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Spatial processing by receptive fields is a core property of the visual system. However, it is unknown how spatial processing in high-level regions contributes to recognition behavior. As face inversion is thought to disrupt typical holistic processing of information in faces, we mapped population receptive fields (pRFs) with upright and inverted faces in the human visual system. Here we show that in face-selective regions, but not primary visual cortex, pRFs and overall visual field coverage are smaller and shifted downward in response to face inversion. From these measurements, we successfully predict the relative behavioral detriment of face inversion at different positions in the visual field. This correspondence between neural measurements and behavior demonstrates how spatial processing in face-selective regions may enable holistic perception. These results not only show that spatial processing in high-level visual regions is dynamically used towards recognition, but also suggest a powerful approach for bridging neural computations by receptive fields to behavior. It is unknown whether spatial processing in the ventral (‘what’) stream contributes to high-level visual recognition. Here the authors show that spatial processing in face-selective regions directly contributes to whole face recognition behavior.

Face-processing occurs across ventral and lateral visual streams, which are involved in static and dynamic face perception, respectively. However, the nature of spatial computations across streams is unknown. Using functional MRI and population receptive field (pRF) mapping, we measured pRFs in face-selective regions. Results reveal that spatial computations by pRFs in ventral face-selective regions are concentrated around the center of gaze (fovea), but spatial computations in lateral face-selective regions extend peripherally. Diffusion MRI reveals that these differences are mirrored by a preponderance of white matter connections between ventral face-selective regions and foveal early visual cortex (EVC), while connections with lateral regions are distributed more uniformly across EVC eccentricities. These findings suggest a rethinking of spatial computations in face-selective regions, showing that they vary across ventral and lateral streams, and further propose that spatial computations in high-level regions are scaffolded by the fine-grain pattern of white matter connections from EVC. Humans process faces using face-selective regions in the ventral and lateral streams which perform different tasks. Here, the authors show via functional and diffusion MRI that the spatial computations in face-selective regions vary across streams, constrained by connections from early visual areas.

For over a century, researchers have examined the functional relevancy of white matter bundles. Consequently, many large-scale bundles spanning several centimeters have been associated in their entirety with specific brain functions, such as language or attention. However, these coarse structural–functional relationships are at odds with modern understanding of the fine-grained functional organization of human cortex, such as the mosaic of category-selective regions in ventral temporal cortex. Here, we review a multimodal approach that combines fMRI to define functional regions of interest within individual’s brains with dMRI tractography to identify the white matter bundles of the same individual. Combining these data allows to determine which subsets of streamlines within a white matter bundle connect to specific functional regions in each individual. That is, this approach identifies the functionally defined white matter sub-bundles of the brain. We argue that this approach not only enhances the accuracy of interpreting the functional relevancy of white matter bundles, but also enables segmentation of these large-scale bundles into meaningful functional units, which can then be linked to behavior with enhanced precision. Importantly, this approach has the potential for making new discoveries of the fine-grained functional relevancy of white matter connections in the visual system and the brain more broadly, akin to the flurry of research that has identified functional regions in cortex.

Key Points Understanding information processing in the visual system requires an understanding of the interplay among the system's computational goals and representations, and their physical implementation in the brain. Recent results indicate a consistent topology of functional representations relative to each other and anatomical landmarks in high-level visual cortex. The consistent topology of functional representations reveals that axes of representational spaces are physically implemented as axes in cortical space. Anatomical constraints might determine the topology of functional representations in the brain, which would explain the correspondence between representational and anatomical axes in the ventral temporal cortex (VTC). Superimposition and topology generate predictable spatial convergences and divergences among functional representations, which in turn enable information integration and parallel processing, respectively. Superimposition and topological organization in the VTC generates a series of nested functional representations, the arrangements of which generate a spatial hierarchy of category information. The spatial scale of functional representations may be tied to the level of category abstractness in which more abstract information is represented in larger spatial scales across the VTC. The ventral temporal cortex (VTC) rapidly and flexibly categorizes visual stimuli. In this Review, Grill-Spector and Weiner discuss how the structural features of the VTC support the computations that are necessary to achieve this categorization. Visual categorization is thought to occur in the human ventral temporal cortex (VTC), but how this categorization is achieved is still largely unknown. In this Review, we consider the computations and representations that are necessary for categorization and examine how the microanatomical and macroanatomical layout of the VTC might optimize them to achieve rapid and flexible visual categorization. We propose that efficient categorization is achieved by organizing representations in a nested spatial hierarchy in the VTC. This spatial hierarchy serves as a neural infrastructure for the representational hierarchy of visual information in the VTC and thereby enables flexible access to category information at several levels of abstraction.

How is temporal information processed in human visual cortex? Visual input is relayed to V1 through segregated transient and sustained channels in the retina and lateral geniculate nucleus (LGN). However, there is intense debate as to how sustained and transient temporal channels contribute to visual processing beyond V1. The prevailing view associates transient processing predominately with motion-sensitive regions and sustained processing with ventral stream regions, while the opposing view suggests that both temporal channels contribute to neural processing beyond V1. Using fMRI, we measured cortical responses to time-varying stimuli and then implemented a two temporal channel-encoding model to evaluate the contributions of each channel. Different from the general linear model of fMRI that predicts responses directly from the stimulus, the encoding approach first models neural responses to the stimulus from which fMRI responses are derived. This encoding approach not only predicts cortical responses to time-varying stimuli from milliseconds to seconds but also, reveals differential contributions of temporal channels across visual cortex. Consistent with the prevailing view, motion-sensitive regions and adjacent lateral occipitotemporal regions are dominated by transient responses. However, ventral occipitotemporal regions are driven by both sustained and transient channels, with transient responses exceeding the sustained. These findings propose a rethinking of temporal processing in the ventral stream and suggest that transient processing may contribute to rapid extraction of the content of the visual input. Importantly, our encoding approach has vast implications, because it can be applied with fMRI to decipher neural computations in millisecond resolution in any part of the brain.

•The ILF, AF, and VOF predict the spatial layout of reading-related responses in VTC.•Gray matter microstructure improves the prediction of reading-related responses.•Fascicles and gray matter structure together predict the location of the VWFA. Reading-related responses in the lateral ventral temporal cortex (VTC) show a consistent spatial layout across individuals, which is puzzling, since reading skills are acquired during childhood. Here, we tested the hypothesis that white matter fascicles and gray matter microstructure predict the location of reading-related responses in lateral VTC. We obtained functional (fMRI), diffusion (dMRI), and quantitative (qMRI) magnetic resonance imaging data in 30 adults. fMRI was used to map reading-related responses by contrasting responses in a reading task with those in adding and color tasks; dMRI was used to identify the brain's fascicles and to map their endpoint densities in lateral VTC; qMRI was used to measure proton relaxation time (T1), which depends on cortical tissue microstructure. We fit linear models that predict reading-related responses in lateral VTC from endpoint density and T1 and used leave-one-subject-out cross-validation to assess prediction accuracy. Using a subset of our participants (N=10, feature selection set), we find that i) endpoint densities of the arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), and vertical occipital fasciculus (VOF) are significant predictors of reading-related responses, and ii) cortical T1 of lateral VTC further improves the predictions of the fascicle model. In the remaining participants (N=20, validation set), we show that a linear model that includes T1, AF, ILF and VOF significantly predicts i) the map of reading-related responses across lateral VTC and ii) the location of the visual word form area, a region critical for reading. Overall, our data-driven approach reveals that the AF, ILF, VOF and cortical microstructure have a consistent spatial relationship with an individual's reading-related responses in lateral VTC.

Functional magnetic resonance imaging (fMRI) has identified face- and body part-selective regions, as well as distributed activation patterns for object categories across human ventral temporal cortex (VTC), eliciting a debate regarding functional organization in VTC and neural coding of object categories. Using high-resolution fMRI, we illustrate that face- and limb-selective activations alternate in a series of largely nonoverlapping clusters in lateral VTC along the inferior occipital gyrus (IOG), fusiform gyrus (FG), and occipito-temporal sulcus (OTS). Both general linear model (GLM) and multivoxel pattern (MVP) analyses show that face- and limb-selective activations minimally overlap and that this organization is consistent across experiments and days. We provide a reliable method to separate two face-selective clusters on the middle and posterior FG (mFus and pFus), and another on the IOG using their spatial relation to limb-selective activations and retinotopic areas hV4, VO-1/2, and hMT+. Furthermore, these activations show a gradient of increasing face selectivity and decreasing limb selectivity from the IOG to the mFus. Finally, MVP analyses indicate that there is differential information for faces in lateral VTC (containing weakly- and highly-selective voxels) relative to non-selective voxels in medial VTC. These findings suggest a sparsely-distributed organization where sparseness refers to the presence of several face- and limb-selective clusters in VTC, and distributed refers to the presence of different amounts of information in highly-, weakly-, and non-selective voxels. Consequently, theories of object recognition should consider the functional and spatial constraints of neural coding across a series of minimally overlapping category-selective clusters that are themselves distributed.

Receptive fields (RFs) processing information in restricted parts of the visual field are a key property of visual system neurons. However, how RFs develop in humans is unknown. Using fMRI and population receptive field (pRF) modeling in children and adults, we determine where and how pRFs develop across the ventral visual stream. Here we report that pRF properties in visual field maps, from the first visual area, V1, through the first ventro-occipital area, VO1, are adult-like by age 5. However, pRF properties in face-selective and character-selective regions develop into adulthood, increasing the foveal coverage bias for faces in the right hemisphere and words in the left hemisphere. Eye-tracking indicates that pRF changes are related to changing fixation patterns on words and faces across development. These findings suggest a link between face and word viewing behavior and the differential development of pRFs across visual cortex, potentially due to competition on foveal coverage. Population receptive fields (pRFs) in the visual system are key information-processors, but how they develop is unknown. Here, authors use fMRI and pRF modeling in children and adults to show that in the ventral stream only pRFs in face- and word-selective regions continue to develop, mirroring changes in viewing behavior.

The functional organization of human high-level visual cortex, such as the face- and place-selective regions, is strikingly consistent across individuals. An unanswered question in neuroscience concerns which dimensions of visual information constrain the development and topography of this shared brain organization. To answer this question, we used functional magnetic resonance imaging to scan a unique group of adults who, as children, had extensive visual experience with Pokémon. These animal-like, pixelated characters are dissimilar from other ecological categories, such as faces and places, along critical dimensions (foveal bias, rectilinearity, size, animacy). We show not only that adults who have Pokémon experience demonstrate distinct distributed cortical responses to Pokémon, but also that the experienced retinal eccentricity during childhood can predict the locus of Pokémon responses in adulthood. These data demonstrate that inherent functional representations in the visual cortex—retinal eccentricity—combined with consistent viewing behaviour of particular stimuli during childhood result in a shared functional topography in adulthood. Early childhood experience with the visual stimulus of Pokémon leads to a new brain representation whose location in high-level visual cortex suggests that the way we look at objects as children determines the functional organization of the cortex.

Neurophysiology and optical imaging studies in monkeys and functional magnetic resonance imaging (fMRI) studies in both monkeys and humans have localized clustered neural responses in inferotemporal cortex selective for images of biologically relevant categories, such as faces and limbs. Using higher resolution (1.5 mm voxels) fMRI scanning methods than past studies (3–5 mm voxels), we recently reported a network of multiple face- and limb-selective regions that neighbor one another in human ventral temporal cortex (Weiner and Grill-Spector, Neuroimage, 52(4):1559–1573, 2010 ) and lateral occipitotemporal cortex (Weiner and Grill-Spector, Neuroimage, 56(4):2183–2199, 2011 ). Here, we expand on three basic organization principles of high-level visual cortex revealed by these findings: (1) consistency in the anatomical location of functional regions, (2) preserved spatial relationship among functional regions, and (3) a topographic organization of face- and limb-selective regions in adjacent and alternating clusters. We highlight the implications of this structure in comparing functional brain organization between typical and atypical populations. We conclude with a new model of high-level visual cortex consisting of ventral, lateral, and dorsal components, where multimodal processing related to vision, action, haptics, and language converges in the lateral pathway.