Crossed-uncrossed projections from primate retina are adapted to disparities of natural scenes.

In mammals with frontal eyes, optic-nerve fibers from nasal retina project to the contralateral hemisphere of the brain, and fibers from temporal retina project ipsilaterally. The division between crossed and uncrossed projections occurs at or near the vertical meridian. If the division was precise, a problem would arise. Small objects near midline, but nearer or farther than current fixation, would produce signals that travel to opposite hemispheres, making the binocular disparity of those objects difficult to compute. However, in species that have been studied, the division is not precise. Rather, there are overlapping crossed and uncrossed projections such that some fibers from nasal retina project ipsilaterally as well as contralaterally and some from temporal retina project contralaterally as well as ipsilaterally. This increases the probability that signals from an object near vertical midline travel to the same hemisphere, thereby aiding disparity estimation. We investigated whether there is a deficit in binocular vision near the vertical meridian in humans and found no evidence for one. We also investigated the effectiveness of the observed decussation pattern, quantified from anatomical data in monkeys and humans. We used measurements of naturally occurring disparities in humans to determine disparity distributions across the visual field. We then used those distributions to calculate the probability of natural disparities transmitting to the same hemisphere, thereby aiding disparity computation. We found that the pattern of overlapping projections is quite effective. Thus, crossed and uncrossed projections from the retinas are well designed for aiding disparity estimation and stereopsis.

Structural covariance and heritability of the optic tract and primary visual cortex in living human brains.

Individual differences among human brains exist at many scales, spanning gene expression, white matter tissue properties, and the size and shape of cortical areas. One notable example is an approximately 3-fold range in the size of human primary visual cortex (V1), a much larger range than is found in overall brain size. A previous study (Andrews et al., 1997) reported a correlation between optic tract cross-section area and V1 size in post-mortem human brains, suggesting that there may be a common developmental mechanism for multiple components of the visual pathways. We evaluated the relationship between properties of the optic tract and V1 in a much larger sample of living human brains by analyzing the Human Connectome Project 7 Tesla Retinotopy Dataset (including 107 females and 71 males). This dataset includes retinotopic maps measured with functional MRI (fMRI) and fiber tract data measured with diffusion MRI (dMRI). We found a negative correlation between optic tract fractional anisotropy and V1 surface area (r = -0.19). This correlation, though small, was consistent across multiple dMRI datasets differing in acquisition parameters. Further, we found that both V1 size and optic tract properties were correlated among twins, with higher correlations for monozygotic than dizygotic twins, indicating a high degree of heritability for both properties. Together, these results demonstrate covariation across individuals in properties of the retina (optic tract) and cortex (V1) and show that each is influenced by genetic factors.SIGNIFICANCE STATEMENT:The size of human primary visual cortex (V1) has large inter-individual differences. These differences do not scale with overall brain size. A previous post-mortem study reported a correlation between the size of the human optic tract and V1. In this study, we evaluated the relationship between the optic tract and V1 in living humans by analyzing a neuroimaging dataset that included functional and diffusion MRI data. We found a small, but robust correlation between optic tract tissue properties and V1 size, supporting the existence of structural covariance between the optic tract and V1 in living humans. The results suggest that characteristics of retinal ganglion cells, reflected in optic tract measurements, are related to individual differences in human V1.

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

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 (109 female, 72 male), 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 threefold across individuals. This threefold 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.SIGNIFICANCE STATEMENT Areas V1, V2, and V3 are among the best studied functionally defined regions in human cortex. Using the largest retinotopy dataset to date, we characterized the variability of these regions across individuals and the similarity between twin pairs. We find that the size of visual areas varies dramatically (up to 3.5×) across healthy young adults, far more than the variability of the cerebral cortex size as a whole. Much of this variability appears to arise from inherited factors, as we find very high correlations in visual area size between monozygotic twin pairs, and lower but still substantial correlations between dizygotic twin pairs. These results provide the most comprehensive assessment of how functionally defined visual cortex varies across the population to date.

Optic radiations representing different eccentricities age differently.

The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.

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

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 polar angle 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 cone photon absorptions contributes to polar angle asymmetries in performance. First, we quantify the extent of asymmetries in cone density, midget RGC density, and V1 CMF. We find that both polar angle asymmetries and eccentricity gradients increase from cones to mRGCs, and from mRGCs to cortex. Second, we extend our previously published computational observer model to quantify the contribution of phototransduction by the cones and spatial filtering by mRGCs to behavioral asymmetries. Starting with photons emitted by a visual display, the model simulates the effect of human optics, cone isomerizations, phototransduction, 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 that asymmetries in a decision maker's performance across polar angle are greater when assessing the photocurrents than when assessing isomerizations and are greater still when assessing mRGC signals. Nonetheless, the polar angle asymmetries of the mRGC outputs are still considerably smaller than those observed from human performance. We conclude that cone isomerizations, phototransduction, and the spatial filtering properties of mRGCs contribute to polar angle performance differences, but that a full account of these differences will entail additional contribution from cortical representations.

A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex.

Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.

Cross-dataset reproducibility of human retinotopic maps.

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. Here, 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 simultaneously differ in their stimulus apertures, participant pool, fMRI protocol, MRI field strength, and preprocessing pipeline. We assessed the cross-dataset reproducibility of the two datasets in terms of the similarity of vertex-wise pRF estimates and in terms of large-scale polar angle asymmetries in cortical magnification. 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 NYU dataset confirms earlier reports that more cortical surface area represents horizontal than vertical visual field meridian, and lower than upper vertical visual field meridian. Together, our findings show that the retinotopic properties of V1, V2, V3, and hV4 can be reliably measured across two datasets, despite numerous differences in their experimental design. fMRI-derived retinotopic maps are reproducible because they rely on an explicit computational model of the fMRI response. In the case of pRF mapping, the model 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 7T Retinotopy Dataset.

A population receptive field model of the magnetoencephalography response.

Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject's pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.

Predicting neuronal dynamics with a delayed gain control model.

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements-intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.

Electrocorticography Evidence of Tactile Responses in Visual Cortices.

There is ongoing debate regarding the extent to which human cortices are specialized for processing a given sensory input versus a given type of information, independently of the sensory source. Many neuroimaging and electrophysiological studies have reported that primary and extrastriate visual cortices respond to tactile and auditory stimulation, in addition to visual inputs, suggesting these cortices are intrinsically multisensory. In particular for tactile responses, few studies have proven neuronal processes in visual cortex in humans. Here, we assessed tactile responses in both low-level and extrastriate visual cortices using electrocorticography recordings in a human participant. Specifically, we observed significant spectral power increases in the high frequency band (30-100 Hz) in response to tactile stimuli, reportedly associated with spiking neuronal activity, in both low-level visual cortex (i.e. V2) and in the anterior part of the lateral occipital-temporal cortex. These sites were both involved in processing tactile information and responsive to visual stimulation. More generally, the present results add to a mounting literature in support of task-sensitive and sensory-independent mechanisms underlying functions like spatial, motion, and self-processing in the brain and extending from higher-level as well as to low-level cortices.

Compressive Temporal Summation in Human Visual Cortex.

Combining sensory inputs over space and time is fundamental to vision. Population receptive field models have been successful in characterizing spatial encoding throughout the human visual pathways. A parallel question, how visual areas in the human brain process information distributed over time, has received less attention. One challenge is that the most widely used neuroimaging method, fMRI, has coarse temporal resolution compared with the time-scale of neural dynamics. Here, via carefully controlled temporally modulated stimuli, we show that information about temporal processing can be readily derived from fMRI signal amplitudes in male and female subjects. We find that all visual areas exhibit subadditive summation, whereby responses to longer stimuli are less than the linear prediction from briefer stimuli. We also find fMRI evidence that the neural response to two stimuli is reduced for brief interstimulus intervals (indicating adaptation). These effects are more pronounced in visual areas anterior to V1-V3. Finally, we develop a general model that shows how these effects can be captured with two simple operations: temporal summation followed by a compressive nonlinearity. This model operates for arbitrary temporal stimulation patterns and provides a simple and interpretable set of computations that can be used to characterize neural response properties across the visual hierarchy. Importantly, compressive temporal summation directly parallels earlier findings of compressive spatial summation in visual cortex describing responses to stimuli distributed across space. This indicates that, for space and time, cortex uses a similar processing strategy to achieve higher-level and increasingly invariant representations of the visual world.SIGNIFICANCE STATEMENT Combining sensory inputs over time is fundamental to seeing. Two important temporal phenomena are summation, the accumulation of sensory inputs over time, and adaptation, a response reduction for repeated or sustained stimuli. We investigated these phenomena in the human visual system using fMRI. We built predictive models that operate on arbitrary temporal patterns of stimulation using two simple computations: temporal summation followed by a compressive nonlinearity. Our new temporal compressive summation model captures (1) subadditive temporal summation, and (2) adaptation. We show that the model accounts for systematic differences in these phenomena across visual areas. Finally, we show that for space and time, the visual system uses a similar strategy to achieve increasingly invariant representations of the visual world.

The Human Connectome Project 7 Tesla retinotopy dataset: Description and population receptive field analysis.

About a quarter of human cerebral cortex is dedicated mainly to visual processing. The large-scale spatial organization of visual cortex can be measured with functional magnetic resonance imaging (fMRI) while subjects view spatially modulated visual stimuli, also known as "retinotopic mapping." One of the datasets collected by the Human Connectome Project involved ultrahigh-field (7 Tesla) fMRI retinotopic mapping in 181 healthy young adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we describe the experimental paradigm and the results of model-based analysis of the fMRI data. These results provide estimates of population receptive field position and size. Our analyses include both results from individual subjects as well as results obtained by averaging fMRI time series across subjects at each cortical and subcortical location and then fitting models. Both the group-average and individual-subject results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal cortex as well as subcortical areas. The group-average results agree well with previously published parcellations of visual areas. In addition, split-half analyses show strong within-subject reliability, further demonstrating the high quality of the data. We make publicly available the analysis results for individual subjects and the group average, as well as associated stimuli and analysis code. These resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical organization and the properties of high-resolution fMRI. In addition, they provide a set of observations that can be compared with other Human Connectome Project measures acquired in these same participants.

Resting-State Retinotopic Organization in the Absence of Retinal Input and Visual Experience.

Early visual areas have neuronal receptive fields that form a sampling mosaic of visual space, resulting in a series of retinotopic maps in which the same region of space is represented in multiple visual areas. It is not clear to what extent the development and maintenance of this retinotopic organization in humans depend on retinal waves and/or visual experience. We examined the corticocortical receptive field organization of resting-state BOLD data in normally sighted, early blind, and anophthalmic (in which both eyes fail to develop) individuals and found that resting-state correlations between V1 and V2/V3 were retinotopically organized for all subject groups. These results show that the gross retinotopic pattern of resting-state connectivity across V1-V3 requires neither retinal waves nor visual experience to develop and persist into adulthood. Significance statement: Evidence from resting-state BOLD data suggests that the connections between early visual areas develop and are maintained even in the absence of retinal waves and visual experience.

Unsupervised learning of cone spectral classes from natural images.

The first step in the evolution of primate trichromatic color vision was the expression of a third cone class not present in ancestral mammals. This observation motivates a fundamental question about the evolution of any sensory system: how is it possible to detect and exploit the presence of a novel sensory class? We explore this question in the context of primate color vision. We present an unsupervised learning algorithm capable of both detecting the number of spectral cone classes in a retinal mosaic and learning the class of each cone using the inter-cone correlations obtained in response to natural image input. The algorithm's ability to classify cones is in broad agreement with experimental evidence about functional color vision for a wide range of mosaic parameters, including those characterizing dichromacy, typical trichromacy, anomalous trichromacy, and possible tetrachromacy.

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

Several domains of neuroscience offer map-like models that link location on the cortical surface to properties of sensory representation. Within cortical visual areas V1, V2, and V3, algebraic transformations can relate position in the visual field to the retinotopic representation on the flattened cortical sheet. A limit to the practical application of this structure-function model is that the cortex, while topologically a two-dimensional surface, is curved. Flattening of the curved surface to a plane unavoidably introduces local geometric distortions that are not accounted for in idealized models. Here, we show that this limitation is overcome by correcting the geometric distortion induced by cortical flattening. We use a mass-spring-damper simulation to create a registration between functional MRI retinotopic mapping data of visual areas V1, V2, and V3 and an algebraic model of retinotopy. This registration is then applied to the flattened cortical surface anatomy to create an anatomical template that is linked to the algebraic retinotopic model. This registered cortical template can be used to accurately predict the location and retinotopic organization of these early visual areas from cortical anatomy alone. Moreover, we show that prediction accuracy remains when extrapolating beyond the range of data used to inform the model, indicating that the registration reflects the retinotopic organization of visual cortex. We provide code for the mass-spring-damper technique, which has general utility for the registration of cortical structure and function beyond the visual cortex.

The fine-scale functional correlation of striate cortex in sighted and blind people.

To what extent are spontaneous neural signals within striate cortex organized by vision? We examined the fine-scale pattern of striate cortex correlations within and between hemispheres in rest-state BOLD fMRI data from sighted and blind people. In the sighted, we find that corticocortico correlation is well modeled as a Gaussian point-spread function across millimeters of striate cortical surface, rather than degrees of visual angle. Blindness produces a subtle change in the pattern of fine-scale striate correlations between hemispheres. Across participants blind before the age of 18, the degree of pattern alteration covaries with the strength of long-range correlation between left striate cortex and Broca's area. This suggests that early blindness exchanges local, vision-driven pattern synchrony of the striate cortices for long-range functional correlations potentially related to cross-modal representation.

Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.

BACKGROUND: Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS: We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS: We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS: Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

In this study, we explored the relationship between glaucoma, the most common cause of blindness, and changes within the brain. We used data from diffusion MRI, a measurement method which assesses the properties of brain connections. We examined 905 individuals with glaucoma alongside 5292 healthy people. We refined the test cohort to be closely matched in age, sex, ethnicity, and socioeconomic backgrounds. The use of deep learning neural networks allowed accurate detection of glaucoma by focusing on the tissue properties of the optic radiations, a major brain pathway that transmits visual information, rather than other brain pathways used for comparison. Our work provides additional evidence that brain connections may age differently based on varying sensory inputs.

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

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.

Primary visual cortical thickness in correlation with visual field defects in patients with pituitary macroadenomas: a structural 7-Tesla retinotopic analysis.

OBJECTIVE: Vision loss remains a debilitating complication of pituitary adenomas, although there is considerable variability in visual impairment before and after decompression surgery. Growing evidence suggests secondary damage to remote visual structures may contribute to vision loss in patients with chiasmatic compression. The present study leverages ultrahigh field 7-T MRI to study the retinotopic organization of the primary visual cortex (V1), and correlates visual defects with cortical thinning in V1 to characterize consequences of pituitary adenomas on the posterior visual system. METHODS: Eight patients (4 males and 4 females, mean age 44.3 years) with pituitary adenomas who exhibited chiasmatic compression and visual field defects, as well as 8 matched healthy controls (4 males and 4 females, mean age 43.3 years), were scanned at 7-T MRI for this prospective study. Whole-brain cortical thickness was calculated using an automated algorithm. A previously published surface-based algorithm was applied to associate the eccentricity and polar angle with each position in V1. Cortical thickness was calculated at each point in the retinotopic organization, and a cortical thickness ratio was generated against matched controls for each point in the visual fields. Patients with adenoma additionally underwent neuroophthalmological examination including 24-2 Humphrey automated visual field perimetry. Pattern deviation (PD) of each point in the visual field, i.e., the deviation in point detection compared with neurologically healthy controls, was correlated with cortical thickness at corresponding polar and eccentricity angles in V1. RESULTS: Whole-brain cortical thickness was successfully derived for all patients and controls. The mean tumor volume was 19.4 cm3. The median global thickness of V1 did not differ between patients (mean ± SD 2.21 ± 0.12 cm), compared with controls (2.06 ± 0.13 cm, p > 0.05). Surface morphometry-based retinotopic maps revealed that all 8 patients with adenoma showed a significant positive correlation between PD and V1 thickness ratios (r values ranged from 0.31 to 0.53, p < 0.05). Mixed-procedure analysis revealed that PD = -8.0719 + 5.5873*[Median V1 Thickness Ratio]. CONCLUSIONS: All 8 patients showed significant positive correlations between V1 thickness and visual defect. These findings provide retinotopic maps of localized V1 cortical neurodegeneration spatially corresponding to impairments in the visual field. These results further characterize changes in the posterior visual pathway associated with chiasmatic compression, and may prove useful in the neuroophthalmological workup for patients with pituitary macroadenoma.