The visual system faces the daunting task of combining highly ambiguous local patterns of contrast into robust, coherent, and spatially extensive complex object representations (Connor et al., 2007; Haxby et al., 1991; Mishkin et al., 1983). Such information is predominantly processed along the ventral visual pathway (areas V1, V2, V4, and inferotemporal cortex [IT]). At early stages of this cortical pathway, neurons are tuned to a local single orientation (Hubel and Livingstone, 1987; Hubel and Wiesel, 1968) or a combination of orientations (Anzai et al., 2007; Ito and Komatsu, 2004). Orientation responses are functionally organized into iso-orientation domains that form pinwheel structures in V1 (Ts'o et al., 1990). At later stages like IT, neurons are selective for complex objects, predominantly organized categorically (Desimone et al., 1984; Freiwald and Tsao, 2010; Fujita et al., 1992; Kobatake and Tanaka, 1994; Tsao et al., 2003; Tsao et al., 2006). Such complex object organization is embodied using combinations of structurally separated feature columns (Fujita et al., 1992; Rajalingham and DiCarlo, 2019; Tanaka, 2003; Tsunoda et al., 2001; Wang et al., 1996). Positioned in-between the local orientation architecture of V1 and the global object architecture of IT lies cortical area V4, exhibiting visual selectivity that demonstrates integration of simple-towards-complex information (Pasupathy et al., 2019; Roe et al., 2012; Yue et al., 2014), and extensive anatomical connectivity across the visual hierarchy (Gattass et al., 1990; Ungerleider et al., 2008).

Functional organization within V4 has previously been visualized by intrinsic signal optical imaging (ISOI), and cortical representations of low-level features for orientation, color, and spatial frequency have been systematically demonstrated (Conway et al., 2007; Li et al., 2014; Li et al., 2013; Lu et al., 2018; Tanigawa et al., 2010). Such functional clustering suggests that the intracortical organizational motifs in V4 bear some similarity to V1. It remains unknown how more complex feature-selective neurons in V4 are spatially organized, and whether feature-like columns found in IT also exist in V4. Because intrinsic imaging is both spatially and temporally limited, it is unable to measure selective responses of single neurons. Using electrophysiology, early studies in V4 using bar and grating stimuli found that V4 neurons are tuned for orientation, size, and spatial frequency (Desimone and Schein, 1987). Subsequent studies revealed V4 selectivity for complex gratings and shapes in natural scenes (David et al., 2006; Gallant et al., 1993; Kobatake and Tanaka, 1994). In particular, Gallant and colleagues discovered V4 neurons with significant preferences for concentric, radial, and hyperbolic gratings (Gallant et al., 1993; Gallant et al., 1996). Neurons with similar preferences were spatially clustered when reconstructing the electrophysiological electrode penetrations (Gallant et al., 1996). These results were extended by later studies confirming the systematic tuning of V4 neurons for shape segments such as curves and corners as well as combination of these segments using parametric stimulus sets consisting of complex shape features (Cadieu et al., 2007; Carlson et al., 2011; Oleskiw et al., 2014; Pasupathy and Connor, 1999; Pasupathy and Connor, 2001; Pasupathy and Connor, 2002). Temporally varying heterogeneous fine-scale tuning within the spatial-temporal receptive field has also been observed (Nandy et al., 2016; Nandy et al., 2013; Yau et al., 2013). More recently, artificial neural networks were used to generate complex stimuli that characterize the selectivity of V4 neurons (Bashivan et al., 2019). However, whether such complex feature-selective neurons are spatially organized in V4 remains poorly understood.

In this study, we aimed to confirm the presence of functional domains in V4 encoding complex features such as curves and corners. We utilized two-photon (2P) calcium imaging in awake macaque V4, which provides visualization of the spatial distribution and clustering within the cortical population alongside substantially enhanced spatial resolution for functional characterization at the single-cell level (Garg et al., 2019; Li et al., 2017; Nauhaus et al., 2012; Ohki et al., 2005; Seidemann et al., 2016; Tang et al., 2018). We scanned a large cortical area in dorsal V4 using a low-power objective lens to search for patches selectively activated by curves or corners. We subsequently imaged these patches using a high-power objective lens to record single neurons’ responses in order to examine whether spatially clustered curve or corner-selective neurons could be found. If such neural clusters were found, we further aimed to understand how different curves and corners are encoded and differentiated in greater detail.

We injected AAV1-hSyn-GCaMP into dorsal V4 (V4d) of two rhesus macaques — GCaMP6f for monkey A and GCaMP5G for monkey B. An imaging window and head posts were implanted 1–2 months after viral injection (see Materials and methods). Subjects were trained to initiate and maintain fixation within a 1° circular window for 2 s: the first second contained the fixation spot alone, and then stimuli appeared for 1 s on a LCD monitor positioned 45 cm away (17 inch, 1280 × 960 pixel, 30 pixel/°). Neuronal responses were recorded using 2P calcium imaging, with differential images generated using ΔF = F – F0, where F0 is the average fluorescence 0.5–0 s before stimulus onset, and F is the average response 0.5–1.25 s after stimulus onset.

Cortical mapping of curve-biased and corner-biased patches in V4

We first identified the retinal eccentricity using drifting gratings for our sites and found they were positioned with an eccentricity of ~0.7° from the fovea in monkey A and ~0.9° in monkey B. We next used a low-power (4×) objective lens to identify and localize any cortical subregions selectively activated by curves or corners. Using a large range of contour feature stimuli including bars, curves, and corners (Figure 1A), we scanned a large area (3.4 × 3.4 mm) in V4d (Figure 1B, C, Figure 1—figure supplement 1) between the lunate sulcus (LS) and the terminal portion of the inferior occipital sulcus (IOS). We obtained global activation maps by Gaussian smoothing (standard deviation σ = 10 pixels, 67 μm) the ΔF/F0 maps. We observed that orientation is organized in linear iso-orientation domains or pinwheel-like patterns, as previously reported (Roe et al., 2012), using ISOI in V4 (Figure 1—figure supplement 2).

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We then examined the response to curve and corner stimuli. Using map subtraction, we computed the curve-selective activation as the average response (ΔF/F0) to all curves minus all corners and bars, and corner-selective activation as the average response to all corners minus all curves and bars (Figure 1D, E). The subtraction maps we obtained clearly revealed several possible candidates for curve- or corner-selective patches. To statistically detect and locate the curve and corner patches, we performed pixel-level FDR tests to examine the curve or corner preference. For each pixel, we performed independent t-tests to compare the responses to all curves, all corners, and all bars, obtaining the p-value maps for curve and corner selectivity (see Materials and methods and Figure 1—figure supplement 3). We then computed the FDR (false dicovery rate) using Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995), with the threshold level q = 0.01 to locate the significant patches. Cluster permutation tests were also performed to exclude patches with not enough significant pixels (Nichols and Holmes, 2002). We found several patches significantly selective to curves or corners in dorsal V4 (Figure 1F, G). These curve- or corner-selective patches were considered candidates for functional domains encoding shape segments in V4.

Single-cell mapping of curve- and corner-selective neurons reveals they are spatially clustered

To confirm that neurons within these patches were indeed curve or corner selective, we next performed single-cell resolution imaging with a high-power objective lens (16×) to record neuronal responses (ΔF/F0) as well as their spatial organization (Figure 2—figure supplement 1). The imaging sites (850 × 850 μm) in both subjects were chosen to include both curve- and corner-selective domains found by our 4× imaging (Figure 1F, G). 535 visually responsive neurons (292 from monkey A and 243 from monkey B) were recorded in total. Each stimulus was repeated 10 times and averaged to derive neuronal responses (Figure 2—figure supplement 2). To characterize neurons’ curve and corner selectivity, we calculated a curve selectivity index (CVSI) and corner selectivity index (CNSI). A positive CVSI value indicates a neuron’s maximal response to curves is stronger than its maximal response to other stimuli: a CVSI = 0.33 signifies a response twice as strong, and a CVSI = 0.2 is 1.5 times as strong. The same definition applies to CNSI. 70.5% (74 out of 105) neurons with CVSI > 0.2 significantly (one-way ANOVA, p<0.05) preferred curves over corners and bars, and 76.9% (120 out of 156) for CNSI (Figure 2—figure supplement 3A, B). We found neurons with high CVSI or CNSI were spatially clustered (Figure 2A–D), and these neurons were also selective to the orientation of the integral curves or corners (Figure 2E–H; 91.6% of the neurons are significantly tuned to the orientation of curves or corners; one-way ANOVA, p<0.05). Their overall spatial distribution was consistent with the spatial distribution of curve and corner domains revealed by 4× imaging (Figure 2A–D vs. Figure 1E, F), especially considering the possible loss of detailed spatial information during Gaussian smoothing of 4× images. This parsimoniously suggests that the observed cortical activation was evoked by responsive neuronal clusters.

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We next assessed this clustering quantitatively by examining how neuronal responses correlate with spatial distance. For each neuronal pair recorded from the same subject, we computed the pairwise tuning correlation and absolute value differences for CVSI and CNSI plotted against the neuronal pairwise distances. We found that neurons close to each other (<300 μm approximately) often had more correlated tuning (Figure 2I) and generally exhibited more similar CVSI and CNSI values (Figure 2J, K). These results indicate curve-selective and corner-selective neurons are spatially clustered, which could potentially form curve domains and corner domains in V4, which could therefore be detected when imaged at a larger scale.

Out of all 535 neurons recorded from two animals, the majority (346 neurons, 64.7%) significantly preferred curve and corner stimuli over single bars, and only 1.5% (eight neurons) significantly preferred bars over curves and corners (Figure 3—figure supplement 1A), indicating that neurons in these areas were indeed much more likely to encode more complex shape features compared to simple orientation. Therefore, we made a combined cell map to depict curve and corner selectivity (Figure 3A), neglecting bar responses, by calculating curve/corner index (CVCNI). Similar to CVSI and CNSI, positive CVCNI values indicate a neuron’s maximum response to curves is stronger than its maximum response to corners, and vice versa. As expected, neurons with similar CVCNI values were spatially clustered. Neurons that fell into the 4×-defined curve domains generally had positive CVCNI values (Figure 3B) and those in the 4× corner domains generally had negative CVCNI values (Figure 3C). We also performed a one-way ANOVA comparing neurons’ maximum curve and corner responses. We found that neurons with CVCNI > 0.2 or <−0.2 (which means 1.5 times as strong) predominantly showed significant preferences (p<0.05) to curves or corners over the other kind (Figure 3D). The curve- or corner-selective neurons (red and blue neurons in Figure 3D) have very diverse curve or corner tuning, and could be either selective or invariant to the radius and radian of curves or bar length and separation angle of corners (Figure 3—figure supplement 1B–D), which potentially enables the encoding of multiple shape segments. More interestingly, these neurons that were heavily biased to curves or corners over the other tended to respond very weakly to single bars (Figure 3E), implying that they might be detecting more complex and integral shape features instead of local orientation. These results suggest that curves and corners are encoded by different neuronal clusters organized in curve and corner domains, and these domains are distinct from those representing single orientations.

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Curve-preferring neurons are selective for smoothness

Curves and corners are both different from single bars in that they potentially contain multiple different local orientations, yet we found them to be encoded by different neuronal clusters in V4. This suggests that V4 neurons are not recognizing shapes with more than one local orientation, but computing a more fundamental feature difference. To investigate what distinguishes curves from corners in V4, we tested hexagonal segments (Π-shape stimuli; Figure 4A) that highly resemble curves except for a lack of smoothness (Nandy et al., 2013). We found that neurons that were very selective to smooth curves did not respond strongly to Π-shape stimuli (Figure 4A), suggesting that they were selective to smoothness, rather than multiple orientations. In the same way as CVCNI, we calculated curve/Π-shape index (CVPII), which characterizes a neuron’s preference to smooth curves over the Π-shape stimuli. We found that neurons’ CVPII were highly consistent with CVCNI (R = 0.72, p<0.001, Figure 4B), which means neurons preferring smooth curves over corners would also prefer smooth curves over Π-shape stimuli. As a result, the maps of CVPII were also consistent to CVCNI maps (Figure 4C vs. Figure 3A). K-means clustering analysis of population responses also showed that smooth curves are encoded differently from rectilinear shapes including Π-shapes and corners (Figure 4—figure supplement 1). Therefore, smoothness is important to the distinct encoding of curves and corners in the specific curve domains and corner domains in V4.

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Curve and corner selectivity is related to concentric and radial grating preference

Early studies in V4 demonstrated that many V4 neurons are selective for non-Cartesian gratings (David et al., 2006; Gallant et al., 1993; Gallant et al., 1996). While concentric gratings highly resemble curves and radial gratings resemble corners, this result highly implied the curve/corner preference. Therefore, we wondered whether these two types of gratings are also separately encoded by neurons in curve and corner domains. So in addition to contour feature stimuli, we also tested concentric, radial, and Cartesian gratings (Figure 5—figure supplement 1A). The resultant selectivity maps were consistent with the contour feature maps as predicted. 48.4% of the neurons recorded in the imaging areas significantly preferred concentric or radial gratings over Cartesian gratings, while only 2.2% significantly preferred Cartesian gratings (Figure 5—figure supplement 1B). In addition, many of them were heavily biased to one over the other. Similar to CVCNI, we computed concentric/radial index (CRI) to characterize this bias. CRI and CVCNI values were found to be correlated (R = 0.38, p<0.001; Figure 5B, Figure 5—figure supplement 2), and naturally their cell maps were also consistent (Figure 5C vs. Figure 3A), suggesting that classical polar grating selectivity is closely related to curve and corner selectivity. Meanwhile, to assess whether the observed selectivity is related to different spatial frequencies, we examined the CRI map at 1, 2, and 4 cycle/°. The CRI values of all neurons at three spatial frequencies are highly correlated (Pearson correlation, all R > 0.5, p<0.001), and the map structures were found to remain consistent across three spatial frequencies (Figure 5C), implying such selectivity is not directly related to spatial frequency.

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Using 2P calcium imaging, we identified cortical patches in macaque V4d selective for curves or corners (Figure 1E, F), with individual curve- and corner-selective neurons consistently clustered spatially (Figure 3A). These neurons exhibited diverse curve or corner selectivity (Figure 3—figure supplement 1B–D) and could potentially be involved in the encoding and processing of a large variety of curves and corners. These results demonstrate the existence of functionally specific curve and corner domains in V4d.

Functional organization for low-order orientation and spatial frequency representations in macaque V4 had previously been visualized using ISOI (Lu et al., 2018; Roe et al., 2012). For more complex shape features, very few studies have been carried out in V4 to characterize its functional organization, let alone at single-cell resolution. We report here the existence of cortical micro-domains consisting almost entirely of neurons selective for curves. This finding at the single-cell level is consistent with an fMRI study that reported curvature-biased patches in macaque V4 (Yue et al., 2014). The patches we found were smaller in size (about 300 μm) than those observed using fMRI; we suspect due to the improved spatial resolution afforded by 2P imaging. Additionally, we also found cortical domains in V4d selective for corners. Apart from this fMRI study, one of the reasons why exactly the curve/corner contrast was used to study functional domains in V4 was that in our recent study using natural images we found smooth curves and rectilinear corners to be one of the dominant features encoded by many V4 neurons (Jiang et al., 2019). Here, we directly demonstrated and visualized the combined functional organization of smooth curves and rectilinear corners in V4 at both cortical and single-cell level.

Two recent papers have also reported curvature domains in anesthetized macaque V4. Hu et al., 2020 used ISOI, finding functional domains that prefer curved over straight gratings. Tang et al., 2020 used both ISOI and 2P imaging, finding functional domains that prefer circles over rectilinear triangles. In general, these two imaging studies alongside our own provide clear replication of the core importance of curvature as an organizing principle in the functional architecture of V4. Compared to ISOI, 2P imaging holds the advantage of higher spatial resolution, and therefore makes it possible to characterize the transition between domains more precisely than Gaussian smoothed ISOI. We found the transition taking place within around 300 µm, remaining relatively elevated thereafter (Figure 2—figure supplement 3, Figure 5—figure supplement 2). Comparing the different stimulus set (curves vs. corners, concentric vs. radial), the transition of CRI maps of monkey A in Figure 5A looked sharper probably because too many neurons had negative CRI values. CRI and CVCNI were correlated but not identical. Since concentric gratings only have 360° full circles but some neurons might prefer short arches (small radian, Figure 3—figure supplement 1), it is possible that they do not respond strongly to concentric gratings and tend to have negative CRI.

A number of electrophysiology studies have reported that some neurons in V4d are selective for more complex features (Gallant et al., 1993; Hegdé and Van Essen, 2007; Kobatake and Tanaka, 1994; Pasupathy and Connor, 1999). Our results, consistent with these works, identified many curve- or corner-selective neurons. In addition, given the ability of 2P imaging to quantify the spatial relationships between neurons, we confirm that they are spatially clustered. We also observed some deviations of our results from earlier studies. First, the percentage of complex feature-selective neurons we found in our study is higher than previously observed (Gallant et al., 1993; Pasupathy and Connor, 1999); in our hands, the vast majority of neurons preferred curves and corners over bars and concentric and radial gratings over Cartesian gratings. Second, although Π-shape stimuli were sometimes regarded also as curved contours (Nandy et al., 2013), we found V4 neurons responding to them differently. We think these two deviations are primarily due to sampling neurons within or close to curve and corner domains (which is difficult to detect with classical electrophysiology). We do not wish to infer that curve and corner stimuli are only encoded by neurons in the curve and corner domains while other neurons are not involved. But we have demonstrated that neurons in the curve and corner domains are tuned to more complex and integral features rather than local orientation, spatial frequency, or multiple orientations, alone, supporting the encoding of shape segments with intermediate complexity in V4 (Bushnell and Pasupathy, 2012; El-Shamayleh and Pasupathy, 2016; Oleskiw et al., 2014; Rust and DiCarlo, 2010).

Complexity increases as visual shape information is processed along the ventral visual pathway. Neurons in V1 are tuned to low-order orientation and spatial frequency and organized in iso-orientation domains and orientation pinwheels (Nauhaus et al., 2012; Ts'o et al., 1990). Neurons in IT are selective for complex features and objects and organized in feature columns and face patches (Tanaka, 2003; Tsao et al., 2006; Tsunoda et al., 2001). The simple-to-complex transformation and integration take place in the intermediate stages between V1 and IT. Researchers have reported that some V2 neurons are selective to combination of multiple local orientations, from which corner selectivity might emerge (Anzai et al., 2007; Ito and Komatsu, 2004). Our results in V4d showed that intermediate shape segments like curves and corners are separately encoded by neurons in specific functional domains, and the curve- and corner-selective neurons are tuned to the integral features instead of local orientation or combination of orientations. It is possible that these complex feature-selective neurons receive inputs or modulation from nearby neurons or downstream areas to form a recurrent network, which might underlie previous findings that the response profiles of V4 neurons were temporally heterogeneous (Nandy et al., 2016; Yau et al., 2013). Such evidence is also recently accumulating for IT cortex (Kar and DiCarlo, 2021). Unfortunately, this question is difficult to address given the temporal resolution of the existing calcium imaging technique. One possible solution is to use genetically encoded voltage indicators (Xu et al., 2017; Yang and St-Pierre, 2016), which once successfully applied in macaques could help to reveal the simple-to-complex integration of neurons.

Given that we recorded neurons whose stimulation was not isolated to the ‘optimal’ spatial location in the receptive fields (i.e., the RF locations of some neurons might deviate for the population RF), the nature of the domains may also be modulated by stimulus translation variance, and future studies addressing positional variance and stimulus encoding are warranted. Our sample of V4d was also near-foveal in terms of eccentricity. It is well established that the ventral pathway connectivity to IT favors central rather than peripheral visual space (Ungerleider et al., 2008), but the relationship of visual eccentricity to these functional domains remains unknown. The existence of curve and corner domains for neuronal encoding in V4d provides significant support for integration of shape information in the intermediate stages of the visual hierarchy. These findings provide a more comprehensive understanding of the functional architecture of V4 feature selectivity.

Finally, our results may also help to explore the later stage of the visual hierarchy. The data suggests that higher-order pattern domains may emerge gradually along the ventral pathway. The specificity of clustered patches/domains in the cortex has been proposed as an important organizing principle for some, though not all, domains of cortical processing (Kanwisher, 2010). A recent study has suggested that, at least for faces and color processing, such functional domains are causally specific for human visual recognition (Schalk et al., 2017). The curve and corner domain responses in V4 could possibly form the basis for more complex feature columns, object domains, and face patches in IT. This is consistent with a growing body of evidence from the ventral stream (Bao et al., 2020; Rajalingham and DiCarlo, 2019; Yue et al., 2014). Recent explorations of neuronal response fields in artificial neural networks have likewise found a prevalence of curve detectors with increasing complexity along the processing hierarchy (Cammarata et al., 2020). Studying such functional cross-areal connectivity (both bottom-up and top-down) remains a critical goal for future studies of the visual system. It is also interesting to try to identify why smooth curves and rectilinear corners are separated as early as V4. One possible explanation is that smooth curves are more prevalent in living animals or foods that are of particular interest to primates, while corners are often found in the background environment of stones or branches. Such differences may underlie the statistical regularities in natural images of objects (Long et al., 2018; Levin et al., 2001; Yetter et al., 2020; Zachariou et al., 2018). Such comparisons will provide a basis for future investigations comparing the statistical feature relationships for natural images between V4 and IT functional domains.

All procedures involving animals were in accordance with the Guide of Institutional Animal Care and Use Committee (IACUC) of Peking University Laboratory Animal Center and approved by the Peking University Animal Care and Use Committee (LSC-TangSM-5).

The data and MATLAB codes used in this study can be found in GitHub (https://github.com/RJiang1994/macaque-v4-2P; copy archived at https://archive.softwareheritage.org/swh:1:rev:57dfeac5e81b91c93ef0687f8cf04010d3f47f8c).

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Author details

1. Rundong Jiang

   1. Peking University School of Life Sciences, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Beijing, China
   3. IDG/McGovern Institute for Brain Research at Peking University, Beijing, China
   4. Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9217-0749

2. Ian Max Andolina

   The Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

   Contribution

   Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9985-3414

3. Ming Li

   Beijing Normal University Faculty of Psychology, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5173-1602

4. Shiming Tang

   1. Peking University School of Life Sciences, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Beijing, China
   3. IDG/McGovern Institute for Brain Research at Peking University, Beijing, China
   4. Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China

   Contribution

   Conceptualization, Resources, Data curation, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   For correspondence

   tangshm@pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0294-3259

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