Mapping human visual contrast sensitivity and vision loss across the visual field with model-based fMRI

At least 2.2 billion people worldwide experience blinding eye diseases that emerge or progress across the lifespan, with 1.1 billion living with vision loss in 2020 (Bourne et al., 2021; World Health Organization, 2019). Accurate characterization of visual function is critical for optimal intervention and patient support, but fraught with challenges. The gold clinical standard measure is visual acuity, the highest spatial resolution that can be discerned. This index mainly relies on a well-functioning fovea, the small retinal area with the highest density of photoreceptors, which processes the central 0°–2° around fixation. However, many forms of visual impairment do not affect the fovea alone. The vast majority of our visual input comes from peripheral vision, which provides coarse but invaluable information (Alvarez, 2011; Oliva, 2005) – nearly all daily functions critically depend on intact functionality of the wider visual field, including driving, crossing the street, social function, mobility, and even reading (Lange et al., 2021). Accordingly, impairment of tissue beyond the fovea and its projections drastically affects function and quality of life (Lange et al., 2021; Lisboa et al., 2013; Roh et al., 2018; Subhi et al., 2017). Still, testing visual function across the visual field remains limited in clinical and therapeutic contexts, especially in patients with drastic central vision loss. In this study, we aimed to address this gap by introducing a novel functional MRI (fMRI)-based approach to measure visual field sensitivity across a wide expanse of the visual field (40° diameter).

Beyond visual acuity, functional impairment across the wider visual field can be measured using a range of visual field tests, from the finger counting visual confrontation field test to more complicated and/or computerized tests (e.g., standard automatic perimetry, kinetic perimetry, microperimetry; Rai et al., 2024). Computerized tests typically involve measuring sensitivity to the luminance contrast of a target relative to a background at different visual field locations while the participant’s gaze is fixed on a central point. In some cases (e.g., microperimetry), sensitivity measurements are paired with fundus imaging, offering greater precision in linking visual field functions to specific retinal locations (Rai et al., 2024). As a result, visual field assessments can reveal functionally relevant deficits – including localized sensitivity loss and scotomas – that are not captured by foveal acuity alone, and are therefore potentially valuable for tracking disease progression and therapeutic efficacy.

Despite their clinical relevance, visual field testing comes with challenges and limitations, and as a result, the inclusion of visual field measures in sight-rescuing therapy trials is limited. First, it requires prolonged fixation and sustained visual attention. This can be very challenging for patients with severe vision loss, who often struggle to fixate, and strain to detect even high-intensity stimuli. This can lead to long and unpleasant testing sessions with unreliable results. Second, as perception of light stimuli is inherently subjective (Rai et al., 2024) and effortful, patients may vary in their criteria for visual recognition, and in their ability to report visual signals that are weakened or distorted by disease. Together, these constraints reduce the feasibility, robustness, and interpretability of conventional visual field testing in clinical trials, underscoring the need for alternative or complementary approaches that can assess functional vision while placing fewer demands on subjective reporting.

fMRI has recently been proposed as a promising alternative to measure visual field loss, as it requires no overt task, and instead measures visual sensitivity directly from brain responses (Farahbakhsh et al., 2022; Prabhakaran et al., 2021; Ritter et al., 2019). Population receptive field (pRF) mapping fMRI can measure which parts of the cortex respond to which parts of the visual scene (Dumoulin and Wandell, 2008). This is achieved by measuring local changes in cortical blood oxygenation in response to high-contrast stimuli such as flickering checkerboards that systematically traverse the visual field. These neural timeseries are then fit with a retinotopic spatial tuning function. The fMRI signal strength in a cortical region that encodes a specific visual field location provides information about visual sensitivity in this location (Dumoulin and Wandell, 2008). This approach has recently been used to accurately recover blind spots in the visual field detected with visual field perimetry (Pawloff et al., 2023; Ritter et al., 2019). These results in turn suggest that fMRI may be a useful tool to monitor disease progression and recovery in visually impaired patients.

However, there are also significant challenges associated with this pRF mapping. Because pRF models require knowledge of where visual stimuli fall on the retina, this approach requires accurate and sustained fixation. If fixation is poor, the visual field locations assigned to cortical regions will be displaced, leading to noise or bias in the pRF maps. Additionally, most fMRI studies measure vision across a small central part of the visual fields (~10° of eccentricity), which will not capture critical dynamics in the periphery. Finally, most studies use a single maximum contrast stimulus to assess visual function (Broderick et al., 2022; Farahbakhsh et al., 2022; Liu et al., 2006; O’Connell et al., 2016; Ritter et al., 2019). This is suboptimal for at least two reasons: First, it may reduce the ability to detect subtle but clinically important changes that do not result in complete sensitivity loss or recovery. Second, with a single contrast stimulus, it becomes more difficult to determine whether a reduction in fMRI signal across sessions or brain regions reflects vision-related changes or other factors. Together, these factors currently limit the utility of neuroimaging-based visual field tests.

In this study, we sought to develop fMRI methods for measuring visual field sensitivity to address these challenges and to test its utility in artificial and realistic cases of visual field loss. Rather than presenting a single maximum contrast eccentricity-scaled checkerboard in a pRF mapping protocol, observers viewed large-field sinusoidal gratings at a range of contrasts without strict requirement to fixate. We used a large screen setup (40° diameter) to measure visual function across a large expanse of the visual field. To quantify visual sensitivity, we fit a simple cortical contrast response model to voxel timeseries evoked by the gratings, avoiding reliance on arbitrary signal amplitude thresholds. We first tested if this approach could sensitively and reliably quantify predicted contrast sensitivity differences across eccentricity and polar angle dimensions of the entire visual field (Figure 1), using a short, patient-friendly protocol in sighted controls. Critically, to achieve this, we linked cortical contrast sensitivity estimates to visual field locations using retinotopic tuning estimates obtained in a separate session. However, for patients with severe vision loss, it is infeasible to obtain accurate pRF measures due to poor ability to fixate a central target. We therefore also evaluated the approach using the structure-based atlas of retinotopic values developed by Benson et al., 2014; Benson and Winawer, 2018. This atlas predicts retinotopic organization by aligning individual cortical anatomy (e.g., surface curvature) to a group-average template that incorporates an algebraic model of retinotopy (Benson et al., 2014). Once the subject’s brain is aligned to this structural atlas, retinotopic maps defined by the model – that is, polar angle and eccentricity maps – are projected onto the individual’s cortex. This allows estimation of visual field maps without requiring functional imaging, and provides a non-invasive, anatomy-driven approximation of visual field representations. To increase accuracy of the Benson atlas for our large-field stimuli, we subsequently fit these eccentricity estimates to the Horton and Hoyt, 1991 model of cortical magnification in each individual.

Figure 1

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We demonstrate that our approach can recover, at a cortical level, known sensitivity changes across the visual field. V1 neuronal populations encoding the central visual field show the highest sensitivity to high spatial frequencies, while those encoding the periphery are most sensitive to low spatial frequencies. We also find greater sensitivity in V1 regions receiving visual inputs from the horizontal compared to vertical quadrants, and from the lower compared to upper quadrants. Crucially, these effects are detectable at the individual level and repeatable across visits, suggesting our approach will be suitable for detecting visual field sensitivity changes over time (e.g., due to learning, aging, disease progression, or recovery). Moreover, when using the Benson atlas instead of individual pRF measures to link neuronal responses to visual field locations, we still detect these effects, although with slightly reduced sensitivity. We also demonstrate the clinical relevance of this approach by recovering simulated scotomas (i.e., a ring of visual field loss around fixation and the loss of an entire visual field quadrant), as well as visual field loss in a patient with a neurodegenerative disorder causing large areas of visual field loss. We conclude that this approach, that is, the combination of cortical contrast sensitivity testing, large field stimulation, and a large-field calibrated retinotopic atlas, may be particularly useful for quantifying visual recovery in patients, whether spontaneous or following gene-therapy interventions, and for linking brain-based activations to behavioral performance.

To measure visual field function, we developed a new measure of cortical contrast sensitivity, assessing the brain’s ability to discriminate gratings of varying spatial frequencies based on luminance variations. This depends on many factors, including the size, orientation, and density of cells in different parts of the retina. As these factors change across the visual field, contrast sensitivity varies with spatial frequency and visual field location in the typical, healthy visual system. We first investigated whether our brain-based approach can reliably detect these known differences in contrast sensitivity as a function of eccentricities (up to 20° from fixation) and visual field quadrants, in individual normal-sighted controls. We then investigated whether our approach could recover loss of visual inputs at the cortical level, either simulated in a healthy control or pathological in a patient. In each section, findings were first reported for the pRF-defined ROIs and then for the Benson-defined ROIs.

Spatial frequency preference changes in V1 neuronal populations across eccentricities

For high spatial frequency stimuli, we expected better sensitivity in V1 neuronal populations encoding central locations in visual space than in those encoding the periphery. In contrast, for low spatial frequency information, we expected the opposite, better sensitivity in V1 neuronal populations encoding the periphery than the center of the visual field.

Using ANOVA, we found the expected interaction between spatial frequency and eccentricity ($F(1.96,11.79)=28.66,\mathrm{p}<0.001$; Figure 2) as well as a main effect of eccentricity ($F(2.33,13.99)=12.67,\mathrm{p}<0.001$). These effects were statistically significant at the individual participant level showing high sensitivity (Figure 2A, transparent lines show individual data; Figure 2C shows individual visual field plots; for stats, see Appendix 1—table 1).

Figure 2

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Post hoc tests using Holm–Bonferroni correction show that V1 neuronal populations receiving inputs from the central visual field (0.5°–4.5°) showed greater contrast sensitivity to high spatial frequency as compared to low spatial frequency stimuli (steeper slope for the 3 versus 0.3 cpd condition: 0.5°–2.5°: $t(6)=4.35,p_{bonf}=0.0149$; 2.5°–4.5°: $t(6)=3.471,p_{bonf}=0.0266$). Conversely, peripheral eccentricities in V1 (above 9.5°) showed higher contrast sensitivity to low as compared to high spatial frequency stimuli (steeper slope for 0.3 versus 3 cpd condition: 9.5°–15°: $t(6)=-4.591,p_{bonf}=0.0149$; 15°–20°: $t(6)=-6.615,p_{bonf}=0.0029$). Between 4.5° and 9.5°, V1 contrast sensitivity was similar for both spatial frequencies ($t(6)=-0.226,p_{bonf}=0.8286$). Crucially, these effects remained when using retinotopic estimates based on structural scans derived from the Benson retinotopic atlas instead of the pRF-mapping measures (0.5°–2.5°: $t(6)=5.768,p_{bonf}=0.0059$; 2.5°–4.5°: $t(6)=2.531,p_{bonf}=0.0892$; 4.5°–9.5°: $t(6)=-0.293,p_{bonf}=0.7792$; 9.5°–15°: $t(6)=-3.274,p_{bonf}=0.0509$; 15°–20°: $t(6)=-3.528,p_{bonf}=0.0496$; see Appendix 1—figure 2 and Appendix 1—table 3).

These results demonstrate that our approach can detect subtle changes in visual sensitivity across eccentricities at the individual participant level. The ability to reveal these gradients was made possible by the large peripheral coverage provided by our large-field stimulation setup (see Appendix 1—figure 1), which enabled a more complete characterization of V1 sensitivity across the visual field. Importantly, the same effects were preserved when using retinotopic estimates derived from structure-based atlases, demonstrating that atlas-based methods can be used as alternatives to pRF mapping in cases where it might otherwise be difficult or impossible to directly collect pRF measures. Together, these highlight both the validity of our approach and its potential to broaden the scope of visual neuroscience.

Contrast sensitivity is not equal across the visual field quadrants in V1 neurons

Contrast sensitivity is known to vary across the quadrants of the visual field; previous behavioral studies have shown greater contrast sensitivity along the horizontal versus vertical meridians, and along the lower versus upper vertical meridian (Barbot et al., 2021; Carrasco et al., 2022; Himmelberg et al., 2020; Himmelberg et al., 2023). Recent fMRI studies have also shown that these asymmetries in BOLD response amplitude to single contrast gratings (Kurzawski et al., 2022; Liu et al., 2006; O’Connell et al., 2016). Therefore, we expect our fMRI approach to uncover these established asymmetry patterns at the cortical level.

Figure 3 shows how cortical contrast sensitivity in V1 varies across visual field quadrants, for the 0.3 and 3 cpd conditions. For this analysis, we collapsed eccentricities (0.5°–20°) into a single bin. We found a main effect of visual field quadrant location on V1 sensitivity ($F(2.46,14.76)=20.71,\mathrm{p}<0.001$). Post hoc pairwise comparisons using Holm–Bonferroni corrections revealed that, as predicted, the cortical contrast response function had a higher slope – indicating better V1 sensitivity – along the horizontal versus vertical quadrants (Horizontal–Vertical Anisotropy – HVA: $t(6)=5.908,p_{bonf}=0.0031$) and along the lower versus upper quadrant (Vertical Meridian Anisotropy – VMA: $t(6)=4.106,p_{bonf}=0.0126$). Conversely, no difference in cortical contrast sensitivity was found between V1 neuronal populations encoding the left and right quadrants of the visual field (Left–Right Horizontal Meridian Anisotropy – LRHMA: $t(6)=0.7197,p_{bonf}=0.4988$). Moreover, there was no interaction between spatial frequency and visual field quadrant positions ($F(2.16,12.99)=1.34,\mathrm{p}=0.298$), suggesting V1 visual field anisotropies are relatively constant across spatial frequencies. Importantly, all these differences are also present in individual participants (Figure 3A, transparent lines are individual data, Figure 3C shows individual visual field plots; for stats, see Appendix 1—table 2). All participants had better V1 sensitivity in the horizontal versus vertical quadrants. Six out of seven participants showed asymmetries between the lower and upper quadrants. Differences in V1 sensitivity between left and right quadrants were inconsistent across participants (three participants: left > right; two participants: right > left; two participants: non-significant), in line with no difference at the group level. This again demonstrates the high sensitivity of our approach for identifying and quantifying sensitivity changes across the visual field, at the individual participant level, offering a potential valuable tool for assessing and addressing visual field loss in clinical populations.

Figure 3

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We next tested if we could recover these V1 anisotropies when using retinotopic estimates based on structural scans fit with the Benson atlas instead of pRF measures (see Appendix 1—figure 3 and Appendix 1—Tables 3 and 4). We found that the horizontal–vertical anisotropy effect was recovered (HVA: $t(6)=3.584,p_{bonf}=0.0347$), but that the vertical meridian anisotropy effect was not (VMA: $t(6)=0.744,p_{bonf}=0.9697$) with this approach. These results provide evidence that our approach can still detect subtle visual field quadrant anisotropies using a structure-based retinotopic atlas, although less accurately.

While the most pronounced contrast sensitivity difference along visual field quadrants (horizontal versus vertical quadrants) was retained when the adjusted Benson atlas was used to replace pRF measurements, not all anisotropies survived. This may be because the template does not model cortical magnification differences along meridians, reducing accuracy. It is not surprising that retinotopic estimates based on structure-retinotopy relationship modeling provide less accurate estimates of retinotopic tuning of neuron populations in V1 than direct measures of the individual retinotopy. Any individual deviations from the model make it more challenging to accurately assign cortical contrast sensitivity to visual field locations, potentially decreasing the ability to detect changes in visual function across the field. Given this challenge, results with the adjusted Benson map are surprisingly similar to those based on pRF measurement, showing promise for clinical applications when pRF mapping is unfeasible.

V1 sensitivity differences across the visual field are reproducible across visits

Having shown that changes in cortical contrast sensitivity across the visual space can be detected with fMRI, it is crucial to determine how reliable our measure is. This is especially important for potential applications in monitoring changes over time. Using Spearman correlations, we measured the reliability of cortical contrast sensitivity estimates for the 0.3 cpd spatial frequency condition across two repeated measurements, collected on two different days. We collected test–retest reliability measures from four out of seven participants (Figure 4A, B) and benchmarked them against the correlations between the 0.3 cpd condition and 3 cpd spatial frequency condition, collected in the same session (Figure 4C). If measures are reliable, correlations should be higher for repeated measures with the same spatial frequency stimulus, collected on different days. We tested this prediction using a one-tailed paired t-test.

Figure 4

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Our findings clearly show that the gradual increase in cortical sensitivity toward the periphery for the 0.3 cpd condition is consistent across the two sessions (Figure 4A). Moreover, the data of individual participants show characteristic profiles that stay highly consistent. Additionally, we observed moderate-to-strong positive Spearman correlations across sessions for the 0.3 cpd condition (Figure 4B; C2: $\rho =0.5793$; C4: $\rho =0.6996$; C5: $\rho =0.4059$; C6: $\rho =0.7677$), which were consistently higher than those across different spatial frequency conditions (Figure 4C; C2: $\rho =0.5503$; C4: $\rho =0.2318$; C5: $\rho =0.2158$; C6: $\rho =0.4993$). This difference was statistically significant ($t(3)=2.62,p<0.0395$). This indicates that within participants, our measure is reliable across sessions and distinguishes effectively between patterns elicited by various spatial frequency conditions. Collectively, this suggests that our measure could serve as a robust tool for detecting changes in cortical sensitivity, such as those occurring over time or following therapeutic interventions.

Effect of eye movements on V1 cortical sensitivity

So far, we have demonstrated that our measure of cortical sensitivity can reliably recover known gradients in sensitivity across eccentricities and visual quadrants. We also showed that this measure was consistent across visits and sessions, suggesting its potential utility for monitoring changes over time. However, all prior tasks were conducted under conditions of central fixation, with participants instructed to maintain gaze on a central dot. A key motivation for this approach was its theoretical robustness to fixation instability. We therefore also aimed to investigate how varying degrees of eye movement might influence cortical sensitivity across the visual field.

To address this, two participants (C5 and C6) completed a modified version of the contrast sensitivity task in which they made eye movements either by following a dot moving randomly at a radius of 2° or 5° around fixation, or by self-initiated very large eye movements. Eye movements across these conditions (Figure 5, bottom row; Figure 6, bottom row) were quantified using bivariate contour ellipse area (BCEA; C5 – central fixation: mean ± SD = 0.57 ± 0.11 deg², 2° eye motion: 2.69 ± 0.48 deg², 5° eye motion: 20.3 ± 1.32 deg², random eye motion: 133.7 ± 23.36 deg²; C6 – central fixation: 0.96 ± 0.56 deg², 2° eye motion: 1.28 ± 0.15 deg²). For reference, in severe (idiopathic) nystagmus, the eye movement variability along the vertical and horizontal planes is 1.08 and 1.60 deg, respectively (Tailor et al., 2021). Assuming a moderate correlation between axes ($\rho =0.3$), the average fixation stability would equate to a BCEA of ~21.46 deg² (i.e.,~5° eye motion condition in our data).

Figure 5

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Figure 6

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Despite these very large levels of eye movements, we observed that the overall cortical contrast sensitivity spatial pattern across eccentricity remained remarkably consistent (Figure 5, top and middle rows; Figure 6, top row). However, at the most extreme movements, contrast sensitivity estimates (slope values) were lower; and while the overall cortical visual field map structure was still clearly present for low spatial frequencies, it appeared more flattened for 3 cpd, suggesting reduced sensitivity of our measure for large eye movement and high spatial frequency stimuli.

A test-case of simulated loss of visual inputs

In the previous sections, we showed that the slope of a square root function provides a reliable measure of contrast sensitivity in the brain of healthy controls. But can this brain-level model also quantify loss of visual inputs? To test this, we first simulated an artificial scotoma in one normally sighted participant, by (a) masking a region of the visual field with a gray, annular ring, covering 3°–8° eccentricity (Figure 7A), and (b) masking the upper-right visual quadrant using a gray quarter-sector overlay (Figure 8A). We expect smaller slope values in V1 neuronal populations that would under normal circumstances encode that part of the visual space.

Figure 7

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Figure 8

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As expected, we observed reduced responses in V1 locations corresponding to the artificial scotoma (Figures 7 and 8), with increased responses along the edges of the mask for the ring scotoma condition (Figure 7). This artificial loss of visual input was also clearly present in the cortical contrast sensitivity estimate, with significantly reduced slope steepness in V1 between 3° and 8° for the ring scotoma condition (Figure 7C, D) and in the upper-right quadrant for the quarter-sector scotoma condition (Figure 8B, C). Additionally, we could recover this scotoma using the calibrated Benson template, although less accurately (Figures 7E and 8D). These results show that this measure of V1 contrast sensitivity is sensitive enough to detect loss of visual inputs in the brain at an individual level, when a complete local loss of sight is simulated, and that this approach does not crucially rely on pRF mapping data from the individual. This supports the utility of our approach in recovering patterns of vision loss and recovery at a cortical level.

Case of severe central vision loss

We next tested if brain-based cortical contrast sensitivity could recover visual field loss in a clinical patient with Leber Hereditary Optic Neuropathy (LHON; M, 45.5 years old, mutation: m.11778G>A, disease duration: 36.1 years). LHON is a neurodegenerative disease caused by mitochondrial DNA mutations, leading to acute loss of retinal ganglion cells across large visual field areas. LHON patients often present with dense, central scotoma extending up to 10°–15° around fixation.

As vision loss often varies between eyes, the patient underwent monocular testing for the fMRI contrast sensitivity task (tested spatial frequencies: 0.3 and 1 cpd). For comparison, a behavioral visual field perimetry map was obtained with the Compass fundus perimeter (CenterVue, Padova, Italy) for each eye, using a 24-2 testing grid with a Goldmann size III target (54 locations: 52 from the 24-2 grid, 1 at fixation, and 1 at the physiological blind spot). The Compass actively tracks the retina to compensate for fixation loss, automatically repositioning targets based on current eye position. Sensitivity thresholds at each location were determined using the ZEST projection strategy implemented in the device. The patient, who had some preserved central vision, could see the fixation targets in behavioral testing and the scanner, making them an ideal participant for methods evaluation.

The Compass perimetry map revealed drastic loss of sensitivity across the visual field (Figure 9 – top row). For the left eye, the left hemifield was more affected than the right, with most severe loss in the upper-left quadrant. In contrast, the right eye showed more reduced sensitivity in the right hemifield, particularly in the upper-right quadrant.

Figure 9

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We found correspondence between affected and non-affected visual field regions across the Compass and the fMRI contrast sensitivity measures (Figure 9). Here, V1 contrast sensitivity (i.e., slope values) was computed for the upper-left, upper-right, lower-left, and lower-right quadrants to match the layout from the Compass visual field maps. Using the brain-based approach, we recovered sensitivity loss in the upper-right visual quadrant for the right eye at the cortical level, reflected in smaller cortical contrast sensitivity slopes in V1 neuronal populations encoding that area. For the left eye, cortical contrast sensitivity was also lower in the upper-left quadrant of the visual field, but differences were less pronounced. This is in line with the observation that the left eye was the better eye, with substantially less severe vision loss in the affected regions (lighter shading, Figure 9 – top row). When using the calibrated Benson atlas instead of pRF mapping data to link neuronal responses to visual locations, this pattern was retained for the right eye, but the more subtle pattern in the left eye became noisier.

By combining large-field stimulation and contrast sensitivity modeling in fMRI, with the cortical magnification-adjusted Benson atlas, these data show how brain-based measures could provide a tool to characterize vision loss in patients without need for demanding tasks or sustained precise fixation, while highlighting challenges of detecting more subtle vision loss and pinpointing it to visual locations.

In this study, we aimed to develop and validate an fMRI-based method for measuring cortical contrast sensitivity across a broad extent of the visual field in V1, addressing the limitations of traditional visual field tests and existing neuroimaging approaches. To achieve this, we implemented a large-field presentation setup in fMRI, allowing us to stimulate the visual cortex up to 20° eccentricity. By integrating population receptive field (pRF) modeling with contrast sensitivity measurements of the BOLD response to large-field sinusoidal gratings varying in contrast levels and spatial frequencies, we created a quantitative, sensitive, and reliable measure of cortical contrast sensitivity. Our key findings demonstrate that this approach can accurately quantify known variations in V1 contrast sensitivity across eccentricity and polar angle, recover simulated and pathological visual field losses, and produce consistent and reliable measures both at the group and individual participant levels. Crucially, the ability to visualize these sensitivity gradients was made possible by the large peripheral coverage provided by our large-field stimulation setup. Such coverage is particularly important for clinical applications, as it enables the detection of visual field losses beyond the macula (i.e., beyond 10° eccentricity) and the evaluation of residual peripheral vision in patients with macular-restricted damage. In doing so, this work provides a useful tool for advancing both basic visual neuroscience and translational research in clinical populations.

All post-processed and anonymized data are available on Zenodo (https://doi.org/10.5281/zenodo.19051439). Codes to reproduce the figures from the provided dataset are available on GitHub (https://github.com/HugoCWB/ChowWingBomEtAl_2025_eLife, copy archived at Chow-Wing-Bom, 2026b). Codes to adjust the Benson maps' eccentricity are available on GitHub (https://github.com/HugoCWB/AdjustEccTool, copy archived at Chow-Wing-Bom and Benson, 2026a). A Docker image for the Benson eccentricity adjustment toolbox is also available on Zenodo (https://doi.org/10.5281/zenodo.18770065). The original raw data cannot be shared publicly because of participant consent and ethical approval restrictions. Access can be requested by emailing the corresponding author, Dr Hugo Chow-Wing-Bom (hugo.chow-wing-bom.15@ucl.ac.uk), or the senior author, Prof Tessa Dekker (t.dekker@ucl.ac.uk). Requests should include a brief project proposal and will be assessed by Prof Dekker and the study team. Access will be granted subject to a data sharing agreement between the requesting organisation and UCL.

1. 1. Chow-Wing-Bom H
   2. Lisi M
   3. Benson N
   4. Lygo-Frett F
   5. Yu-Wai-Man P
   6. Dick F
   7. Maison-Mor R
   8. Dekker T

   (2026) Zenodo

   Dataset for Chow-Wing-Bom et al., 2025, eLife.

   https://doi.org/10.5281/zenodo.19051439
2. 1. Chow-Wing-Bom H
   2. Benson N

   (2026) Zenodo

   Benson Map Eccentricity Adjustment Tool.

   https://doi.org/10.5281/zenodo.18770065

1. 1. Abadi RV
   2. Bjerre A

   (2002) Motor and sensory characteristics of infantile nystagmus

   The British Journal of Ophthalmology 86:1152–1160.

   https://doi.org/10.1136/bjo.86.10.1152

   • PubMed
   • Google Scholar
2. 1. Aghajari S
   2. Vinke LN
   3. Ling S

   (2020) Population spatial frequency tuning in human early visual cortex

   Journal of Neurophysiology 123:773–785.

   https://doi.org/10.1152/jn.00291.2019

   • PubMed
   • Google Scholar
3. 1. Alvarez GA

   (2011) Representing multiple objects as an ensemble enhances visual cognition

   Trends in Cognitive Sciences 15:122–131.

   https://doi.org/10.1016/j.tics.2011.01.003

   • Google Scholar
4. 1. Barbot A
   2. Xue S
   3. Carrasco M

   (2021) Asymmetries in visual acuity around the visual field

   Journal of Vision 21:2.

   https://doi.org/10.1167/jov.21.1.2

   • PubMed
   • Google Scholar
5. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting linear mixed-effects models using lme4

   Journal of Statistical Software 67:v067.i01.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
6. 1. Benson NC
   2. Butt OH
   3. Brainard DH
   4. Aguirre GK

   (2014) Correction of distortion in flattened representations of the cortical surface allows prediction of V1-V3 functional organization from anatomy

   PLOS Computational Biology 10:e1003538.

   https://doi.org/10.1371/journal.pcbi.1003538

   • PubMed
   • Google Scholar
7. 1. Benson NC
   2. Winawer J

   (2018) Bayesian analysis of retinotopic maps

   eLife 7:e40224.

   https://doi.org/10.7554/eLife.40224

   • PubMed
   • Google Scholar
8. 1. Bourne R
   2. Steinmetz JD
   3. Flaxman S
   4. Briant PS
   5. Taylor HR
   6. Resnikoff S
   7. Casson RJ
   8. Abdoli A
   9. Abu-Gharbieh E
   10. Afshin A
   11. Ahmadieh H
   12. Akalu Y
   13. Alamneh AA
   14. Alemayehu W
   15. Alfaar AS
   16. Alipour V
   17. Anbesu EW
   18. Androudi S
   19. Arabloo J
   20. Arditi A
   21. Asaad M
   22. Bagli E
   23. Baig AA
   24. Bärnighausen TW
   25. Battaglia Parodi M
   26. Bhagavathula AS
   27. Bhardwaj N
   28. Bhardwaj P
   29. Bhattacharyya K
   30. Bijani A
   31. Bikbov M
   32. Bottone M
   33. Braithwaite T
   34. Bron AM
   35. Butt ZA
   36. Cheng CY
   37. Chu DT
   38. Cicinelli MV
   39. Coelho JM
   40. Dagnew B
   41. Dai X
   42. Dana R
   43. Dandona L
   44. Dandona R
   45. Del Monte MA
   46. Deva JP
   47. Diaz D
   48. Djalalinia S
   49. Dreer LE
   50. Ehrlich JR
   51. Ellwein LB
   52. Emamian MH
   53. Fernandes AG
   54. Fischer F
   55. Friedman DS
   56. Furtado JM
   57. Gaidhane AM
   58. Gaidhane S
   59. Gazzard G
   60. Gebremichael B
   61. George R
   62. Ghashghaee A
   63. Golechha M
   64. Hamidi S
   65. Hammond BR
   66. Hartnett MER
   67. Hartono RK
   68. Hay SI
   69. Heidari G
   70. Ho HC
   71. Hoang CL
   72. Househ M
   73. Ibitoye SE
   74. Ilic IM
   75. Ilic MD
   76. Ingram AD
   77. Irvani SSN
   78. Jha RP
   79. Kahloun R
   80. Kandel H
   81. Kasa AS
   82. Kempen JH
   83. Keramati M
   84. Khairallah M
   85. Khan EA
   86. Khanna RC
   87. Khatib MN
   88. Kim JE
   89. Kim YJ
   90. Kisa S
   91. Kisa A
   92. Koyanagi A
   93. Kurmi OP
   94. Lansingh VC
   95. Leasher JL
   96. Leveziel N
   97. Limburg H
   98. Majdan M
   99. Manafi N
   100. Mansouri K
   101. McAlinden C
   102. Mohammadi SF
   103. Mohammadian-Hafshejani A
   104. Mohammadpourhodki R
   105. Mokdad AH
   106. Moosavi D
   107. Morse AR
   108. Naderi M
   109. Naidoo KS
   110. Nangia V
   111. Nguyen CT
   112. Nguyen HLT
   113. Ogundimu K
   114. Olagunju AT
   115. Ostroff SM
   116. Panda-Jonas S
   117. Pesudovs K
   118. Peto T
   119. Quazi Syed Z
   120. Rahman MHU
   121. Ramulu PY
   122. Rawaf S
   123. Rawaf DL
   124. Reinig N
   125. Robin AL
   126. Rossetti L
   127. Safi S
   128. Sahebkar A
   129. Samy AM
   130. Saxena D
   131. Serle JB
   132. Shaikh MA
   133. Shen TT
   134. Shibuya K
   135. Shin JI
   136. Silva JC
   137. Silvester A
   138. Singh JA
   139. Singhal D
   140. Sitorus RS
   141. Skiadaresi E
   142. Skirbekk V
   143. Soheili A
   144. Sousa R
   145. Spurlock EE
   146. Stambolian D
   147. Taddele BW
   148. Tadesse EG
   149. Tahhan N
   150. Tareque MI
   151. Topouzis F
   152. Tran BX
   153. Travillian RS
   154. Tsilimbaris MK
   155. Varma R
   156. Virgili G
   157. Wang YX
   158. Wang N
   159. West SK
   160. Wong TY
   161. Zaidi Z
   162. Zewdie KA
   163. Jonas JB
   164. Vos T

   (2021) Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study

   The Lancet Global Health 9:e130–e143.

   https://doi.org/10.1016/S2214-109X(20)30425-3

   • Google Scholar
9. 1. Broderick WF
   2. Simoncelli EP
   3. Winawer J

   (2022) Mapping spatial frequency preferences across human primary visual cortex

   Journal of Vision 22:3.

   https://doi.org/10.1167/jov.22.4.3

   • PubMed
   • Google Scholar
10. 1. Buracas GT
    2. Fine I
    3. Boynton GM

    (2005) The relationship between task performance and functional magnetic resonance imaging response

    The Journal of Neuroscience 25:3023–3031.

    https://doi.org/10.1523/JNEUROSCI.4476-04.2005

    • PubMed
    • Google Scholar
11. 1. Buracas GT
    2. Boynton GM

    (2007) The effect of spatial attention on contrast response functions in human visual cortex

    The Journal of Neuroscience 27:93–97.

    https://doi.org/10.1523/JNEUROSCI.3162-06.2007

    • PubMed
    • Google Scholar
12. 1. Burr DC
    2. Morrone MC
    3. Ross J

    (1994) Selective suppression of the magnocellular visual pathway during saccadic eye movements

    Nature 371:511–513.

    https://doi.org/10.1038/371511a0

    • PubMed
    • Google Scholar
13. 1. Carrasco M
    2. Roberts M
    3. Myers C
    4. Shukla L

    (2022) Visual field asymmetries vary between children and adults

    Current Biology 32:R509–R510.

    https://doi.org/10.1016/j.cub.2022.04.052

    • PubMed
    • Google Scholar
14. 1. Cauley SF
    2. Polimeni JR
    3. Bhat H
    4. Wald LL
    5. Setsompop K

    (2014) Interslice leakage artifact reduction technique for simultaneous multislice acquisitions

    Magnetic Resonance in Medicine 72:93–102.

    https://doi.org/10.1002/mrm.24898

    • Google Scholar
15. Software

    1. Chow-Wing-Bom H
    2. Benson N

    (2026a) AdjustEccTool, version swh:1:rev:85bba2af3530612050f0166f20343594395e3f30

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:040551a10851989858bc085f62241cb838b26024;origin=https://github.com/HugoCWB/AdjustEccTool;visit=swh:1:snp:a1104936c5c1c1b0ac9711b6ad406e7273cd14a9;anchor=swh:1:rev:85bba2af3530612050f0166f20343594395e3f30
16. Software

    1. Chow-Wing-Bom H

    (2026b) ChowWingBomEtAl_2025_eLife, version swh:1:rev:0014b8fa0797ece45b1bf5cadca99a20e4999f25

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:361ff03e0fb8b3d3a2bcf5c19d29421d3b1ab7c0;origin=https://github.com/HugoCWB/ChowWingBomEtAl_2025_eLife;visit=swh:1:snp:2d16bd12b8e96e065d46a3ff977a2beea5099ca3;anchor=swh:1:rev:0014b8fa0797ece45b1bf5cadca99a20e4999f25
17. 1. DeYoe EA
    2. Carman GJ
    3. Bandettini P
    4. Glickman S
    5. Wieser J
    6. Cox R
    7. Miller D
    8. Neitz J

    (1996) Mapping striate and extrastriate visual areas in human cerebral cortex

    PNAS 93:2382–2386.

    https://doi.org/10.1073/pnas.93.6.2382

    • Google Scholar
18. 1. Dickinson CM
    2. Abadi RV

    (1985) The influence of nystagmoid oscillation on contrast sensitivity in normal observers

    Vision Research 25:1089–1096.

    https://doi.org/10.1016/0042-6989(85)90097-5

    • PubMed
    • Google Scholar
19. 1. D’Souza DV
    2. Auer T
    3. Frahm J
    4. Strasburger H
    5. Lee BB

    (2016) Dependence of chromatic responses in V1 on visual field eccentricity and spatial frequency: an fMRI study

    Journal of the Optical Society of America A 33:A53.

    https://doi.org/10.1364/JOSAA.33.000A53

    • Google Scholar
20. 1. Dumoulin SO
    2. Wandell BA

    (2008) Population receptive field estimates in human visual cortex

    NeuroImage 39:647–660.

    https://doi.org/10.1016/j.neuroimage.2007.09.034

    • Google Scholar
21. 1. Engel SA
    2. Glover GH
    3. Wandell BA

    (1997) Retinotopic organization in human visual cortex and the spatial precision of functional MRI

    Cerebral Cortex 7:181–192.

    https://doi.org/10.1093/cercor/7.2.181

    • PubMed
    • Google Scholar
22. 1. Farahbakhsh M
    2. Anderson EJ
    3. Maimon-Mor RO
    4. Rider A
    5. Greenwood JA
    6. Hirji N
    7. Zaman S
    8. Jones PR
    9. Schwarzkopf DS
    10. Rees G
    11. Michaelides M
    12. Dekker TM

    (2022) A demonstration of cone function plasticity after gene therapy in achromatopsia

    Brain 145:3803–3815.

    https://doi.org/10.1093/brain/awac226

    • PubMed
    • Google Scholar
23. 1. Henriksson L
    2. Nurminen L
    3. Hyvärinen A
    4. Vanni S

    (2008) Spatial frequency tuning in human retinotopic visual areas

    Journal of Vision 8:5.

    https://doi.org/10.1167/8.10.5

    • PubMed
    • Google Scholar
24. 1. Hertle RW
    2. Yang D
    3. Cochran M
    4. Edward PR

    (2017) Clinical contrast sensitivity function in patients with infantile nystagmus syndrome before and after eye muscle surgery

    Journal of Ophthalmology & Clinical Research 4:1–8.

    https://doi.org/10.24966/OCR-8887/100033

    • Google Scholar
25. 1. Himmelberg MM
    2. Winawer J
    3. Carrasco M

    (2020) Stimulus-dependent contrast sensitivity asymmetries around the visual field

    Journal of Vision 20:18.

    https://doi.org/10.1167/jov.20.9.18

    • PubMed
    • Google Scholar
26. 1. Himmelberg MM
    2. Winawer J
    3. Carrasco M

    (2023) Polar angle asymmetries in visual perception and neural architecture

    Trends in Neurosciences 46:445–458.

    https://doi.org/10.1016/j.tins.2023.03.006

    • PubMed
    • Google Scholar
27. 1. Horton JC
    2. Hoyt WF

    (1991) The representation of the visual field in human striate cortex. A revision of the classic Holmes map

    Archives of Ophthalmology 109:816–824.

    https://doi.org/10.1001/archopht.1991.01080060080030

    • PubMed
    • Google Scholar
28. 1. Kurzawski JW
    2. Gulban OF
    3. Jamison K
    4. Winawer J
    5. Kay K

    (2022) Non-neural factors influencing BOLD response magnitudes within individual subjects

    The Journal of Neuroscience 42:7256–7266.

    https://doi.org/10.1523/JNEUROSCI.2532-21.2022

    • PubMed
    • Google Scholar
29. 1. Lange R
    2. Kumagai A
    3. Weiss S
    4. Zaffke KB
    5. Day S
    6. Wicker D
    7. Howson A
    8. Jayasundera KT
    9. Smolinski L
    10. Hedlich C
    11. Lee PP
    12. Massof RW
    13. Stelmack JA
    14. Carlozzi NE
    15. Ehrlich JR

    (2021) Vision-related quality of life in adults with severe peripheral vision loss: A qualitative interview study

    Journal of Patient-Reported Outcomes 5:7.

    https://doi.org/10.1186/s41687-020-00281-y

    • PubMed
    • Google Scholar
30. 1. Lisboa R
    2. Chun YS
    3. Zangwill LM
    4. Weinreb RN
    5. Rosen PN
    6. Liebmann JM
    7. Girkin CA
    8. Medeiros FA

    (2013) Association between rates of binocular visual field loss and vision-related quality of life in patients with glaucoma

    JAMA Ophthalmology 131:486–494.

    https://doi.org/10.1001/jamaophthalmol.2013.2602

    • PubMed
    • Google Scholar
31. 1. Liu T
    2. Heeger DJ
    3. Carrasco M

    (2006) Neural correlates of the visual vertical meridian asymmetry

    Journal of Vision 6:1294–1306.

    https://doi.org/10.1167/6.11.12

    • PubMed
    • Google Scholar
32. 1. Morales MU
    2. Saker S
    3. Wilde C
    4. Pellizzari C
    5. Pallikaris A
    6. Notaroberto N
    7. Rubinstein M
    8. Rui C
    9. Limoli P
    10. Smolek MK
    11. Amoaku WM

    (2016) Reference clinical database for fixation stability metrics in normal subjects measured with the MAIA microperimeter

    Translational Vision Science & Technology 5:6.

    https://doi.org/10.1167/tvst.5.6.6

    • Google Scholar
33. 1. O’Connell C
    2. Ho LC
    3. Murphy MC
    4. Conner IP
    5. Wollstein G
    6. Cham R
    7. Chan KC

    (2016) Structural and functional correlates of visual field asymmetry in the human brain by diffusion kurtosis MRI and functional MRI

    Neuroreport 27:1225–1231.

    https://doi.org/10.1097/WNR.0000000000000682

    • PubMed
    • Google Scholar
34. Book

    1. Oliva A

    (2005) CHAPTER 41—gist of the scene

    In: Itti L, Rees G, Tsotsos JK, editors. Neurobiology of Attention. Academic Press. pp. 251–256.

    https://doi.org/10.1016/B978-012375731-9/50045-8

    • Google Scholar
35. 1. Pawloff M
    2. Linhardt D
    3. Woletz M
    4. Hummer A
    5. Sacu S
    6. Vasileiadi M
    7. Garikoitz LU
    8. Holder G
    9. Schmidt-Erfurth UM
    10. Windischberger C
    11. Ritter M

    (2023) Comparison of stimulus types for retinotopic cortical mapping of macular disease

    Translational Vision Science & Technology 12:6.

    https://doi.org/10.1167/tvst.12.3.6

    • PubMed
    • Google Scholar
36. 1. Prabhakaran GT
    2. Al-Nosairy KO
    3. Tempelmann C
    4. Thieme H
    5. Hoffmann MB

    (2021) Mapping visual field defects With fMRI

    Impact of Approach and Experimental Conditions. Frontiers in Neuroscience 15:745886.

    https://doi.org/10.3389/fnins.2021.745886

    • PubMed
    • Google Scholar
37. 1. Rai BB
    2. Sabeti F
    3. Carle CF
    4. Maddess T

    (2024) Visual field tests: A narrative review of different perimetric methods

    Journal of Clinical Medicine 13:2458.

    https://doi.org/10.3390/jcm13092458

    • PubMed
    • Google Scholar
38. 1. Randall D
    2. Fox SL
    3. Fenner JW
    4. Arblaster GE
    5. Bjerre A
    6. Griffiths HJ

    (2020) Using VR to investigate the relationship between visual acuity and severity of simulated Oscillopsia

    Current Eye Research 45:1611–1618.

    https://doi.org/10.1080/02713683.2020.1772834

    • PubMed
    • Google Scholar
39. Software

    1. R Development Core Team

    (2023) R: a language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
40. 1. Ribeiro FL
    2. Bollmann S
    3. Puckett AM

    (2021) Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning

    NeuroImage 244:118624.

    https://doi.org/10.1016/j.neuroimage.2021.118624

    • PubMed
    • Google Scholar
41. 1. Ritter M
    2. Hummer A
    3. Ledolter AA
    4. Holder GE
    5. Windischberger C
    6. Schmidt-Erfurth UM

    (2019) Correspondence between retinotopic cortical mapping and conventional functional and morphological assessment of retinal disease

    The British Journal of Ophthalmology 103:208–215.

    https://doi.org/10.1136/bjophthalmol-2017-311443

    • PubMed
    • Google Scholar
42. 1. Roelofzen C
    2. Daghlian M
    3. van Dijk JA
    4. de Jong MC
    5. Dumoulin SO

    (2025) Modeling neural contrast sensitivity functions in human visual cortex

    Imaging Neuroscience 3:imag_a_00469.

    https://doi.org/10.1162/imag_a_00469

    • PubMed
    • Google Scholar
43. 1. Roh M
    2. Selivanova A
    3. Shin HJ
    4. Miller JW
    5. Jackson Ml

    (2018) Visual acuity and contrast sensitivity are two important factors affecting vision-related quality of life in advanced age-related macular degeneration

    PLOS ONE 13:e0196481.

    https://doi.org/10.1371/journal.pone.0196481

    • PubMed
    • Google Scholar
44. 1. Ross J
    2. Morrone MC
    3. Goldberg ME
    4. Burr DC

    (2001) Changes in visual perception at the time of saccades

    Trends in Neurosciences 24:113–121.

    https://doi.org/10.1016/s0166-2236(00)01685-4

    • PubMed
    • Google Scholar
45. 1. Rovamo J
    2. Virsu V

    (1979) An estimation and application of the human cortical magnification factor

    Experimental Brain Research 37:495–510.

    https://doi.org/10.1007/BF00236819

    • PubMed
    • Google Scholar
46. 1. Rovamo J
    2. Leinonen L
    3. Laurinen P
    4. Virsu V

    (1984) Temporal integration and contrast sensitivity in foveal and peripheral vision

    Perception 13:665–674.

    https://doi.org/10.1068/p130665

    • PubMed
    • Google Scholar
47. Software

    1. SamSrf

    (2026) SamSrf, version 10.36

    GitHub.

    https://github.com/samsrf
48. 1. Sereno MI
    2. Dale AM
    3. Reppas JB
    4. Kwong KK
    5. Belliveau JW
    6. Brady TJ
    7. Rosen BR
    8. Tootell RBH

    (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging

    Science 268:889–893.

    https://doi.org/10.1126/science.7754376

    • PubMed
    • Google Scholar
49. 1. Subhi H
    2. Latham K
    3. Myint J
    4. Crossland MD

    (2017) Functional visual fields: relationship of visual field areas to self-reported function

    Ophthalmic & Physiological Optics 37:399–408.

    https://doi.org/10.1111/opo.12362

    • PubMed
    • Google Scholar
50. 1. Tailor VK
    2. Theodorou M
    3. Dahlmann-Noor AH
    4. Dekker TM
    5. Greenwood JA

    (2021) Eye movements elevate crowding in idiopathic infantile nystagmus syndrome

    Journal of Vision 21:9.

    https://doi.org/10.1167/jov.21.13.9

    • PubMed
    • Google Scholar
51. 1. Welbourne LE
    2. Morland AB
    3. Wade AR

    (2018) Population receptive field (pRF) measurements of chromatic responses in human visual cortex using fMRI

    NeuroImage 167:84–94.

    https://doi.org/10.1016/j.neuroimage.2017.11.022

    • PubMed
    • Google Scholar
52. Website

    1. World Health Organization

    (2019) WHO launches first World report on vision

    Accessed August 29, 2024.

    https://www.who.int/news/item/08-10-2019-who-launches-first-world-report-on-vision
53. 1. Xu J
    2. Moeller S
    3. Auerbach EJ
    4. Strupp J
    5. Smith SM
    6. Feinberg DA
    7. Yacoub E
    8. Uğurbil K

    (2013) Evaluation of slice accelerations using multiband echo planar imaging at 3 T

    NeuroImage 83:991–1001.

    https://doi.org/10.1016/j.neuroimage.2013.07.055

    • PubMed
    • Google Scholar

Author details

1. Hugo T Chow-Wing-Bom

   1. Institute of Ophthalmology, University College London (UCL), London, United Kingdom
   2. Birkbeck/UCL Centre for NeuroImaging, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   hugo.chow-wing-bom.15@ucl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3348-3005

2. Matteo Lisi

   Department of Psychology, Royal Holloway, University of London, London, United Kingdom

   Contribution

   Software, Formal analysis, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

3. Noah C Benson

   eScience Institute, University of Washington, Seattle, United States

   Contribution

   Software, Formal analysis, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2365-8265

4. Freya Lygo-Frett

   Institute of Cognitive Neuroscience, UCL, London, United Kingdom

   Contribution

   Supervision, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

5. Patrick Yu-Wai-Man

   1. Institute of Ophthalmology, University College London (UCL), London, United Kingdom
   2. John van Geest Centre for Brain Repair and MRC Mitochondrial Biology Unit, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
   3. Cambridge Eye Unit, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
   4. Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom

   Contribution

   Resources, Supervision, Writing – original draft

   Competing interests

   No competing interests declared

6. Frederic Dick

   1. Birkbeck/UCL Centre for NeuroImaging, London, United Kingdom
   2. Department of Experimental Psychology, UCL, London, United Kingdom

   Contribution

   Resources, Supervision, Funding acquisition, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2933-3912

7. Roni O Maimon-Mor

   Institute of Ophthalmology, University College London (UCL), London, United Kingdom

   Contribution

   Conceptualization, Investigation, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5262-9976

8. Tessa M Dekker

   1. Institute of Ophthalmology, University College London (UCL), London, United Kingdom
   2. Birkbeck/UCL Centre for NeuroImaging, London, United Kingdom
   3. Department of Experimental Psychology, UCL, London, United Kingdom

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-3616-2859

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