Biology subjects

Winawer, J.

Publications and source records attributed to Winawer, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Normalization accounts for temporal dynamics in human somatosensory cortex

Sensory processing is fundamentally shaped by stimulation history. For example, in visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (6 participants) and intracranial electroencephalography (iEEG, 2 participants) to measure responses to time-varying vibrotactile stimuli in human somatosensory cortex. Stimuli consisted of single- and paired-pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes, and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced sub-additive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time. Significance statementHow the brain integrates sensory information over time is a fundamental question in neuroscience. While nonlinear temporal integration is well documented in visual cortex, it has not been extensively mapped in the human somatosensory system. By combining fMRI with intracranial EEG in humans, we demonstrate that somatosensory responses to tactile stimulation exhibit subadditive temporal summation. This nonlinearity is accurately captured by a divisive normalization model, matching observations in the visual system. Our results suggest that normalization is a canonical computation shared across different modalities to manage temporal dynamics, providing a unified framework for understanding how the brain encodes dynamic sensory stimuli.

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bioRxiv2026

Object recognition along the ventral stream: effects of visual noise

How does the visual system recognize objects in natural environments? Here, we investigate the neural computations underlying robust object recognition along the ventral stream. We adapted psychophysical critical-band masking to fMRI, measuring BOLD responses along the ventral stream to natural images perturbed by bandpass noise. We found that, along this stream, the overall BOLD response to noise alone is sensitive to an increasingly wide range of stimulus spatial frequency, from 2 octaves in V1 to 5 octaves in ventral temporal cortex (VTC). However, when we assess the effect of the same noise on the accuracy of decoding scene images from the BOLD response, we obtain a different result: Recognition bandwidth is conserved along the ventral stream at about 2 octaves, close to the 1.5-octave behavioral channel. Though the recognition band is conserved, its noise tolerance increases steadily along the ventral stream, approaching behavioral levels in VTC. These findings suggest that V1 sets the bandwidth of the object-recognition channel, while downstream areas progressively denoise the signal, establishing the channels noise tolerance. This biological architecture--early channel isolation followed by successive denoising--may explain why human vision remains more robust than current machine vision.

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bioRxiv2025

Neuronal or vascular receptive fields? On the relation between BOLD-fMRI population receptive field (pRF) estimates and cortical vascularization.

This study investigates the contribution of different vascular compartments on population receptive field (pRF) size estimates within the early visual cortex (V1, V2, and V3) using BOLD-fMRI. We employed T2*-weighted gradient-echo (GE) and T2-weighted spin-echo (SE) sequences at 7 Tesla (7T) and a multi-band GE sequence at 3T to explore how different vessel sensitivities across these sequences influence pRF modeling. Our results confirm the expected pRF size increase across eccentricity and visual areas but found no significant differences in pRF size estimates across MR sequences, voxel sizes, or field strengths. BOLD signal amplitudes were influenced by MR sequence, with largest signal changes observed for 7TGE, and amplitude increases were noted across cortical depth for GE sequences but not for SE sequences. Contrary to our hypotheses, pRF size estimates were not noticeably affected by the local vascularization, suggesting that pRFs primarily reflect neuronal activity rather than vascular compartment characteristics. Our study highlights the robust nature of pRF size estimates across various fMRI conditions and points toward the decoupling of pRF properties from vascular factors.

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bioRxiv2025

Machine Learning Matches Human Performance at Segmenting the Human Visual Cortex

A major problem in human visual neuroscience research is the localization of visual areas on the cortical surface. Currently available methods are capable of making detailed predictions about many areas, but human raters do not agree as well with these methods as they do with each other. Although highly accurate, human raters require substantial time and expertise that many researchers do not have. Additionally, human raters require functional data for drawing visual area boundaries that requires additional scan time, budget, and expertise to collect. Here, we train convolutional neural network (CNN) models to predict the boundaries and iso-eccentric regions of V1, V2, and V3 in both the Human Connectome Project dataset and the NYU Retinotopy dataset. CNNs trained to use the same functional data available to human raters predicted these boundaries with an accuracy similar to humans, while CNNs trained to use only anatomical data had a lower accuracy that was nonetheless higher than that of any currently available method. A comparison of the model accuracies when predicting eccentricity-based boundaries and polar angle-based boundaries suggests that eccentricity is substantially less closely tied to anatomical structure than polar angle and that the cortical magnification function, at least in terms of eccentricity, varies substantially between subjects. We further find that the fraction of V1, V2, and V3 that can be accurately parcellated into function regions using gray matter structural data alone is [~]75% ([~]80% of the inter-rater reliability of human experts), implying a much tighter coupling between structure and function in these areas than previously estimated. We conclude that machine learning techniques such as CNNs provide a powerful tool for mapping the brain with human accuracy and predict that such tools will become integral to neuroscience research going forward.

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bioRxiv2025

Memory responses in visual cortex track recall success after single-trial encoding

Episodic memory retrieval is thought to rely on the reactivation of prior perceptual representations in sensory cortex, a phenomenon known as cortical reinstatement. Support for this idea in early sensory areas comes from memory studies involving repeated exposure and explicit recall instructions. In everyday life, however, people rely on details from single events without repeated practice or instruction. We used fMRI to test whether memory responses emerge in early visual cortex after a single encoding event. Twenty adults viewed objects presented once in one of four parafoveal locations, then later completed object recognition (old/new) and location recall tasks in which objects were shown foveally. Both memory tasks evoked spatially tuned, albeit substantially lower amplitude, responses compared to encoding in V1 to V3. These responses were tuned to the encoded parafoveal location, even though the recognition task did not ask the participant to remember the encoded location. These responses were more robust and precise for successfully remembered items, linking neural tuning in early visual cortex to one-shot memory performance. Our findings demonstrate that early visual cortex supports the defining property of episodic memory, the ability to retrieve sensory details from a single event. Significance StatementEpisodic memory allows us to vividly recall details from single past experiences. While it is hypothesized that this "mental time travel" relies on reactivating sensory cortex, previous studies used highly practiced associations or explicit visualization cues, which differ from how we naturally recall unique events. In this fMRI study, participants viewed a stream of unique objects at parafoveal locations. We found that later retrieval of these objects spontaneously reactivated early visual cortex in a spatially specific manner even when spatial location was not probed: The memory reactivated the same retinotopic location where the object initially appeared. Furthermore, the precision of this reactivation predicted memory success. These findings demonstrate that sensory cortex is fundamental to the rapid, one-shot learning that characterizes human episodic memory.

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bioRxiv2025

Preferred spatial frequency covaries with cortical magnification in human primary visual cortex

Primary visual cortex (V1) has long served as a model system for understanding cortical organization. Although its structural and functional properties vary markedly across its surface, patterns of covariation suggest possible underlying constancies. Such constancies would imply that V1 is composed of multiple identical units whose visual properties differ only due to differences in their inputs. To test this, we used fMRI to investigate how V1 cortical magnification and preferred spatial frequency covary with eccentricity and polar angle, measured in 40 observers. V1 cortical magnification and preferred spatial frequency were strongly correlated across eccentricity and around polar angle, however their relation differed between these dimensions: they were proportional across eccentricity but not polar angle. The constant ratio of cortical magnification to preferred spatial frequency when measured as a function of eccentricity suggests a shared underlying cause of variation in the two properties, e.g., the gradient of retinal ganglion cell density across eccentricity. In contrast, the deviation from proportionality around polar angle implies that cortical variation differs from that in retina along this dimension. Thus, a constancy hypothesis is supported for one of the two spatial dimensions of V1, highlighting the importance of examining the full 2D-map to understand how V1 is organized. AUTHOR SUMMARYHuman primary visual cortex (V1) is the first region of the brain to receive visual input. Its organization, whether built from uniform repeating units or following more complex patterns, remains debated. We assessed this by measuring two key V1 properties: cortical magnification (how much cortex processes each region of the visual field) and preferred spatial frequency (the level of detail neurons respond best to). Using MRI, we found these properties are closely linked, but their relation differs by visual field dimension. Across eccentricity (distance from the center of gaze), the two properties decrease at a fixed ratio, reflecting structural patterns in the retina. Around polar angle (circular dimension), this proportionality no longer holds, even though the two properties still covary. These results show that whereas V1 is partly governed by a simple rule of proportionality, its organization cannot be explained as just repeating units. Instead, different spatial dimensions follow different principles, emphasizing the need to consider the full two-dimensional layout of the visual map to understand how vision is represented in the brain.

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bioRxiv2025

EasyEyes: Crowded Dynamic Fixation for Online Psychophysics

Online vision testing enables efficient data collection from diverse participants but often requires accurate fixation. When needed, fixation accuracy is traditionally ensured by using a camera to track gaze. That works well in the lab, but tracking during online testing with a built-in webcam is not yet sufficiently precise. Kurzawski, Pombo, et al. (2023) introduced a fixation task that improves fixation through hand-eye coordination, requiring participants to track a moving crosshair with a mouse-controlled cursor. This dynamic fixation task greatly reduces peeking at peripheral targets relative to a stationary fixation task, but does not eliminate it. Here, we introduce a crowded dynamic fixation task that further enhances fixation by adding clutter around the fixation mark to leverage crowding. We assessed fixation accuracy during peripheral threshold measurement. Relative to the RMS gaze error during the stationary fixation task, dynamic fixation error was 61%, while crowded dynamic fixation error was only 47%. With a 1.5{degrees} tolerance, peeking occurred on 9% of trials with stationary fixation, 4% with dynamic fixation, and 0% with crowded dynamic fixation. This improvement eliminated implausibly low peripheral thresholds, likely by preventing peeking. We conclude that crowded dynamic fixation provides accurate gaze control for online testing.

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bioRxiv2025

Spatial Frequency Maps in Human Visual Cortex: A Replication and Extension

In a step toward developing a model of human primary visual cortex, a recent study introduced a model of spatial frequency tuning in V1 (Broderick et al., 2022). The model is compact, using just 9 parameters to predict BOLD response amplitude for locations across all of V1 as a function of stimulus orientation and spatial frequency. Here we replicated this analysis in a new dataset, the nsdsynthetic supplement to the Natural Scenes Dataset (Allen et al., 2022), to assess generalization of model parameters. Furthermore, we extended the analyses to extrastriate maps V2 and V3. For each retinotopic map in the 8 NSD subjects, we fit the 9-parameter model. Despite many experimental differences between NSD and the original study, including stimulus size, experimental design, and MR field strength, there was good agreement in most model parameters. The dependence of preferred spatial frequency on eccentricity in V1 was similar between NSD and Broderick et al. Moreover, the effect of absolute stimulus orientation on spatial frequency maps was similar: higher preferred spatial frequency for horizontal and cardinal orientations compared to vertical and oblique orientations in both studies. The extension to extrastriate maps revealed that the biggest change in tuning between maps was in bandwidth: the bandwidth in spatial frequency tuning increased by 70% from V1 to V2 and 100% from V1 to V3. This implies that higher visual areas are sensitive to a greater range of stimulus spatial frequencies, paralleling the fact that higher visual areas are sensitive to a greater range in location (i.e., larger receptive fields). Together, the results show robust reproducibility and bring us closer to a systematic characterization of spatial encoding in the human visual system.

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bioRxiv2024

Traveling Waves in the Human Visual Cortex: a MEG-EEG Model-Based Approach

Brain oscillations might be traveling waves propagating in cortex. Studying their propagation within single cortical areas has mostly been restricted to invasive measurements. Their investigation in healthy humans, however, requires non-invasive recordings, such as MEG or EEG. Identifying traveling waves with these techniques is challenging because source summation, volume conduction, and low signal-to-noise ratios make it difficult to localize cortical activity from sensor responses. The difficulty is compounded by the lack of a known ground truth in traveling wave experiments. Rather than source-localizing cortical responses from sensor activity, we developed a two-part model-based neuroimaging approach: (1) The putative neural sources of a propagating oscillation were modeled within primary visual cortex (V1) via retinotopic mapping from functional MRI recordings (encoding model); and (2) the modeled sources were projected onto MEG and EEG sensors to predict the resulting signal using a biophysical head model. We tested our model by comparing its predictions against the MEG-EEG signal obtained when participants viewed visual stimuli designed to elicit either fovea-to-periphery or periphery-to-fovea traveling waves or standing waves in V1, in which ground truth cortical waves could be reasonably assumed. Correlations on within-sensor phase and amplitude relations between predicted and measured data revealed good model performance. Crucially, the model predicted sensor data more accurately when the input to the model was a traveling wave going in the stimulus direction compared to when the input was a standing wave, or a traveling wave in a different direction. Furthermore, model accuracy peaked at the spatial and temporal frequency parameters of the visual stimulation. Together, our model successfully recovers traveling wave properties in cortex when they are induced by traveling waves in stimuli. This provides a sound basis for using MEG-EEG to study endogenous traveling waves in cortex and test hypotheses related with their role in cognition. Author SummaryBrain oscillations, thought to be crucial for many cognitive processes, might actually be waves that travel across the brains surface. Understanding these traveling waves is notoriously difficult because current non-invasive methods like magneto- and electro-encephalography (MEG-EEG) face significant technical limitations. To address this challenge, we developed a new approach that combines brain imaging techniques and computational modeling. We focused on the primary visual cortical area (V1) of the brain and created a model that simulates traveling activity across the cortex and predicts how these traveling waves should appear in EEG and MEG recordings. We tested our model by comparing its predictions with brain data collected when participants view visual patterns specifically designed to induce traveling waves in the visual system. The results show that our model accurately captures the direction and pattern of the traveling waves, as well as the specific parameters of the visual stimuli. This novel modeling tool offers a promising method for studying endogenous traveling waves and will enable neuroscientists to explore hypotheses about the spatiotemporal organization of brain activity and its role in cognition.

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bioRxiv2024

Feedback scales the spatial tuning of cortical responses during visual memory

Perception, working memory, and long-term memory each evoke neural responses in visual cortex. While previous neuroimaging research on the role of visual cortex in memory has largely emphasized similarities between perception and memory, we hypothesized that responses in visual cortex would differ depending on the origins of the inputs. Using fMRI, we quantified spatial tuning in visual cortex while participants (both sexes) viewed, maintained in working memory, or retrieved from long-term memory a peripheral target. In each condition, BOLD responses were spatially tuned and aligned with the targets polar angle in all measured visual field maps including V1. As expected given the increasing sizes of receptive fields, polar angle tuning during perception increased in width up the visual hierarchy from V1 to V2, V3, hV4, and beyond. In stark contrast, the tuned responses were broad across the visual hierarchy during long-term memory (replicating a prior result) and during working memory. This pattern is consistent with the idea that mnemonic responses in V1 stem from top-down sources, even when the stimulus was recently viewed and is held in working memory. Moreover, in long-term memory, trial-to-trial biases in these tuned responses (clockwise or counterclockwise of target), predicted matched biases in memory, suggesting that the reinstated cortical responses influence memory guided behavior. We conclude that feedback widens spatial tuning in visual cortex during memory, where earlier visual maps inherit broader tuning from later maps thereby impacting the precision of memory. Significance StatementWe demonstrate that remembering a visual stimulus evokes responses in visual cortex that differ in spatial extent compared to seeing the same stimulus. Perception evokes tuned responses in early visual areas that increase in size up the visual hierarchy. Prior work showed that feedback inputs associated with long-term memory originate from later visual areas with larger receptive fields resulting in uniformly wide spatial tuning even in primary visual cortex. We replicate these results and show that the same pattern holds when maintaining in working memory a recently viewed stimulus. That trial-to-trial difficulty is reflected in the accuracy and precision of these representations suggests that visual cortex is flexibly used for processing visuospatial information, regardless of where that information originates.

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bioRxiv2024

Conservation of cortical crowding distance across individuals in human V4

Visual recognition is limited by both object size (acuity) and spacing. The spacing limit, called "crowding", is the failure to recognize an object in the presence of other objects. Here, we take advantage of individual differences in crowding to investigate its biological basis. Crowding distance, the minimum object spacing needed for recognition, varies 2-fold among healthy adults. We test the conjecture that this variation in psychophysical crowding distance is due to variation in cortical map size. To test this, we made paired measurements of brain and behavior in 50 observers. We used psychophysics to measure crowding distance and calculate{lambda} , the number of letters that fit into each observers visual field without crowding. In the same observers, we used fMRI to measure the surface area A (mm{superscript 2}) of retinotopic maps V1, V2, V3, and V4. Across observers,{lambda} is proportional to the surface area of V4 but is uncorrelated with the surface area of V1 to V3. The proportional relationship of{lambda} to area of V4 indicates conservation of cortical crowding distance across individuals: letters can be recognized if they are spaced by at least 1.4 mm on the V4 map, irrespective of map size and psychophysical crowding distance. We conclude that the size of V4 predicts the spacing limit of visual perception.

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bioRxiv2024

Spatial attention alters visual cortical representation during target anticipation

Attention enables us to efficiently and flexibly interact with the environment by prioritizing specific image locations and features in preparation for responding to stimuli. Using a concurrent psychophysics-fMRI experiment, we investigated how covert spatial attention modulates responses in human visual cortex before target onset and how it affects subsequent behavioral performance. Performance improved at cued locations and worsened at uncued locations compared to distributed attention, demonstrating a selective processing tradeoff. Pre-target BOLD responses in cortical visual field maps revealed two key changes: First, a stimulus-independent baseline shift, with increases near cued locations and decreases elsewhere, paralleling behavioral results. Second, a shift in population receptive field centers toward the attended location. Both effects increased in higher visual areas. Together, these findings reveal that spatial attention has large effects on visual cortex prior to target appearance, altering neural response properties across multiple visual field maps and enhancing performance through anticipatory mechanisms.

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bioRxiv2023

Temporal dynamics of neural adaptation across human visual cortex

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventraland lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses across the human visual hierarchy and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences. Author summaryNeural responses in visual cortex adapt over time, with reduced responses to prolonged and repeated stimuli. Here, we examine how adaptation patterns differ across the visual hierarchy in neural responses recorded from human visual cortex with high temporal and spatial precision. To identify possible neural computations underlying short-term adaptation, we fit the response time courses using a temporal divisive normalization model. The model accurately predicts prolonged and repeated responses in lower and higher visual areas, and reveals differences in temporal adaptation across the visual hierarchy and stimulus categories. Our model suggests that differences in adaptation patterns result from differences in divisive normalization dynamics. Our findings shed light on how information is integrated in the brain on a millisecond-time scale and offer an intuitive framework to study the emergence of neural dynamics across brain areas and stimuli.

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bioRxiv2023

Foveated metamers of the early visual system

The ability of humans to discriminate and identify spatial patterns varies across the visual field, and is generally worse in the periphery than in the fovea. This decline in performance is revealed in many kinds of tasks, from detection to recognition. A parsimonious hypothesis is that the representation of any visual feature is blurred (spatially averaged) by an amount that differs for each feature, but that in all cases increases with eccentricity. Here, we examine models for two such features: local luminance and spectral energy. Each model averages the corresponding feature in pooling windows whose diameters scale linearly with eccentricity. We performed perceptual experiments with synthetic stimuli to determine the largest window scaling for which human and model discrimination abilities match (the "critical" scaling). We used much larger stimuli than those of previous studies, subtending 53.6 by 42.2 degrees of visual angle. We found that the critical scaling for the luminance model was approximately one-fourth that of the energy model and, consistent with earlier studies, that the estimated critical scaling value was smaller when discriminating a synthesized stimulus from a natural image than when discriminating two synthesized stimuli. Moreover, we found that initializing the generation of the synthesized images with natural images reduced the critical scaling value when discriminating two synthesized stimuli, but not when discriminating a synthesized from a natural image stimulus. Together, the results show that critical scaling is strongly affected by the image statistic (pooled luminance vs. spectral energy), the comparison type (synthesized vs. synthesized or synthesized vs. natural), and the initialization image for synthesis (white noise vs natural image). We offer a coherent explanation for these results in terms of alignments and misalignments of the models with human perceptual representations.

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bioRxiv2023

Spatial Tuning of Alpha Oscillations in Human Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWNeuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz) but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance StatementThe alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention. Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

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bioRxiv2022

Comparing visual cortex of children and adults reveals a late-stage change in the architecture of V1

Adult visual performance differs with angular location -it is better for stimuli along the horizontal than vertical, and lower than upper vertical meridian of the visual field. These perceptual asymmetries are paralleled by asymmetries in cortical surface area in primary visual cortex (V1). Children, unlike adults, have similar visual performance at the lower and upper vertical meridian. Do children have similar V1 surface area representing the upper and lower vertical meridian? Using MRI, we measure the surface area of retinotopic maps (V1-V3) in children and adults. Many features of the maps are similar between groups, including greater V1 surface area for the horizontal than vertical meridian. However, unlike adults, children have a similar amount of V1 surface area representing the lower and upper vertical meridian. These data reveal a late-stage change in V1 organization that may relate to the emergence of the visual performance asymmetry along the vertical meridian by adulthood.

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bioRxiv2022

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. 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 = -0.2). 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 statementThe size of human primary visual cortex (V1) has large inter-individual differences. These differences cannot be explained by differences in 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.

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bioRxiv2021

The influence of non-neural factors on BOLD signal magnitude

To what extent is the size of the blood-oxygen-level-dependent (BOLD) response influenced by factors other than neural activity? In a re-analysis of three neuroimaging datasets (male and female human participants), we find large systematic inhomogeneities in the BOLD response magnitude in primary visual cortex (V1): stimulus-evoked BOLD responses, expressed in units of percent signal change, are up to 50% larger along the representation of the horizontal meridian than the vertical meridian. To assess whether this surprising effect can be interpreted as differences in local neural activity, we quantified several factors that potentially contribute to the size of the BOLD response. We find relationships between BOLD response magnitude and cortical thickness, curvature, depth and macrovasculature. These relationships are consistently found across subjects and datasets and suggest that variation in BOLD response magnitudes across cortical locations reflects, in part, differences in anatomy and vascularization. To compensate for these factors, we implement a regression-based correction method and show that after correction, BOLD responses become more homogeneous across V1. The correction reduces the horizontal/vertical difference by about half, indicating that some of the difference is likely not due to neural activity differences. We conclude that interpretation of variation in BOLD response magnitude across cortical locations should consider the influence of the potential confounding factors of thickness, curvature, depth and vascularization. Significance statementThe magnitude of the BOLD signal is often used as a surrogate of neural activity, but the exact factors that contribute to its strength have not been studied on a voxel-wise level. Here, we examined several anatomical and measurement-related factors to assess their relationship with BOLD signal magnitude. We find that BOLD magnitude correlates with cortical anatomy, depth and macrovasculature. To remove the contribution of these factors, we propose a simple, data-driven correction method that can be used in any functional magnetic resonance imaging (fMRI) experiment. After accounting for the confounding factors, BOLD magnitude becomes more spatially homogenous. Our correction method improves the ability to make more accurate inferences about local neural activity from fMRI data.

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