to vertical meridian, and a similar finding for the lower as compared to upper visual field on the vertical meridian. This pattern of results matches previous behavioral measurements. The radial variation in cortical representation is demonstrated to be shared in monozygotic twins. Finally, the authors build on prior modeling work to demonstrate that the radial asymmetries that they observe in their cortical measurements (as a function of eccentricity) are larger than is explained by a first order effect of asymmetries in the number of cones, or midget retinal ganglion cell receptive fields.

    There is much to like here. The cortical measurements are lovely. The authors have performed anatomically informed, Bayesian smoothing of the HCP 7T retinotopy dataset and have extremely small error bars on the surface area of the visual regions. Comparisons with retinal anatomy and prior behavioral work strongly support the case that the cortical measures are best related to perceptual performance. My areas of critique and question are organized below, ordered generally from more general to smaller issues.

    1. Representation of 45 degrees. The results demonstrate that 45{degree sign} angles are relatively under-represented on the cortical surface, but without a concomitant decline in perceptual performance (Figure 3). While a prior study demonstrated radial asymmetries in cortical magnification (Silva 2018), that prior study did not have the power and resolution to show the clear reduction in surface area around 45{degree sign} that is shown here. This result is one of the more novel findings of the current study, but is not discussed. I looked at the Barbot (2021) paper, and I gather that acuity was tested with a grating that was oriented at 45{degree sign}. Could this property of the stimulus interact with the radial orientation bias that has been shown in perception and cortical response (e.g., Sasaki 2006).

    2. While the correlation in the MZ twins is impressive, I am not sure that it is an independent source of information. One would not want to conclude, for example, that there is a genetic influence specifically for radial asymmetry of the visual cortex. Instead, there may be genetic influences upon the general shape, folding, and functional organization of the cortex as a whole, of which the visual cortex is just one part. It would be informative, for example, if the correlation in MZ twins for visual cortex radial asymmetry is GREATER than the correlation that is observed for any other cortical property (Chen 2013). It would also be informative to examine perceptual data from these twin pairs, but I understand why this is not available.

    3. I know that these authors have thought carefully about how cortical curvature might influence their measurements. There is the obvious confound that the horizontal meridian is represented in the depth of a sulcus, while the vertical meridian is represented close to the gyral crowns. I would appreciate some consideration in the methods or discussion of why cortical folding can't account for the current results.

    4. The Silva 2018 paper included a more "fine scale" analysis of cortical magnification as a function of polar angle (Figure 4B). The error bars in this prior report are an order of magnitude larger than in the current measurements, but I would appreciate an evaluation of the degree to which the current measures agree with this prior work.

    5. The cortical surface representation is described as an "amplification" of asymmetries that are present in the retina. Looking at Figure 5, however, it doesn't appear to me that a multiplicative scaling of the cone or midget RF functions would fit the cortical data. The cortical asymmetries are certainly larger, but they are of a different form with eccentricity. This might be worth acknowledging, and perhaps considering that perceptual measures as a function of eccentricity and polar angle could deepen the correspondence with the cortical data.

    References:
    Sasaki, Yuka, et al. "The radial bias: a different slant on visual orientation sensitivity in human and nonhuman primates." Neuron 51.5 (2006): 661-670.
    Chen, Chi-Hua, et al. "Genetic topography of brain morphology." Proceedings of the National Academy of Sciences 110.42 (2013): 17089-17094.
    Silva, Maria Fatima, et al. "Radial asymmetries in population receptive field size and cortical magnification factor in early visual cortex." NeuroImage 167 (2018): 41-52.

  6. eLife

    Reviewer #3 (Public Review):

    Noah C. Benson and colleagues investigated the cortical magnification at a fine angular resolution around the visual field using using HCP multimodal imaging data. They report that asymmetries in the primary visual cortex map closely parallel asymmetries in behavior, are larger than asymmetries in retinal cell density and are correlated between twins. These data add in an interesting way to the ongoing discussion on the topic whether and how the visual field asymmetries are shaped by both the genetic and environmental factors, which are reflected in the cortical topography.

    The conclusions of this paper are mostly well supported by data, but some aspects of data analysis and statistics need to be clarified and extended.

    1. The statistical model on repeated measurements: in the present work, there are lots of repeated measurements recorded (e.g., Figure 1, across angular distance and meridian). It is a need of clear and comprehensive description on the statistical methods to be reported in the method part.

    2. Measurement reliability: this is a fundamental concept of individual differences, which the present work is based on to assess the link between brain, behavior and genetics. The reliability levels of these measurements should be reported due to the importance of understanding the correlational outcomes. For example, In Figure 3, a surprisingly high correlation was reported (r = 0.96). How we interpret this correlation in terms of the psychometric theory of individual differences. Again, how this correlation was derived from such a setting on the repeated measurements.

    3. ICC: should be non-negative. In Figure 4, the negative ICCs appeared for DZ twins for some polar angle widths. Please clarify the reason.

    4. Credit HCP data use: Please visit https://www.humanconnectome.org/study/hcp-young-adult/document/hcp-citations

    5. A systems-neuroscience perspective: These is an interesting way of discussing the present findings of the human vision system by looking them at the level of the global brain system (e.g., connectomics), for example, how these vision-related heritable features are related to or implicated for their connectome-level findings (https://pubmed.ncbi.nlm.nih.gov/26891986)?

  7. Version published to 10.1101/2020.08.26.268383v5 on bioRxiv
  8. Version published to 10.1101/2020.08.26.268383v4 on bioRxiv
  9. Version published to 10.1101/2020.08.26.268383v3 on bioRxiv
  10. Version published to 10.1101/2020.08.26.268383v2 on bioRxiv
  11. Version published to 10.1101/2020.08.26.268383v1 on bioRxiv