The ventral temporal cortex (VTC) in the brain supports our remarkable ability to recognize objects rapidly and accurately from the visual input in everyday life. Identity information is extracted from visual input through multiple stages of representation. To fully understand the neural mechanism of object processing, it is critical to know how these representations are physically applied to anatomical neural structure in the VTC. Numerous studies have already revealed multiple levels of feature representation manifest at different scales of anatomical organizations which superimposed in the VTC. The superordinate category representations (e.g. animate/inanimate, real-world size) manifest at large scale organization covering the whole VTC. Meanwhile, the category-selective representations (e.g. face, body, and scene selective regions in the mid-fusiform gyrus) are revealed at finer spatial scale in the VTC (Hasson et al., 2003; Spiridon et al., 2006). Recent evidence suggested a general spatial organization of neural responses to dimensions in object feature space in monkey inferotemporal cortex (Bao et al., 2020). Could such physical organization be further extended to even smaller scale, like object parts/features representations within each category-selective region? In other words, as part representations play a critical role in object processing, would there be consistent spatial tuning across individuals for different object parts within each category-selective region in VTC?

Fine-scale spatial organizations of low-level visual features have already been found in early visual cortex, such as ocular dominance columns and orientation pinwheels (Blasdel and Salama, 1986; Bonhoeffer and Grinvald, 1991; Hubel et al., 1977; Weliky et al., 1996). Among all the object-selective regions in the VTC, the face-selective regions, including FFA and OFA, are one of the most widely examined object-processing networks in the past decades in cognitive neuroscience. As faces have spatially separated yet organized features such as eyes and mouth which are easy to be defined, it is suitable to use face parts to examine whether there are spatial tunings for different object features in the VTC. Neurophysiology studies in non-human primates demonstrated face-selective neurons in face-selective regions showed different sensitivities to various of face feature or combination of dimensions in face feature space (Chang and Tsao, 2017; Freiwald et al., 2009). Human fMRI studies also found the neural response patterns in FFA or OFA could distinguish different face parts (Zhang et al., 2015), suggesting voxels within same face-selective region may have different face feature tuning. In addition, previous study also suggests that the spatial distribution of a face feature may be relevant to the physical location of that feature in a face (Henriksson et al., 2015).

The sizes of the face-selective regions in VTC are relatively small, spanning about 1 cm. To investigate the potential spatial tuning within each face region, high-resolution fMRI with sufficient sensitivity and spatial precision is necessary. With high-field fMRI, fine-scale patterns have been observed in early visual cortex, such as columnar-like structures in V1, V2, V3, V3a, and hMT (Cheng et al., 2001; Goncalves et al., 2015; Nasr et al., 2016; Schneider et al., 2019; Yacoub et al., 2008; Zimmermann et al., 2011). These findings validate the feasibility of using high-field fMRI to reveal fine-scale (several mm) structures in the visual cortex.

Here, we used 7T fMRI to examine whether category-specific feature information, such as object parts, would be represented in certain spatial pattern within object selective regions. With faces as stimuli, the high-field fMRI allowed for measuring detailed neural response patterns from multiple face-selective regions. Our results show that in the right pFFA and right OFA, different face parts elicited differential spatial patterns of fMRI responses. Specifically, eyes induced responses biased to the posterior portion of the ROIs while responses to mouth and chin were biased to the anterior portion of the ROIs. Similar spatial tuning was observed in both the pFFA and OFA, and the patterns are highly consistent across participants. Together, these results reveal robust fine-scale spatial tuning of face features within face-selective regions.

One critical challenge to demonstrate the spatial tuning within single face-selective region is to find the anatomical landmark to align the function maps between different individuals, as the shape, size, and spatial location of FFA vary largely across individuals. Among all the anatomical structures in the VTC, the mid-fusiform sulcus (MFS) could potentially serve as landmark in the current study. MFS is relatively small structure in the VTC, but consistently present in most individuals (Weiner et al., 2014). On the one hand, the structure of MFS could predict the coordinates of face-selective region around mid-fusiform, especially the anterior one (Weiner et al., 2014). On the other hand, MFS is found to be highly consistent with many anatomical lateral-medial transitions in the VTC, such as cytoarchitecture and white-matter connectivity transitions (Weiner et al., 2014; Caspers et al., 2013; Grill-Spector and Weiner, 2014; Lorenz et al., 2017). In addition, it could also predict the transitions in many function organization, such as animacy/inanimacy and face/scene preference (Grill-Spector and Weiner, 2014). Considering its anatomical and functional significance, in the current study, we used the direction of MFS to align the potential spatial tuning of face part across individuals.

Different face parts (i.e., eyes, nose, mouth, hair, and chin, see Figure 1A) and whole faces were presented to participants and they performed a one-back task in 7T MRI scanner. For each participant, five face-selective ROIs (i.e. right pFFA, right aFFA, right OFA, left FFA, and left OFA) were defined with independent localizer scans. Before comparing the spatial response patterns between the face parts, we assessed the overall neural response amplitudes they generated in each ROIs. All face selective regions showed a similar trend that eyes generated higher responses than nose, hair, and chin (ts > 2.61, ps <0.05; except for eyes vs. nose in the left FFA and for eyes vs. chin in left OFA, ts < 2.40, ps >0.06. See Figure 1B). However, mouth generated similar response amplitudes as eyes (ts < 1.58, ps >0.17).

Figure 1

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Considering that eyes and mouth are two dominant features in face perception (Schyns et al., 2002; Wegrzyn et al., 2017), and their response amplitudes were similar in face-selective regions, in the initial step, we compared the spatial patterns of neural responses to eyes with that to mouth within each ROI. Each pattern was first normalized to remove any overall amplitude difference between conditions. Then we directly contrasted the two patterns and projected the difference onto the inflated brain surface. A spatial pattern was observed in the right pFFA consistently across all participants (Figure 2). In the dimension parallel to the mid-fusiform gyrus, the posterior portion of the right pFFA was biased to respond more to eyes, whereas the anterior portion was biased to respond more to mouth. Note that in participant S2, the direction of MFS was more lateral-medial near the position of the right pFFA, and interestingly, the eyes-mouth contrast map was oriented in the same direction, even though S2’s map may initially appear oriented differently from that of other participants. It suggests the anatomical orientation of MFS is highly correlated with such spatial tuning of face parts. To estimate the reliability of such spatial tuning, we split the eight runs data from each participant in the main experiment into two data sets (odd-runs and even-runs), and estimated the eyes-mouth biases within each data set. Then we calculated the correlation coefficient between such biases across different voxels between the two data sets to estimate the reliability of the results in the right pFFA. The results demonstrate strong reliability of the data within participants (Figure 2—figure supplement 1).

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To further demonstrate such relationship, and also to provide a quantitative description of the spatial tuning of face parts within right pFFA, the fMRI responses to different face parts were projected onto the brain surface of each individual participant. Then we grouped vertices based on their location along the direction parallel to the MFS, and averaged the fMRI responses at each location to generate the response profile on this posterior-anterior dimension (Figure 3A, see details in Materials and methods). The group-averaged results clearly showed that the difference between eyes and mouth signals consistently changed along the posterior-anterior direction in the right pFFA (Figure 3B). To quantify this trend, we further calculated the correlation coefficient between the eyes-mouth neural response differences and the position index along the posterior-anterior dimension (i.e. more posterior location was assigned with smaller value) in each participant. The group result revealed a significant negative correlation (t(5)=8.36, p = 0.0004, Cohen’s d = 3.41), confirming the consistency across participants that the posterior part of right pFFA was biased to eyes and anterior part was biased to mouth.

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The contrast map highlighted the differences between eyes and mouth responses. However, the original response patterns elicited by eyes and mouth share the same underlying general ‘face-related’ pattern, which was subtracted out when contrasting the two response patterns. To extract the response profile of individual face parts, we used independently obtained response patterns of whole faces as the general face-related pattern and regressed it out from the eyes and mouth response patterns. The fMRI responses could be influenced by multiple factors other than neural responses, such as the distribution of the vein, which means there is a shared factor driving the raw fMRI response patterns of different conditions. Thus, to eliminate such shared pattern from the patterns of different face parts, we regressed out the spatial patterns of the whole faces from patterns of each face part. With the general pattern regressed out, we observed distinct spatial profiles elicited by eyes and mouth in the right pFFA (Figure 3D top panel). The eye-biased voxels were more posterior than that of mouth-biased voxels, which is consistent with the contrast map shown in Figure 2.

Removing the general pattern helped to reveal the pattern of voxel biases for individual face parts. While removing the face-related general pattern achieved this goal, it is possible that removing the general face-related pattern distorted the parts generated response patterns since they share high-level visual information (i.e. face and eyes stimuli are both face-related). Therefore, it is important to check whether the parts specific patterns could be seen with removal of a common face-independent signal distribution. In five of the six participants, data were also obtained when they viewed everyday objects. Indeed, non-face objects generated significantly lower but spatially similar patterns of activation compared with faces across the right pFFA (Figure 3C). This result suggests that there is a general intrinsic BOLD sensitivity profile in the pFFA regardless of the stimuli. Indeed, both face and non-face object patterns explained large part of the variation of the face part patterns (for faces average R² = 0.86, for objects average R² = 0.72). Thus we proceeded to use the response patterns of either faces or non-face everyday objects to regress out the intrinsic baseline profile from eyes and mouth response patterns, and plotted face part specific patterns along the posterior-anterior dimension. Consistently, results with object patterns removed showed clear posterior bias for eyes and anterior bias for mouth in the right pFFA (Figure 3D bottom panel).

To control for the potential contribution from retinotopic bias of the different face part conditions, in our experiment, all stimuli were presented at the fixation with a 1.3° horizontal jitter either to the left or to the right alternatively in different trials within a block. Even though the stimuli were centered on the fixation, because of the nature of the face parts (e.g. two eyes are apart, chin depicts the outline of the face), there were still small degrees (less than 3°) of retinotopic differences between the eyes and mouth conditions. To further rule out the retinotopic contribution, as well as to replicate our finding, we did two control experiments. In the first control experiment (Control Experiment 1), data were obtained with a single eye or mouth presented at either the near central (1.3°) or near peripheral (3.1°) location during the scan (see Figure 3—figure supplement 1A). This 2 × 2 (face parts x location) design allowed us to contrasted fMRI response patterns between face parts (single eye vs. mouth) regardless the stimulus location, or between locations (near central vs. near peripheral) regardless the face parts presented. Data from six participants were collected in the Control Experiment 1 and two of them (S1 and S5) also participated main experiment. In all participants, the eye vs. mouth contrast revealed spatial patterns in the right pFFA very similar to that in the main experiment (Figure 3—figure supplement 1B). However, contrasting fMRI responses between the near central and near peripheral location regardless the face parts failed to reveal consistent patterns across participants (Figure 3—figure supplement 1C). These results further support that the different fMRI response patterns we observed in the right pFFA were contributed by face feature differences rather than retinotopic bias. In the second control experiment (Control Experiment 2), we used top and bottom parts of the face as stimuli and counterbalance the stimulus location to verify the spatial tuning in the right pFFA. With a 2 × 2 design (eyes vs. nose & mouth x present above vs. below fixation) (Figure 3—figure supplement 2A), consistent anterior-posterior spatial patterns in the right pFFA were observed in eight participants (Figure 3—figure supplement 2B), which further corroborated our main finding of spatially organized representation of face parts in the right pFFA.

In addition to the two control experiments, we also measured the population receptive field (pRF) of each voxel in the right pFFA in three participants from the main experiment (Figure 3—figure supplement 3A) following established procedures (Dumoulin and Wandell, 2008; Kay et al., 2013; Kriegeskorte et al., 2008). For each voxel, parameter x and y were calculated along with other parameters to represent the receptive field center on the horizontal (x) and vertical (y) axis in the visual field. Although generally more voxels in the right pFFA were bias to left visual field, which is consistent with previous report (Kay et al., 2015; Nichols et al., 2016), we observed no consistent spatial pattern in either x or y map of the right pFFA across participants (Figure 3—figure supplement 3B).

To examine the spatial patterns of response from eyes and mouth in other face-selectivity regions, similar analyses as in pFFA were applied to the fMRI response patterns in the right OFA, right aFFA, left FFA, and left OFA. For the left and right OFA, the posterior-anterior dimension was defined as the direction parallel to the occipitotemporal sulcus (OTS), where the OFAs were located in most participants. Among these regions, the right OFA also had distinct response patterns for eyes and mouth along the posterior-anterior dimension (Figure 4), similar to what we observed in the right pFFA. Group negative correlation was observed between the eyes-mouth differences and the posterior-anterior location of the right OFA (t(5)=3.63, p = 0.015, Cohen’s d = 1.48). Such pattern was also observed in the Control Experiments. We also observed similar spatial patterns between eyes-mouth bias and visual field bias in vertical direction (Figure 4—figure supplement 1), which is consistent with previous findings in inferior occipital gyrus (de Haas et al., 2021). While the right OFA and right pFFA have been considered as sensitive to facial components and whole faces respectively, in our data they showed similar spatial profiles of eyes and mouth responses along the posterior-anterior dimension. This is consistent with, but adds some constraints to, the idea that the right pFFA may receive face feature information from right OFA for further processing (Liu et al., 2010; Zhu et al., 2011). In other face-selective regions, no consistent pattern was observed, as the correlations between the eyes-mouth difference and posterior-anterior location were not significant (ts <1.09, ps >0.32, see Figure 4A).

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Beside the anterior-posterior dimension, the spatial representation of parts could organize in other spatial dimensions, such as the lateral-medial dimension in the VTC, or even in more complex nonlinear patterns. However, since the right pFFA located within the sulcus (MFS) in most of our participants, such that voxels distant from each other on the surface along the lateral-medial dimension could be spatially adjacent in the volume space, making it difficult to accurately reconstruct the spatial pattern along the lateral-medial dimension within the sulcus. Nevertheless, the finding of anterior-posterior bias of face parts is sufficient to demonstrate the existence of fine-scale feature map within object-selective regions.

Our stimuli also included nose, hair, and chin images, thus gave us a chance to examine their spatial profiles in each face-selective ROI as we did for eyes and mouth, though their neural responses were generally lower than that from eyes and mouth. Chin and mouth elicited similar response patterns along the anterior-posterior dimension in the right pFFA and right OFA after regressing out general spatial patterns (Figure 5A). By directly contrasting fMRI response patterns between eyes and chin, similar spatial profiles were revealed in the right pFFA and right OFA that the posterior part was biased to eyes and anterior part was biased to chin (ts >5.30, ps <0.01, see Figure 5B). We also observed a similar though less obvious profile in the left FFA (t(5)=2.68, p = 0.04, Cohen’s d = 1.09), but not in the right aFFA or left OFA (ts <0.41, ps >0.71).

Figure 5

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Our results reveal that within certain face-selective regions in the human occipito-temporal cortex, the neural representations of different facial features have consistent spatial profiles. Such fine-scale spatial tuning is found similarly in the right pFFA and right OFA, but not in the more anterior right aFFA nor in the left hemisphere’s face-selective regions. In other words, fine-scale spatial tuning for face parts exists at the early to intermediate stages of face processing hierarchy in the right hemisphere.

In the current study, five face parts (i.e. eyes, nose, mouth, hair, and chin) were tested, with eyes and mouth showed most distinct spatial profiles in the right pFFA and right OFA. No obvious spatial pattern was observed for nose and hair in face-selective regions, but it would be premature to conclude that there is no fine-scale spatial profile for their neural representations. For one, the nose and hair stimuli elicited lower fMRI responses compared with eyes and mouth stimuli, making it more difficult to detect potential spatial patterns. The observation that eyes and mouth elicited most differential patterns is consistent with them providing more information about faces than other features in face processing (Schyns et al., 2002; Wegrzyn et al., 2017). The dominance of eyes and mouth in face-selective regions could be considered as a form of cortical magnification of more informative features, a common principle of functional organization in sensory cortex (Cowey and Rolls, 1974; Daniel and Whitteridge, 1961; Penfield and Boldrey, 1937).

The discovery that some face parts are represented within the face-processing regions with fine-scale spatial tuning improve our understanding about how functional representations are physically applied to anatomical structures in the VTC. To further explore the neural models about object processing in the VTC, it is important to ask what kinds of constrain, functional or anatomical, result in such fine-scale spatial tuning? Many of the visual cortical areas have retinotopic maps, indeed having a retinotopic representation of the visual world is one of the key ways to define a visual cortical area. Along occipitotemporal processing stream, visual areas increasingly become more specialized in processing certain features and object categories. What is the relationship between a potential spatial organization of face part representations and the spatial relationship of face parts in a real face?

A recent study has revealed that in the inferior occipital gyrus, where the OFA located, both tunings for retinotopic location and face parts (de Haas et al., 2021). Although the tuning peak maps were idiosyncratic across individuals, the two tuning maps were correlated within individuals, suggesting a relationship between face parts configuration and their typical retinotopic configuration. Our findings provide additional support for face part turning in the OFA, and further reveal that there exists a consistent spatial profile of face part tuning across individuals. More importantly, our finding of spatial tuning of face part in the pFFA indicates that although the organization of feature tuning could be constrained by the retinotopic tuning in occipital cortex, a more abstract feature tuning could still be spatially organized in cortical areas with absent or minimal retinotopic property in the later stages of VTC.

Another previous study also tested the idea of ‘faciotopy’, that there are cortical patches representing different face features within a face-selective region and the spatial organization of these feature patches on the cortical surface would reflect the physical relationships of face features (Henriksson et al., 2015). Their results showed that in the OFA and FFA, the differences between neural response patterns of face parts were correlated with physical distances between face parts in a face. Our results support the existence of stable spatial profile of face features in the right OFA and right pFFA, especially for eyes and mouth. The possible mechanism underlying such faciotopy organization is the local-to-global computation, that physically adjacent face parts interact more than parts far apart from each other during the early stages of processing, thus it is more efficient for them to have neural representations near each other. However, in the current study, we did not find the posterior bias pattern for hair as we did for eyes, even though hair and eyes are spatially adjacent, which could be caused by the hair being generally less invariant and less informative in the face identification.

Another potential explanation could be that while both contribute to face identification, eyes and mouth are differentially involved in different neural networks and have distinct functional connectivity profiles with other brain regions. Specifically, the mouth region provides more information for language processing and audio-visual communication perception, thus it may be more connected to the language processing system. Meanwhile, the eyes are more important in face detection and eye gaze signifies interest, thus it may be more connected to the attention system. Previous studies have already found the connectivity profiles could predict the functional selectivity in the VTC, thus it would be interesting to examine whether the face part spatial tuning in the pFFA could be predicted using functional or anatomical connectivity to the other brain regions in the future studies.

The third explanation of our results is that the fine-scale pattern of face part sensitivity is driven by larger-scale organization of object-selective regions in the ventral pathway. As the FFA is overlapped with body-selective region fusiform-body area (FBA), it is possible that some face features (e.g. mouth) could be represented closer to the FBA, while face parts such as the eyes are more represented in the FFA proper. However, existing evidence does not support a consistent anterior-posterior relationship between FFA and FBA (Kim et al., 2014). It remains important to directly compare the eyes-mouth pattern against the face-body pattern with high-resolution fMRI in future studies.

The spatial clustering of neurons with similar tuning is one of the organization principles in the brain. Such clustering may optimize the efficiency of the neural computation by reducing the total wire length (Laughlin and Sejnowski, 2003), thus the clustering of neurons with similar feature tuning within face-selective regions could improve the processing efficiency to face stimuli. Our results provide evidence from neural imaging data to support the voxel level neuronal clustering driven by the tuning to different face parts. Previous fMRI and single unit recording studies in monkey face processing network have demonstrated strong correspondence between fMRI signal and neuronal responses (Tsao et al., 2006), suggesting that the face part tuning in our results may be driven by neuronal response biases. In addition to neuronal response biases, the clustering could also reflect activity synchronization across neurons. Further neurophysiology studies are needed to delineate the specific mechanisms to the spatial clustering observed within face-selective regions.

Among five face-selective regions we examined, only the right pFFA and right OFA exhibited distinct fMRI response patterns for eyes and mouth. In the face processing network, face parts are believed to be represented in the posterior regions such as the OFA (Liu et al., 2010; Arcurio et al., 2012; Pitcher et al., 2007). Part information is transmitted to anterior regions to be further integrated to form holistic face representations (Zhang et al., 2015; Rotshtein et al., 2005). In that sense, the more anterior regions in the face processing network are more responsible for representing integrated face information such as gender or identity rather than individual face parts (Freiwald and Tsao, 2010; Landi and Freiwald, 2017). Consistent with this idea, at the right aFFA, there is no obvious spatial tuning of face parts. Meanwhile, a clear hemispheric difference was found in our results that the distinct spatial response patterns for face parts were observed in the right but not the left hemisphere, which is consistent with previous findings that compared with left FFA, right FFA is more sensitive to face specific features (Meng et al., 2012) and configural information (Rossion et al., 2000). The neural clustering of face part tuning and consistent spatial patterns across individuals in the right rather than in the left face selective regions provides a potential computational advantage for right lateralization for face processing. The clustering of neurons with similar feature tuning have been found extensively in the ventral pathway, which may help to support a more efficient neural processing. Therefore, one of the neural mechanisms underlying the functional lateralization of face processing could be the existence of spatial clustering of face part tunings in the right hemisphere.

Much progress has been made in our understanding of object feature representation in the VTC during the past decade, especially with the view of feature space representation (Bao et al., 2020; Chang and Tsao, 2017). Consequently, we now believe that a large number of features are represented for object recognition, but how does our brain physically organize such complex feature representations in the VTC? One possible solution is that these feature representations manifest in different spatial scales. For more general features the representation manifests at large spatial scale across the whole VTC (e.g. large/small, animate/inanimate), and for more specific features such as face parts, it manifests at finer spatial scales within specific object processing regions. Under this view, we would expect more fine-scale feature organizations to be revealed with more advanced neural imaging tools, which are critical for fully understanding the neural algorithm of object processing in the VTC.

fMRI response data have been deposited in Dryad.

1. 1. Zhang J
   2. Jiang Y
   3. Song Y
   4. Zhang P
   5. He S

   (2021) Dryad Digital Repository

   Dataset for Spatial tuning of face part representations within face-selective areas revealed by high-field fMRI.

   https://doi.org/10.5061/dryad.gmsbcc2nh

1. 1. Arcurio LR
   2. Gold JM
   3. James TW

   (2012) The response of face-selective cortex with single face parts and part combinations

   Neuropsychologia 50:2454–2459.

   https://doi.org/10.1016/j.neuropsychologia.2012.06.016

   • PubMed
   • Google Scholar
2. 1. Bao P
   2. She L
   3. McGill M
   4. Tsao DY

   (2020) A map of object space in primate inferotemporal cortex

   Nature 583:103–108.

   https://doi.org/10.1038/s41586-020-2350-5

   • PubMed
   • Google Scholar
3. 1. Blasdel GG
   2. Salama G

   (1986) Voltage-sensitive dyes reveal a modular organization in monkey striate cortex

   Nature 321:579–585.

   https://doi.org/10.1038/321579a0

   • PubMed
   • Google Scholar
4. 1. Bonhoeffer T
   2. Grinvald A

   (1991) Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns

   Nature 353:429–431.

   https://doi.org/10.1038/353429a0

   • PubMed
   • Google Scholar
5. 1. Caspers J
   2. Zilles K
   3. Eickhoff SB
   4. Schleicher A
   5. Mohlberg H
   6. Amunts K

   (2013) Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus

   Brain Structure & Function 218:511–526.

   https://doi.org/10.1007/s00429-012-0411-8

   • PubMed
   • Google Scholar
6. 1. Chang L
   2. Tsao DY

   (2017) The Code for Facial Identity in the Primate Brain

   Cell 169:1013–1028.

   https://doi.org/10.1016/j.cell.2017.05.011

   • PubMed
   • Google Scholar
7. 1. Cheng K
   2. Waggoner RA
   3. Tanaka K

   (2001) Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging

   Neuron 32:359–374.

   https://doi.org/10.1016/s0896-6273(01)00477-9

   • PubMed
   • Google Scholar
8. 1. Cowey A
   2. Rolls ET

   (1974) Human cortical magnification factor and its relation to visual acuity

   Experimental Brain Research 21:447–454.

   https://doi.org/10.1007/BF00237163

   • PubMed
   • Google Scholar
9. 1. Daniel PM
   2. Whitteridge D

   (1961) The representation of the visual field on the cerebral cortex in monkeys

   The Journal of Physiology 159:203–221.

   https://doi.org/10.1113/jphysiol.1961.sp006803

   • PubMed
   • Google Scholar
10. 1. de Haas B
    2. Sereno MI
    3. Schwarzkopf DS

    (2021) Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity

    The Journal of Neuroscience 41:5511–5521.

    https://doi.org/10.1523/JNEUROSCI.2415-20.2021

    • PubMed
    • Google Scholar
11. 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

    • PubMed
    • Google Scholar
12. 1. Freiwald WA
    2. Tsao DY
    3. Livingstone MS

    (2009) A face feature space in the macaque temporal lobe

    Nature Neuroscience 12:1187–1196.

    https://doi.org/10.1038/nn.2363

    • PubMed
    • Google Scholar
13. 1. Freiwald WA
    2. Tsao DY

    (2010) Functional compartmentalization and viewpoint generalization within the macaque face-processing system

    Science 330:845–851.

    https://doi.org/10.1126/science.1194908

    • PubMed
    • Google Scholar
14. 1. Goncalves NR
    2. Ban H
    3. Sánchez-Panchuelo RM
    4. Francis ST
    5. Schluppeck D
    6. Welchman AE

    (2015) 7 tesla FMRI reveals systematic functional organization for binocular disparity in dorsal visual cortex

    The Journal of Neuroscience 35:3056–3072.

    https://doi.org/10.1523/JNEUROSCI.3047-14.2015

    • PubMed
    • Google Scholar
15. 1. Grill-Spector K
    2. Weiner KS

    (2014) The functional architecture of the ventral temporal cortex and its role in categorization

    Nature Reviews. Neuroscience 15:536–548.

    https://doi.org/10.1038/nrn3747

    • PubMed
    • Google Scholar
16. 1. Hasson U
    2. Harel M
    3. Levy I
    4. Malach R

    (2003) Large-scale mirror-symmetry organization of human occipito-temporal object areas

    Neuron 37:1027–1041.

    https://doi.org/10.1016/s0896-6273(03)00144-2

    • PubMed
    • Google Scholar
17. 1. Henriksson L
    2. Mur M
    3. Kriegeskorte N

    (2015) Faciotopy-A face-feature map with face-like topology in the human occipital face area

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 72:156–167.

    https://doi.org/10.1016/j.cortex.2015.06.030

    • PubMed
    • Google Scholar
18. 1. Hubel DH
    2. Wiesel TN
    3. lecture F

    (1977) Ferrier lecture. Functional architecture of macaque monkey visual cortex

    Proceedings of the Royal Society of London. Series B, Biological Sciences 198:1–59.

    https://doi.org/10.1098/rspb.1977.0085

    • PubMed
    • Google Scholar
19. 1. Kay KN
    2. Winawer J
    3. Mezer A
    4. Wandell BA

    (2013) Compressive spatial summation in human visual cortex

    Journal of Neurophysiology 110:481–494.

    https://doi.org/10.1152/jn.00105.2013

    • PubMed
    • Google Scholar
20. 1. Kay KN
    2. Weiner KS
    3. Grill-Spector K

    (2015) Attention reduces spatial uncertainty in human ventral temporal cortex

    Current Biology 25:595–600.

    https://doi.org/10.1016/j.cub.2014.12.050

    • PubMed
    • Google Scholar
21. 1. Kim NY
    2. Lee SM
    3. Erlendsdottir MC
    4. McCarthy G

    (2014) Discriminable spatial patterns of activation for faces and bodies in the fusiform gyrus

    Frontiers in Human Neuroscience 8:632.

    https://doi.org/10.3389/fnhum.2014.00632

    • PubMed
    • Google Scholar
22. 1. Kriegeskorte N
    2. Mur M
    3. Ruff DA
    4. Kiani R
    5. Bodurka J
    6. Esteky H
    7. Tanaka K
    8. Bandettini PA

    (2008) Matching categorical object representations in inferior temporal cortex of man and monkey

    Neuron 60:1126–1141.

    https://doi.org/10.1016/j.neuron.2008.10.043

    • PubMed
    • Google Scholar
23. 1. Landi SM
    2. Freiwald WA

    (2017) Two areas for familiar face recognition in the primate brain

    Science 357:591–595.

    https://doi.org/10.1126/science.aan1139

    • PubMed
    • Google Scholar
24. 1. Laughlin SB
    2. Sejnowski TJ

    (2003) Communication in neuronal networks

    Science 301:1870–1874.

    https://doi.org/10.1126/science.1089662

    • PubMed
    • Google Scholar
25. 1. Liu J
    2. Harris A
    3. Kanwisher N

    (2010) Perception of face parts and face configurations: an FMRI study

    Journal of Cognitive Neuroscience 22:203–211.

    https://doi.org/10.1162/jocn.2009.21203

    • PubMed
    • Google Scholar
26. 1. Lorenz S
    2. Weiner KS
    3. Caspers J
    4. Mohlberg H
    5. Schleicher A
    6. Bludau S
    7. Eickhoff SB
    8. Grill-Spector K
    9. Zilles K
    10. Amunts K

    (2017) Two New Cytoarchitectonic Areas on the Human Mid-Fusiform Gyrus

    Cerebral Cortex 27:373–385.

    https://doi.org/10.1093/cercor/bhv225

    • PubMed
    • Google Scholar
27. 1. Meng M
    2. Cherian T
    3. Singal G
    4. Sinha P

    (2012) Lateralization of face processing in the human brain

    Proceedings. Biological Sciences 279:2052–2061.

    https://doi.org/10.1098/rspb.2011.1784

    • PubMed
    • Google Scholar
28. 1. Morgan PS
    2. Bowtell RW
    3. McIntyre DJO
    4. Worthington BS

    (2004) Correction of spatial distortion in EPI due to inhomogeneous static magnetic fields using the reversed gradient method

    Journal of Magnetic Resonance Imaging 19:499–507.

    https://doi.org/10.1002/jmri.20032

    • PubMed
    • Google Scholar
29. 1. Nasr S
    2. Polimeni JR
    3. Tootell RBH

    (2016) Interdigitated Color- and Disparity-Selective Columns within Human Visual Cortical Areas V2 and V3

    The Journal of Neuroscience 36:1841–1857.

    https://doi.org/10.1523/JNEUROSCI.3518-15.2016

    • PubMed
    • Google Scholar
30. 1. Nichols DF
    2. Betts LR
    3. Wilson HR

    (2016) Position selectivity in face-sensitive visual cortex to facial and nonfacial stimuli: an fMRI study

    Brain and Behavior 6:e00542.

    https://doi.org/10.1002/brb3.542

    • PubMed
    • Google Scholar
31. 1. Penfield W
    2. Boldrey E

    (1937) SOMATIC MOTOR AND SENSORY REPRESENTATION IN THE CEREBRAL CORTEX OF MAN AS STUDIED BY ELECTRICAL STIMULATION

    Brain : A Journal of Neurology 60:389–443.

    https://doi.org/10.1093/brain/60.4.389

    • Google Scholar
32. 1. Pitcher D
    2. Walsh V
    3. Yovel G
    4. Duchaine B

    (2007) TMS evidence for the involvement of the right occipital face area in early face processing

    Current Biology 17:1568–1573.

    https://doi.org/10.1016/j.cub.2007.07.063

    • PubMed
    • Google Scholar
33. 1. Pitcher D
    2. Dilks DD
    3. Saxe RR
    4. Triantafyllou C
    5. Kanwisher N

    (2011) Differential selectivity for dynamic versus static information in face-selective cortical regions

    NeuroImage 56:2356–2363.

    https://doi.org/10.1016/j.neuroimage.2011.03.067

    • PubMed
    • Google Scholar
34. 1. Rossion B
    2. Dricot L
    3. Devolder A
    4. Bodart JM
    5. Crommelinck M
    6. De Gelder B
    7. Zoontjes R

    (2000) Hemispheric asymmetries for whole-based and part-based face processing in the human fusiform gyrus

    Journal of Cognitive Neuroscience 12:793–802.

    https://doi.org/10.1162/089892900562606

    • PubMed
    • Google Scholar
35. 1. Rotshtein P
    2. Henson RNA
    3. Treves A
    4. Driver J
    5. Dolan RJ

    (2005) Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain

    Nature Neuroscience 8:107–113.

    https://doi.org/10.1038/nn1370

    • PubMed
    • Google Scholar
36. 1. Schneider M
    2. Kemper VG
    3. Emmerling TC
    4. De Martino F
    5. Goebel R

    (2019) Columnar clusters in the human motion complex reflect consciously perceived motion axis

    PNAS 116:5096–5101.

    https://doi.org/10.1073/pnas.1814504116

    • PubMed
    • Google Scholar
37. 1. Schyns PG
    2. Bonnar L
    3. Gosselin F

    (2002) Show me the features! Understanding recognition from the use of visual information

    Psychological Science 13:402–409.

    https://doi.org/10.1111/1467-9280.00472

    • PubMed
    • Google Scholar
38. 1. Spiridon M
    2. Fischl B
    3. Kanwisher N

    (2006) Location and spatial profile of category-specific regions in human extrastriate cortex

    Human Brain Mapping 27:77–89.

    https://doi.org/10.1002/hbm.20169

    • PubMed
    • Google Scholar
39. 1. Teeuwisse WM
    2. Brink WM
    3. Webb AG

    (2012) Quantitative assessment of the effects of high-permittivity pads in 7 Tesla MRI of the brain

    Magnetic Resonance in Medicine 67:1285–1293.

    https://doi.org/10.1002/mrm.23108

    • PubMed
    • Google Scholar
40. 1. Tsao DY
    2. Freiwald WA
    3. Tootell RBH
    4. Livingstone MS

    (2006) A cortical region consisting entirely of face-selective cells

    Science 311:670–674.

    https://doi.org/10.1126/science.1119983

    • PubMed
    • Google Scholar
41. 1. Van de Moortele P-F
    2. Auerbach EJ
    3. Olman C
    4. Yacoub E
    5. Uğurbil K
    6. Moeller S

    (2009) T1 weighted brain images at 7 Tesla unbiased for Proton Density, T2* contrast and RF coil receive B1 sensitivity with simultaneous vessel visualization

    NeuroImage 46:432–446.

    https://doi.org/10.1016/j.neuroimage.2009.02.009

    • PubMed
    • Google Scholar
42. 1. Wegrzyn M
    2. Vogt M
    3. Kireclioglu B
    4. Schneider J
    5. Kissler J

    (2017) Mapping the emotional face. How individual face parts contribute to successful emotion recognition

    PLOS ONE 12:e0177239.

    https://doi.org/10.1371/journal.pone.0177239

    • PubMed
    • Google Scholar
43. 1. Weiner KS
    2. Golarai G
    3. Caspers J
    4. Chuapoco MR
    5. Mohlberg H
    6. Zilles K
    7. Amunts K
    8. Grill-Spector K

    (2014) The mid-fusiform sulcus: a landmark identifying both cytoarchitectonic and functional divisions of human ventral temporal cortex

    NeuroImage 84:453–465.

    https://doi.org/10.1016/j.neuroimage.2013.08.068

    • PubMed
    • Google Scholar
44. 1. Weliky M
    2. Bosking WH
    3. Fitzpatrick D

    (1996) A systematic map of direction preference in primary visual cortex

    Nature 379:725–728.

    https://doi.org/10.1038/379725a0

    • PubMed
    • Google Scholar
45. 1. Yacoub E
    2. Harel N
    3. Ugurbil K

    (2008) High-field fMRI unveils orientation columns in humans

    PNAS 105:10607–10612.

    https://doi.org/10.1073/pnas.0804110105

    • PubMed
    • Google Scholar
46. 1. Zhang J
    2. Liu J
    3. Xu Y

    (2015) Neural decoding reveals impaired face configural processing in the right fusiform face area of individuals with developmental prosopagnosia

    The Journal of Neuroscience 35:1539–1548.

    https://doi.org/10.1523/JNEUROSCI.2646-14.2015

    • PubMed
    • Google Scholar
47. 1. Zhu Q
    2. Zhang J
    3. Luo YLL
    4. Dilks DD
    5. Liu J

    (2011) Resting-state neural activity across face-selective cortical regions is behaviorally relevant

    The Journal of Neuroscience 31:10323–10330.

    https://doi.org/10.1523/JNEUROSCI.0873-11.2011

    • PubMed
    • Google Scholar
48. 1. Zimmermann J
    2. Goebel R
    3. De Martino F
    4. van de Moortele P-F
    5. Feinberg D
    6. Adriany G
    7. Chaimow D
    8. Shmuel A
    9. Uğurbil K
    10. Yacoub E

    (2011) Mapping the organization of axis of motion selective features in human area MT using high-field fMRI

    PLOS ONE 6:e28716.

    https://doi.org/10.1371/journal.pone.0028716

    • PubMed
    • Google Scholar

Author details

1. Jiedong Zhang

   1. Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

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

   For correspondence

   zhangjiedong@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4432-2752

2. Yong Jiang

   1. Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data curation, Formal analysis, Methodology, Validation, Visualization

   Competing interests

   No competing interests declared

3. Yunjie Song

   1. Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data curation, Formal analysis, Validation, Visualization

   Competing interests

   No competing interests declared

4. Peng Zhang

   1. Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data curation, Investigation, Methodology, Validation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Sheng He

   1. Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China
   3. Department of Psychology, University of Minnesota, Minneapolis, United States

   Contribution

   Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   hes@ibp.ac.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5547-923X

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