The concept of a receptive field (RF) is crucial for our understanding of the mechanisms underlying perception, cognition, and action. RFs (Hartline, 1938; Sherrington, 1906) typically describe stimulus locations that evoke or modulate neuronal responses, but they can be generalized to different stimulus features such as color or spatial frequency. RFs are usually measured by determining the neuronal firing rate elicited by visual stimuli (Hubel and Wiesel, 1998; Hubel and Wiesel, 1968; Hubel and Wiesel, 1959), but they can also be defined based on other neuronal signals such as subthreshold activity (Priebe, 2008), properties of the local field potential (LFP; Victor et al., 1994), or calcium levels that can, for instance, be measured with fluorescent calcium indicators (van Beest et al., 2021; Bonin et al., 2011).

Noninvasive methods lack the spatial resolution to measure the RF properties of single neurons, but they can characterize the RF properties of the aggregate neural signals being measured. The retinotopic organization of the human brain has now been characterized with functional magnetic resonance imaging for decades (Wandell et al., 2007; Wandell and Winawer, 2011). Early studies used phase-encoding with ‘rotating wedge’ and ‘expanding or contracting ring’ stimuli to identify RF position (Engel, 2012; Engel et al., 1994; Sereno et al., 1995), while later studies increasingly used the ‘population receptive field’ (pRF) method that estimates RF size in addition to position (Dumoulin and Wandell, 2008; Wandell et al., 2007; Wandell and Winawer, 2015; Wandell and Winawer, 2011). The method is popular and has been used to map a range of visual and cognitive functions (Binda et al., 2018; Ekman et al., 2020; Harvey et al., 2020; Harvey et al., 2015; He et al., 2019; Hughes et al., 2019; Mo et al., 2018; Poltoratski et al., 2019; Poltoratski and Tong, 2020; Puckett et al., 2020; Shao et al., 2013; Shen et al., 2020; Silson et al., 2018; Stoll et al., 2020; Thomas et al., 2015; Welbourne et al., 2018; Zuiderbaan et al., 2017), dysfunctions (Ahmadi et al., 2020; Alvarez et al., 2020; de Best et al., 2019; Dumoulin and Knapen, 2018; Green et al., 2019; Schwarzkopf et al., 2014), mechanisms of brain development (Dekker et al., 2019), cortical evolution (Keliris et al., 2019; Kolster et al., 2014; Zhu and Vanduffel, 2019), and information transfer across different brain areas (Haak et al., 2013).

The term ‘pRF’ highlights the analogy to neuronal RFs. It assumes that the blood-oxygen-level-dependent (BOLD) signal measured with fMRI reflects the aggregate response of a large population of neurons within a voxel. Indeed, pRFs of the human visual cortex are qualitatively similar to the RFs of single neurons or multi-unit activity (MUA) in animals (Dumoulin and Wandell, 2008). However, most of the previous comparisons were between species, and between studies that used different techniques to measure pRFs/RFs (Barlow et al., 1966; Hubel and Wiesel, 1968). Exceptions are pRF studies based on intracranial recordings in human patients (Harvey et al., 2013; Winawer et al., 2013), which used a limited number of surface electrodes to measure ECoG or intracranial EEG, but not spiking activity. The pRFs derived from the LFP exhibited similar properties to pRFs derived from BOLD signals, including similar spatial summation characteristics (Winawer et al., 2013). Another study compared BOLD-based pRFs in monkeys to the RF properties of single units in published work (Kolster et al., 2014). Furthermore, Keliris et al., 2019 found that single-unit RFs in one of their monkeys were smaller than pRFs measured with BOLD and proposed another method to estimate RF sizes. Their study included MUA but not the LFP. A systematic within-species comparison of pRFs derived from BOLD, MUA, and LFP has however never been carried out.

Here, we fill that gap with extensive pRF modeling based on BOLD, MUA, and LFP signals in macaque monkeys. The question that neuronal signal forms the basis of the fMRI-BOLD signal has far-reaching consequences for the interpretation of human neuroimaging results and is therefore a topic of ongoing debate and rigorous investigation (Arthurs and Boniface, 2002; Bartels et al., 2008; Boynton, 2011; Drew, 2019; Ekstrom, 2010; Goense and Logothetis, 2008; Logothetis, 2010; Logothetis, 2003; Logothetis et al., 2001; Logothetis and Wandell, 2004; Maier et al., 2008; Schölvinck et al., 2010; Sirotin and Das, 2009; Winawer et al., 2013; Winder et al., 2017). Some studies reported that properties of the BOLD signal resemble features of both neuronal spiking and the LFP (Mukamel et al., 2005; Nir et al., 2007; Rees et al., 2000), others that they resemble the LFP but not spiking (Bartolo et al., 2011; Maier et al., 2008; Niessing et al., 2005; Viswanathan and Freeman, 2007), and yet others that they resemble spiking rather than the LFP (Lima et al., 2014). Here, we examine the degree to which pRFs based on the BOLD signal resemble pRFs based on spiking activity and distinct frequency bands of the LFP (Buzsáki, 2006; Buzsáki and Draguhn, 2004; Einevoll et al., 2013; van Kerkoerle et al., 2014). In nonhuman primates, we measured BOLD-pRFs using whole-brain fMRI and determined neuronal pRFs with large-scale neurophysiological recordings in V1 and V4 (Figure 1). Besides showing the presence of retinotopic information throughout the brain based on the fMRI data, we could directly compare V1 and V4 pRFs obtained with fMRI and electrophysiology. This intraspecies comparison provides new insight into the neurophysiological basis of the BOLD-defined pRFs and offers a benchmark for visual field maps obtained with fMRI.

Figure 1 with 1 supplement see all

Download asset Open asset

The original method of estimating pRFs from BOLD responses (Dumoulin and Wandell, 2008) uses a forward modeling approach to fit the location and size of a symmetrical two-dimensional Gaussian to the BOLD responses. This approach is often used to predict neuronal activity elicited by moving bar-shaped stimuli. The RF model minimizes the difference between measured and predicted responses by multiplying the pRF with the stimulus and convolving the result with a hemodynamic response function (HRF), which accounts for the time course of neurovascular coupling (Figure 1). Later refinements implemented a difference-of-Gaussians pRF profile (DoG) to account for center-surround interactions (Zuiderbaan et al., 2012; Figure 1C), with substantial improvements in early visual cortex. Another refinement is the introduction of a static nonlinearity that models nonlinear spatial summation across RFs (Britten and Heuer, 1999; Kay et al., 2013; Oleksiak et al., 2011; Winawer et al., 2013). In such a model, the best parameters indicate subadditive spatial summation in all visual areas (Kay et al., 2013). This means that if stimulus S₁ elicits a response R₁ and a nonoverlapping stimulus S₂ elicits response R₂, the response to the combined stimulus, S₁ + S₂, is smaller than the sum, R₁ + R₂. For this reason, the nonlinear spatial summation model has also been called the ‘compressive spatial summation’ (CSS) model. A third extension has been the modeling of negative pRFs. Standard approaches tend to only include voxels that show increases in the BOLD signal in response to a stimulus. The inclusion of ‘negative’ pRFs with decreased BOLD activity has revealed the retinotopic organization of a number of areas in the so-called default mode network (DMN) (Szinte and Knapen, 2020). In our analysis of pRFs based on BOLD, MUA, and LFPs, we explored several pRF models, allowing us to investigate the potential presence of nonlinear spatial summation and negative pRFs.

Four macaque monkeys (Macaca mulatta) participated in this study. They were rewarded with fluid for maintaining their gaze inside a 1.5° window around a fixation point that was presented at the center of a frontoparallel screen. While they fixated, a 2° wide bar containing full-contrast moving checkerboards traversed the screen in eight different directions (Figure 1). Two animals performed this task in a 3T horizontal bore MRI scanner. Two other monkeys were each implanted with 1024 electrodes in the visual cortex (V1, V4). They performed the same task while neuronal activity (MUA and LFP) was recorded simultaneously from all electrodes. In the MRI setup, the stimulus covered 16° of the visual field, which was the maximum possible with the monitor located just outside of the scanner bore. The bar traveled across this screen in 20 steps of 2.5 s (1 TR). In the electrophysiology setup, the monitor was closer to the animal, allowing a visual field coverage of 28°. The stimulus bar moved across this aperture in 30 steps of 500 ms. For both the MRI and electrophysiology recordings, we only included data from epochs when the animals maintained fixation for >80% of the time.

After preprocessing (see Materials and methods), we independently fit four pRF models to the average BOLD time courses. These models were (1) a linear pRF model constrained to have positive responses (P-LIN) (Dumoulin and Wandell, 2008), (2) an unconstrained version of the linear pRF model that can also model negative responses (U-LIN), (3) a DoG pRF model (Zuiderbaan et al., 2012), and (4) a nonlinear CSS pRF model (Kay et al., 2013; Figure 1). We used the fitting method to determine pRF size, shape, and location. A cross-validated goodness of fit (R²) was determined by fitting the model to one half of the data and calculating R² using the other half of the data. Cross-validation allows the comparison of fit quality between models with different numbers of parameters.

pRFs measured with BOLD-fMRI

All models provided good fits to the BOLD time courses in a range of cortical and subcortical areas known to be involved in visual processing. For both monkeys (M1 and M2), we found robust retinotopic information in occipital, temporal, and parietal cortex (Figure 2). pRFs in all these areas were in the contralateral visual field and retinotopic maps were consistent with previous reports (Figure 2B), some of which were more extensive (Arcaro et al., 2011; Arcaro and Livingstone, 2017; Brewer et al., 2002; Janssens et al., 2014; Kolster et al., 2014; Rima et al., 2020; Zhu and Vanduffel, 2019). Weaker and sparser retinotopic information was also observed in the frontal cortex, for example, around the arcuate sulcus (area 8, including the frontal eye fields [FEF]) and in the ventrolateral prefrontal cortex (VLPFC). Throughout this study, we will use a voxel inclusion criterion of R² > 5% unless otherwise noted. While R² was generally much higher in visual areas (Figure 2A, bottom panel), this relatively low threshold also reveals retinotopic information in more frontal areas and some subcortical regions (where the signal picked up by surface coils has a lower signal-to-noise ratio [SNR]). Figure 2C shows the number of voxels within a range of areas for which the models explained more than 5% of the variance (Figure 2—figure supplement 1 shows the proportion per region of interest [ROI]). The functional parcellation of visual areas based on field sign inversions around horizontal and vertical meridians lined up well with a probabilistic atlas, co-registered to the individual animal’s anatomy (D99, Reveley et al., 2017).

Figure 2 with 1 supplement see all

Download asset Open asset

Subcortically, we could segregate the lateral geniculate nucleus (LGN), pulvinar and some striatal regions from their surrounding areas on the basis of a higher R². In both monkeys, the LGN of both hemispheres contained clear retinotopic maps (Figure 3A). Retinotopic information was also evident in the bilateral pulvinar of M1, but some of the pRFs in the pulvinar of M2 were large and crossed the vertical meridian, resulting in noisy polar angle maps (Figure 3B). Striatal retinotopy was less pronounced in M1 and even more variable in terms of polar angle maps (Figure 3—figure supplement 1).

Figure 3 with 1 supplement see all

Download asset Open asset

We calculated cross-validated R² values to compare the four pRF models: P-LIN, U-LIN, DoG, and CSS (Figure 1C). A comparison of model performance pooled over subjects and voxels confirmed that there were significant differences across models, with the CSS model outperforming the P-LIN model (Kruskal–Wallis test on all four models, H = 21.33, df = 3, p<0.0001; post-hoc Tukey’s HSD multiple comparisons of mean rank, R²_CSS > R²_P-LIN, p<0.0001). Indeed, the CSS model fit was better than that of P-LIN in all ROIs in M1 and in 38 out of 39 ROIs in M2 (Wilcoxon signed-rank, p<0.05; no difference in premotor area F7 in M2) (Figure 4). Since the CSS model also provided the best fits for the neurophysiological signals (described later), we report results from this model and only extend the analysis to other models where this is useful (e.g., in the case of negative gain values). The advantage of the CSS model over the P-LIN model generally increased in higher visual areas (Figure 4—figure supplement 1).

Figure 4 with 2 supplements see all

Download asset Open asset

The static nonlinearity parameter, or ‘pRF exponent’ that models the nonlinearity of spatial summation (Materials and methods: Equation 4), was in the range of 0.2–0.4 and significantly below 1 in all areas (Wilcoxon signed-rank, one-tailed, all ROIs with more than four voxels R² > 5%, p<0.001). These values of the pRF exponent represent subadditive (compressive) spatial summation, in accordance with observations in the human visual cortex (Kay et al., 2013; Winawer et al., 2013). The pRF exponent in early visual cortex is comparable to previously reported values for human V1. The exponent value in higher visual areas was similar to that in early visual cortex of the two monkeys, and higher than previously observed in human extrastriate cortex, which suggests that spatial suppression is less pronounced in higher areas of the monkey visual cortex than in higher areas of the human visual cortex.

Both the U-LIN model and the DoG model also performed better than the standard P-LIN model (Kruskal–Wallis, Tukey’s HSD, both ps<0.0001). The DoG had slightly better fits across all pooled voxels than the U-LIN model (Kruskal–Wallis, Tukey’s HSD, p<0.0001). The advantage of the DoG model over the P-LIN model was most pronounced in V1 and decreased in higher visual areas.

pRF size as a function of eccentricity

As expected, pRF sizes were larger at higher eccentricities. This relationship was evident in all areas with larger numbers of well-fit voxels. pRFs were also larger in higher areas, which exhibited a steeper slope of the eccentricity-size relationship (Figure 5, Figure 5—figure supplements 1 and 2). The differences between the slopes in V1 and V2 are smaller than expected based on previous electrophysiological studies, but this is not uncommon with fMRI (Kay et al., 2013). In one animal, we also unexpectedly observed retinotopy in a number of higher areas, such as the anterior cingulate cortex (Figure 5—figure supplements 1 and 2). This brain region has been studied predominantly in the context of decision-making (Amiez et al., 2006; Fouragnan et al., 2019), but it does have resting state correlations with V1 (Griffis et al., 2017). Our design lacked the power for a more detailed investigation of this retinotopic organization, but this result may inspire future work on brain-wide retinotopic tuning.

Figure 5 with 2 supplements see all

Download asset Open asset

Multi-unit spiking activity RFs

We determined the RFs of MUA recorded with chronically implanted electrode arrays (Utah arrays) in areas V1 and V4 in two additional monkeys (M3 and M4) that did not participate in the fMRI experiments. We used a 1024-channel cortical implant, consisting of a titanium pedestal that was connected to 16 Utah arrays (Rousche and Normann, 1998). Each Utah array had 8 × 8 shanks with a length of 1.5 mm. In both monkeys, 14 arrays were placed in V1 and 2 in V4 of the left hemisphere (Figure 6). The stimulus was similar to that used in the fMRI experiments with some small differences due to constraints of the two setups (in the electrophysiology setup, the stimulus covered a larger portion of the visual field because the screen was closer to the animal) and the intrinsic nature of the recorded signals (stimulus steps were faster in the electrophysiology experiments because the electrophysiology signals are much faster than the BOLD response). We fit the four pRF models to the MUA responses and to the LFP power in five distinct frequency bands. We compared the MUA pRFs to a more conventional MUA RF-mapping method (cRFs). For this cRF method, we selected channels with an SNR larger than 3 (i.e., visual responses that were more than three times larger than the standard deviation of the spontaneous activity) and derived the RF borders from the onset and offset of the neuronal activity elicited by a smoothly moving light bar (see Materials and methods). Whenever both methods were able to estimate a pRF and cRF (R² > 25% for the pRF method, SNR > 3 and R² > 25% for the cRF method), the estimated locations were highly similar (median distance between pRF and cRF center, V1: 0.34, IQR 0.18–0.49 dva; V4: 0.90, IQR 0.40–1.41 dva). Compared to the P-LIN pRF model, the moving bar method estimated smaller cRFs (median size difference pRF_sz-cRF_sz: 0.50, IQR: 0.08–0.92) (Figure 7). The CSS model, however, returned pRF size estimates that were very similar to the cRF sizes or even a little bit smaller (median size difference pRF_sz-cRF_sz: –0.13; IQR: –0.41–0.14), suggesting that nonlinear spatial summation might indeed be better at capturing the RF properties of a small population of neurons than linear summation.

Figure 6 with 1 supplement see all

Download asset Open asset

Figure 7

Download asset Open asset

The pRF models that were used in this study are all based on circular (symmetric) RFs. A recent study (Silson et al., 2018) suggested that pRFs in human early visual cortex might be elliptical rather than circular, although this suggestion goes against previous work (Greene et al., 2014; Merkel et al., 2018; Zeidman et al., 2018). A later study demonstrated that the elliptical fits were an artifact of the software that had been used in the analysis (Lerma-Usabiaga et al., 2021). The cRF method separately estimates the width and height of the RF, and can thus be used to calculate a simplified RF aspect ratio to investigate RF symmetry. While this measure differs from RF ellipticity (a 45° tilted ellipse has the same aspect ratio as a circle), it does provide some insight into the symmetry of the MUA RFs. We did observe a few cRFs with aspect ratios (σ_large/σ_small) that were larger than 2 (M3: 18/753; M4: 10/527; together 2.2% of all cRFS), but the vast majority of cRFs in both animals had aspect ratios close to 1 (M3 median: 1.12, IQR: 1.04–1.20; M4 median 1.13, IQR: 1.03–1.22) indicating near-symmetric RFs.

We obtained excellent fits to the MUA for all pRF models (see Figure 1E for an example fit). These pRFs covered a large proportion of the lower-right visual field (Figure 6, Figure 6—figure supplement 1). As expected, the pRFs from electrodes of the same arrays (shown in the same color in Figure 6) were clustered in space, and their locations were in accordance with established retinotopy (e.g., Hubel and Wiesel, 1974). The average R² (over all electrodes with R² > 0) was 64% in V1 (M3: 54%; M4: 73%) and 53% in V4 (M3: 37%; M4: 68%), which is substantially higher than the average R² of 11% in both V1 and V4 for the MRI data (all voxels with R² > 0; V1 M1: 14%, M2: 8%; V4 M1: 14%, M2: 8%).

Cross-validated comparisons revealed significant differences between the four models (Kruskal–Wallis test on all four models: H_V1 = 204, df_V1 = 3, p_V1 < 0.0001; H_V4 = 13.4, df_V4 = 3, p_V4 < 0.01) (Figure 8; similar patterns were present in each individual animal). Post-hoc pairwise comparisons (Tukey’s HSD) revealed that the CSS and DoG models provided a better fit than the linear models, although for V4 the advantage of the CSS model over the P-LIN model was only significant when electrodes with a poor fit (R² < 25%) were excluded (V1, all electrodes: CSS vs. P-LIN, p<0.001, DoG vs. P-LIN, p<0.001; V4, all electrodes: CSS vs. P-LIN, p=0.16, DoG vs. P-LIN, p<0.001; V1, electrodes with R² > 25%: CSS vs. P-LIN, p<0.001, DoG vs. P-LIN, p<0.001; V4, electrodes with R² > 25%: CSS vs. P-LIN, p<0.02, DoG vs. P-LIN, p<0.01) (Figure 8). The improved fit of the DoG model was caused by the suppressive surround (median normalized suppressive amplitude = 0.71, IQR 0.56–0.86).

Figure 8 with 3 supplements see all

Download asset Open asset

The pRF exponent of the CSS model was 0.38 ± 0.23 in V1 (mean ± SD; 1358 electrodes with R² > 25%), which was significantly smaller than 1 (Wilcoxon signed-rank, one-tailed, z = –31.59, p<0.0001) and similar to the MRI-based values, which had a mean of 0.34 ± 0.19 (6341 voxels). Likewise, in V4, MUA pRF exponent values were significantly smaller than 1 (0.33 ± 0.19; z = 11.13, p<0.0001; n = 165), and comparable to MRI-based values in the same area (0.30 ± 0.16; n = 2324). The similarity in the values of the pRF exponent indicates that subadditive spatial summation is a prominent feature in these areas.

The size of the estimated MUA pRFs increased with eccentricity (Figure 9). However, the pRF sizes for two of the V1 arrays in both monkeys were approximately three times smaller than expected when compared to the data from the other arrays. These outlying arrays were located on the posterior-medial side of the surface of V1 in both monkeys where the gray matter is relatively thin (Figure 9A). We therefore suspect that the 1.5-mm-long shanks of the Utah arrays were pushed into the white matter, where they picked up activity of thalamic afferents (i.e., the geniculostriate pathway). We therefore excluded these electrodes from the analyses of pRF sizes. The remaining MUA pRF sizes and eccentricities were highly similar to RFs reported in previous electrophysiology studies at similar eccentricities (Gattass et al., 1987; Gattass et al., 1981; Van Essen et al., 1984; Victor et al., 1994). Next, we compared pRFs between the electrophysiological signals and the fMRI-BOLD signal.

Figure 9

Download asset Open asset

Local field potential pRFs

The LFP was split into five frequency bands: θ (4–8 Hz), α (8–16 Hz), β (16–30 Hz), γ_low (30–60 Hz), and γ_high (60–120 Hz). We fit the four models to estimate pRFs for each frequency band. The results for γ_low and γ_high resembled those for the MUA with high R² values for a large proportion of the electrodes (especially in V1). The CSS model again outperformed the other models, and we did not observe negative pRFs in these V1 and V4 regions (Figure 8—figure supplements 1 and 2). Electrodes with good MUA-pRF fits usually also had good LFP-pRFs, but the opposite was not always true (Figure 8—figure supplement 3). The pRFs in lower frequency bands differed. Whereas θ generally yielded low R² values, α and β yielded good fits for a substantial number of electrodes. Interestingly, there were two classes of electrodes in these frequency bands. For the first class, the power increased with visual stimulation and CSS model fits were best. In contrast, the second class of electrodes had negative pRFs, that is, the stimulus suppressed power (Figure 8—figure supplement 1). Given the few electrodes with good low-frequency LFP-pRFs in V4, we focused our analysis on the positive and negative responses on V1 (the split into positive/negative pRF was based on the parameters of the U-LIN model; Figure 10, Figure 10—figure supplement 1).

Figure 10 with 2 supplements see all

Download asset Open asset

We observed a number of remarkable differences between the positive and negative pRFs. First, negative pRFs generally had lower eccentricities than positive pRFs (Figure 10B, Figure 10—figure supplement 1B). Second, negative pRFs were larger than positive pRFs (Figure 10C, Figure 10—figure supplement 1C). Third, there was a systematic difference in the distance from the γ-pRF of the same electrode. Specifically, we calculated a ‘separation index’ by dividing the distance between the centers of the low- and high-frequency LFP-pRFs by their summed size estimates (SI = Distance/(σ_lf + σ_hf)). A separation index of less than 1 indicates pRF overlap. For both α and β power, the negative pRFs were farther from the γ-pRFs than the positive pRFs (Figure 10D, Figure 10—figure supplement 1D) and generally shifted in the direction of fixation (Figure 10E, Figure 10—figure supplements 1E and 2). These results suggest that the positive pRFs represent visually driven activity, whereas the negative pRFs signal a form of suppression that is strongest at smaller eccentricities, close to the fixation point. One possible explanation is that the monkeys directed attention to the fixation point, which may have caused a ring of suppression closely surrounding it. Another possibility is that the negative pRFs might be a consequence of small eye movements around the fixation point. The size of α-pRFs did not depend on eccentricity (Figure 10F), whereas the size of β-pRFs increased with eccentricity, although this relation was very weak for positive pRFs (Figure 10—figure supplement 1F).

The pRFs derived from all electrophysiology signals on the same electrode had similar locations, with less than 1 dva between their centers on average (CSS model, Figure 11A). We next analyzed RF sizes, normalizing the size estimates to the MUA-cRF. All LFP-pRFs estimates were larger than MUA-pRFs, and lower frequency LFP components yielded larger pRFs than the higher frequencies (Figure 11B; we observed the same pattern present in each animal). The pRF exponent was well below 1 for all LFP components (V1 and V4 electrodes with R² > 25%; Wilcoxon signed-rank, one-tailed <1, p<0.001) and smaller at lower frequencies, indicative of stronger CSS. We also compared the exponents to those of the MRI-pRFs. The exponent of the MRI was not significantly different from that of γ_low in V1, and both γ_low and γ_high in V4 (p>0.05, Figure 11C). Differences between the MRI exponent and those of the other LFP bands were significant (V1: Kruskal–Wallis, H = 505.49, df = 6, p<0.0001; V4: Kruskal–Wallis, H = 21.65, df = 3, p<0.001; Tukey’s HSD for multiple comparisons).

Figure 11

Download asset Open asset

Comparison of pRF eccentricity-size relationship between fMRI and electrophysiology signals

We next compared the eccentricity-size relationship of the electrophysiological signals to that of the BOLD-fMRI pRFs using linear mixed models (LMMs) to evaluate the pRF estimates of the CSS model, separately for V1 and V4 (electrodes with R² > 50% and R² > 5% for MRI). We included voxels for which the pRF was in the lower-right visual quadrant where we had electrode coverage. In V1, positive eccentricity-size relationships existed for MRI, MUA, β, γ_low, and γ_high (Figure 12A). We compared these signals with a single LMM, revealing an interaction between signal type and eccentricity (F = 14.44, df = 4, p<0.0001), which indicated that the slopes differed. We further used pairwise LMMs to compare the slope of the MRI-pRFs to the electrophysiological signals with a significant eccentricity-size relationship. The fMRI eccentricity-size relationship was similar to that of MUA (F = 0.38, df = 1, p=0.54), whereas it was significantly different from all LFP signals (β: F = 7.97, df = 1, p<0.01; γ_low, F = 20.02, df = 1, p<0.001; γ_high, F = 20.05, df = 1, p<0.001) (Figure 12C). We repeated this analysis in V4 (Figure 12B) where we had fewer electrodes. We obtained positive eccentricity-size slopes for MUA, γ_low, and γ_high but did not obtain fits of sufficient quality for the θ, α, and β bands. In contrast to V1, there was no clear difference across these signals (F = 1.11, df = 3, p=0.24). Pairwise comparisons with the MRI size-eccentricity relationship did not reveal a difference between those for γ_low (F = 0.36, df = 1, p=0.55), γ_high (F = 0.28, df = 1, p=0.60), or MUA (F = 2.40, df = 1, p=0.12). Because the visual field coverage in V4 differed between the two animals that were used for electrophysiology, we repeated the analysis in both individuals and observed similar results (not shown).

Figure 12 with 1 supplement see all

Download asset Open asset

To further investigate the robustness of these results, we repeated the analysis (1) with the inclusion of either all V1 and V4 voxels or a subset of the voxels in approximately the same anatomical location as the electrode arrays and (2) by varying the R²-based inclusion criteria for electrodes and voxels (Figure 12—figure supplement 1). MUA-pRFs in V1 and V4 were generally similar to the BOLD-pRFs, although in V4 γ_low and γ_high also approximated the fMRI results in some of the comparisons.

The current study conducted a systematic comparison of pRFs obtained with fMRI and electrophysiological recordings in V1 and V4 of awake behaving macaque monkeys. Within the same species, we fit several pRF models to seven different signal types (BOLD, MUA, and the power in five LFP frequency bands) to gain insight into the neuronal basis of MRI-BOLD-pRF measurements (Wandell and Winawer, 2015). Our results demonstrate retinotopic tuning in many brain regions and the presence of negative pRFs in areas of the DMN. We found that subadditive spatial summation is a prominent feature of many measures of brain activity. Furthermore, the results establish a clear relationship between BOLD-pRFs and electrophysiologically determined pRFs, with MUA-pRFs being most similar to BOLD-pRFs (as discussed below).

All data and code are available on GIN: https://doi.gin.g-node.org/10.12751/g-node.2j01af. Unthresholded fMRI model fitting results are available on Neurovault: https://identifiers.org/neurovault.collection:8082.

1. 1. Klink PC
   2. Chen X
   3. Vanduffel W
   4. Roelfsema PR

   (2021) NeuroVault

   ID 8082. Visual population receptive fields - macaque fMRI.

   https://identifiers.org/neurovault.collection:8082
2. 1. Klink PC
   2. Chen X
   3. Vanduffel W
   4. Roelfsema PR

   (2021) GIN

   Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex.

   https://doi.org/10.12751/g-node.2j01af

1. 1. Ahmadi K
   2. Fracasso A
   3. Puzniak RJ
   4. Gouws AD
   5. Yakupov R
   6. Speck O
   7. Kaufmann J
   8. Pestilli F
   9. Dumoulin SO
   10. Morland AB
   11. Hoffmann MB

   (2020) Triple visual hemifield maps in a case of optic chiasm hypoplasia

   NeuroImage 215:116822.

   https://doi.org/10.1016/j.neuroimage.2020.116822

   • PubMed
   • Google Scholar
2. 1. Allman JM
   2. Miezin F
   3. McGuinness E

   (1985) Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons

   Annual Review of Neuroscience 8:407–430.

   https://doi.org/10.1146/annurev.ne.08.030185.002203

   • PubMed
   • Google Scholar
3. 1. Alvarez I
   2. Smittenaar R
   3. Handley SE
   4. Liasis A
   5. Sereno MI
   6. Schwarzkopf DS
   7. Clark CA

   (2020) Altered visual population receptive fields in human albinism

   Cortex 128:107–123.

   https://doi.org/10.1016/j.cortex.2020.03.016

   • Google Scholar
4. 1. Amano K
   2. Wandell BA
   3. Dumoulin SO

   (2009) Visual Field Maps, Population Receptive Field Sizes, and Visual Field Coverage in the Human MT+ Complex

   Journal of Neurophysiology 102:2704–2718.

   https://doi.org/10.1152/jn.00102.2009

   • PubMed
   • Google Scholar
5. 1. Amiez C
   2. Joseph JP
   3. Procyk E

   (2006) Reward Encoding in the Monkey Anterior Cingulate Cortex

   Cerebral Cortex 16:1040–1055.

   https://doi.org/10.1093/cercor/bhj046

   • PubMed
   • Google Scholar
6. 1. Arcaro MJ
   2. Pinsk MA
   3. Li X
   4. Kastner S

   (2011) Visuotopic Organization of Macaque Posterior Parietal Cortex: A Functional Magnetic Resonance Imaging Study

   The Journal of Neuroscience 31:2064–2078.

   https://doi.org/10.1523/JNEUROSCI.3334-10.2011

   • PubMed
   • Google Scholar
7. 1. Arcaro MJ
   2. Livingstone MS

   (2017) Retinotopic Organization of Scene Areas in Macaque Inferior Temporal Cortex

   The Journal of Neuroscience 37:7373–7389.

   https://doi.org/10.1523/JNEUROSCI.0569-17.2017

   • PubMed
   • Google Scholar
8. 1. Arsenault JT
   2. Caspari N
   3. Vandenberghe R
   4. Vanduffel W

   (2018) Attention Shifts Recruit the Monkey Default Mode Network

   The Journal of Neuroscience 38:1202–1217.

   https://doi.org/10.1523/JNEUROSCI.1111-17.2017

   • PubMed
   • Google Scholar
9. 1. Arthurs OJ
   2. Boniface S

   (2002) How well do we understand the neural origins of the fMRI BOLD signal?

   Trends in Neurosciences 25:27–31.

   https://doi.org/10.1016/s0166-2236(00)01995-0

   • PubMed
   • Google Scholar
10. 1. Barlow HB
    2. Levick WR
    3. Westheimer G

    (1966) Computer-plotted receptive fields

    Science 154:920.

    https://doi.org/10.1126/science.154.3751.920-a

    • PubMed
    • Google Scholar
11. 1. Bartels A
    2. Logothetis NK
    3. Moutoussis K

    (2008) fMRI and its interpretations: an illustration on directional selectivity in area V5/MT

    Trends in Neurosciences 31:444–453.

    https://doi.org/10.1016/j.tins.2008.06.004

    • PubMed
    • Google Scholar
12. 1. Bartolo MJ
    2. Gieselmann MA
    3. Vuksanovic V
    4. Hunter D
    5. Sun L
    6. Chen X
    7. Delicato LS
    8. Thiele A

    (2011) Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the resonance blood oxygen level-dependent signal in macaque primary visual cortex

    The European Journal of Neuroscience 34:1857–1870.

    https://doi.org/10.1111/j.1460-9568.2011.07877.x

    • PubMed
    • Google Scholar
13. 1. Berens P
    2. Keliris GA
    3. Ecker AS
    4. Logothetis NK
    5. Tolias AS

    (2008) Feature selectivity of the gamma-band of the local field potential in primate primary visual cortex

    Frontiers in Neuroscience 2:199–207.

    https://doi.org/10.3389/neuro.01.037.2008

    • PubMed
    • Google Scholar
14. 1. Binda P
    2. Kurzawski JW
    3. Lunghi C
    4. Biagi L
    5. Tosetti M
    6. Morrone MC

    (2018) Response to short-term deprivation of the human adult visual cortex measured with 7T BOLD

    eLife 7:e40014.

    https://doi.org/10.7554/eLife.40014

    • PubMed
    • Google Scholar
15. 1. Bokil H
    2. Andrews P
    3. Kulkarni JE
    4. Mehta S
    5. Mitra PP

    (2010) Chronux: A platform for analyzing neural signals

    Journal of Neuroscience Methods 192:146–151.

    https://doi.org/10.1016/j.jneumeth.2010.06.020

    • PubMed
    • Google Scholar
16. 1. Bonin V
    2. Histed MH
    3. Yurgenson S
    4. Reid RC

    (2011) Local diversity and fine-scale organization of receptive fields in mouse visual cortex

    Journal of Neuroscience 31:18506–18521.

    https://doi.org/10.1523/JNEUROSCI.2974-11.2011

    • Google Scholar
17. 1. Boynton GM

    (2011) Spikes, BOLD, Attention, and Awareness: A comparison of electrophysiological and fMRI signals in V1

    Journal of Vision 11:12.

    https://doi.org/10.1167/11.5.12

    • Google Scholar
18. 1. Brewer AA
    2. Press WA
    3. Logothetis NK
    4. Wandell BA

    (2002)

    Visual Areas in Macaque Cortex Measured Using Functional Magnetic Resonance Imaging

    The Journal of Neuroscience 22:10416–10426.

    • Google Scholar
19. 1. Britten KH
    2. Heuer HW

    (1999) Spatial summation in the receptive fields of MT neurons

    The Journal of Neuroscience 19:5074–5084.

    https://doi.org/10.1523/JNEUROSCI.19-12-05074.1999

    • PubMed
    • Google Scholar
20. 1. Burkhalter A
    2. Van Essen DC

    (1986)

    Processing of color, form and disparity information in visual areas VP and V2 of ventral extrastriate cortex in the macaque monkey

    The Journal of Neuroscience 6:2327–2351.

    • Google Scholar
21. 1. Buzsáki G
    2. Draguhn A

    (2004) Neuronal Oscillations in Cortical Networks

    Science 304:1926–1929.

    https://doi.org/10.1126/science.1099745

    • PubMed
    • Google Scholar
22. Book

    1. Buzsáki G

    (2006) Rhythms of the Brain

    Oxford University Press.

    https://doi.org/10.1093/acprof:oso/9780195301069.001.0001

    • Google Scholar
23. 1. Cavanaugh JR
    2. Bair W
    3. Movshon JA

    (2002) Nature and interaction of signals from the receptive field center and surround in macaque V1 neurons

    Journal of Neurophysiology 88:2530–2546.

    https://doi.org/10.1152/jn.00692.2001

    • PubMed
    • Google Scholar
24. 1. Chen X
    2. Possel JK
    3. Wacongne C
    4. van Ham AF
    5. Klink PC
    6. Roelfsema PR

    (2017) 3D printing and modelling of customized implants and surgical guides for non-human primates

    Journal of Neuroscience Methods 286:38–55.

    https://doi.org/10.1016/j.jneumeth.2017.05.013

    • PubMed
    • Google Scholar
25. 1. Chen X
    2. Wang F
    3. Fernandez E
    4. Roelfsema PR

    (2020) Shape perception via a high-channel-count neuroprosthesis in monkey visual cortex

    Science 370:1191–1196.

    https://doi.org/10.1126/science.abd7435

    • PubMed
    • Google Scholar
26. 1. Cohen MR
    2. Maunsell JHR

    (2009) Attention improves performance primarily by reducing interneuronal correlations

    Nature Neuroscience 12:1594–1600.

    https://doi.org/10.1038/nn.2439

    • PubMed
    • Google Scholar
27. 1. Cotton PL
    2. Smith AT

    (2007) Contralateral Visual Hemifield Representations in the Human Pulvinar Nucleus

    Journal of Neurophysiology 98:1600–1609.

    https://doi.org/10.1152/jn.00419.2007

    • PubMed
    • Google Scholar
28. 1. Cox RW

    (1996) AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages

    Computers and Biomedical Research, an International Journal 29:162–173.

    https://doi.org/10.1006/cbmr.1996.0014

    • PubMed
    • Google Scholar
29. 1. de Best PB
    2. Raz N
    3. Guy N
    4. Ben-Hur T
    5. Dumoulin SO
    6. Pertzov Y
    7. Levin N

    (2019) Role of Population Receptive Field Size in Complex Visual Dysfunctions: A Posterior Cortical Atrophy Model

    JAMA Neurology 76:1391–1396.

    https://doi.org/10.1001/jamaneurol.2019.2447

    • PubMed
    • Google Scholar
30. 1. Dekker TM
    2. Schwarzkopf DS
    3. de Haas B
    4. Nardini M
    5. Sereno MI

    (2019) Population receptive field tuning properties of visual cortex during childhood

    Developmental Cognitive Neuroscience 37:100614.

    https://doi.org/10.1016/j.dcn.2019.01.001

    • PubMed
    • Google Scholar
31. 1. DeSimone K
    2. Viviano JD
    3. Schneider KA

    (2015) Population Receptive Field Estimation Reveals New Retinotopic Maps in Human Subcortex

    The Journal of Neuroscience 35:9836–9847.

    https://doi.org/10.1523/JNEUROSCI.3840-14.2015

    • PubMed
    • Google Scholar
32. 1. Drew PJ

    (2019) Vascular and neural basis of the BOLD signal

    Current Opinion in Neurobiology 58:61–69.

    https://doi.org/10.1016/j.conb.2019.06.004

    • PubMed
    • Google Scholar
33. 1. Dubey A
    2. Ray S

    (2016) Spatial spread of local field potential is band-pass in the primary visual cortex

    Journal of Neurophysiology 116:1986–1999.

    https://doi.org/10.1152/jn.00443.2016

    • PubMed
    • Google Scholar
34. 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
35. 1. Dumoulin SO
    2. Knapen T

    (2018) How Visual Cortical Organization Is Altered by Ophthalmologic and Neurologic Disorders

    Annual Review of Vision Science 4:357–379.

    https://doi.org/10.1146/annurev-vision-091517-033948

    • PubMed
    • Google Scholar
36. 1. Einevoll GT
    2. Kayser C
    3. Logothetis NK
    4. Panzeri S

    (2013) Modelling and analysis of local field potentials for studying the function of cortical circuits

    Nature Reviews. Neuroscience 14:770–785.

    https://doi.org/10.1038/nrn3599

    • PubMed
    • Google Scholar
37. 1. Ekman M
    2. Roelfsema PR
    3. de LF

    (2020) Object selection by automatic spreading of top-down attentional signals in V1

    The Journal of Neuroscience 40:9250–9259.

    https://doi.org/10.1523/jneurosci.0438-20.2020

    • Google Scholar
38. 1. Ekstrom LB
    2. Roelfsema PR
    3. Arsenault JT
    4. Bonmassar G
    5. Vanduffel W

    (2008) Bottom-Up Dependent Gating of Frontal Signals in Early Visual Cortex

    Science 321:414–417.

    https://doi.org/10.1126/science.1153276

    • PubMed
    • Google Scholar
39. 1. Ekstrom A

    (2010) How and when the fMRI BOLD signal relates to underlying neural activity: The danger in dissociation

    Brain Research Reviews 62:233–244.

    https://doi.org/10.1016/j.brainresrev.2009.12.004

    • PubMed
    • Google Scholar
40. 1. Engel SA
    2. Rumelhart DE
    3. Wandell BA
    4. Lee AT
    5. Glover GH
    6. Chichilnisky EJ
    7. Shadlen MN

    (1994) fMRI of human visual cortex

    Nature 369:525.

    https://doi.org/10.1038/369525a0

    • PubMed
    • Google Scholar
41. 1. Engel SA

    (2012) The development and use of phase-encoded functional MRI designs

    NeuroImage 62:1195–1200.

    https://doi.org/10.1016/j.neuroimage.2011.09.059

    • PubMed
    • Google Scholar
42. 1. Erwin E
    2. Baker FH
    3. Busen WF
    4. Malpeli JG

    (1999) Relationship between laminar topology and retinotopy in the rhesus lateral geniculate nucleus: Results from a functional atlas

    The Journal of Comparative Neurology 407:92–102.

    https://doi.org/10.1002/(SICI)1096-9861(19990428)407:13.0.CO;2-1

    • PubMed
    • Google Scholar
43. 1. Felleman DJ
    2. Van Essen DC

    (1987) Receptive field properties of neurons in area V3 of macaque monkey extrastriate cortex

    Journal of Neurophysiology 57:889–920.

    https://doi.org/10.1152/jn.1987.57.4.889

    • PubMed
    • Google Scholar
44. 1. Fischl B

    (2012) FreeSurfer

    NeuroImage 62:774–781.

    https://doi.org/10.1016/j.neuroimage.2012.01.021

    • PubMed
    • Google Scholar
45. 1. Fouragnan EF
    2. Chau BKH
    3. Folloni D
    4. Kolling N
    5. Verhagen L
    6. Klein-Flügge M
    7. Tankelevitch L
    8. Papageorgiou GK
    9. Aubry J-F
    10. Sallet J
    11. Rushworth MFS

    (2019) The macaque anterior cingulate cortex translates counterfactual choice value into actual behavioral change

    Nature Neuroscience 22:797–808.

    https://doi.org/10.1038/s41593-019-0375-6

    • Google Scholar
46. 1. Fox AS
    2. Holley D
    3. Klink PC
    4. Arbuckle SA
    5. Barnes CA
    6. Diedrichsen J
    7. Kwok SC
    8. Kyle C
    9. Pruszynski JA
    10. Seidlitz J
    11. Zhou X
    12. Poldrack RA
    13. Gorgolewski KJ

    (2021) Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault

    NeuroImage 225:117518.

    https://doi.org/10.1016/j.neuroimage.2020.117518

    • PubMed
    • Google Scholar
47. 1. Gao JS
    2. Huth AG
    3. Lescroart MD
    4. Gallant JL

    (2015) Pycortex: an interactive surface visualizer for fMRI

    Frontiers in Neuroinformatics 9:23.

    https://doi.org/10.3389/fninf.2015.00023

    • Google Scholar
48. 1. Gattass R
    2. Gross CG
    3. Sandell JH

    (1981) Visual topography of V2 in the macaque

    The Journal of Comparative Neurology 201:519–539.

    https://doi.org/10.1002/cne.902010405

    • PubMed
    • Google Scholar
49. 1. Gattass R
    2. Sousa AP
    3. Rosa MG

    (1987) Visual topography of V1 in the Cebus monkey

    The Journal of Comparative Neurology 259:529–548.

    https://doi.org/10.1002/cne.902590404

    • PubMed
    • Google Scholar
50. 1. Gattass R
    2. Sousa AP
    3. Gross CG

    (1988) Visuotopic organization and extent of V3 and V4 of the macaque

    The Journal of Neuroscience 8:1831–1845.

    https://doi.org/10.1523/JNEUROSCI.08-06-01831.1988

    • Google Scholar
51. 1. Gattass R
    2. Nascimento-Silva S
    3. Soares JGM
    4. Lima B
    5. Jansen AK
    6. Diogo ACM
    7. Farias MF
    8. Botelho M
    9. Mariani OS
    10. Azzi J
    11. Fiorani M

    (2005) Cortical visual areas in monkeys: location, topography, connections, columns, plasticity and cortical dynamics

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 360:709–731.

    https://doi.org/10.1098/rstb.2005.1629

    • PubMed
    • Google Scholar
52. 1. Goense JBM
    2. Logothetis NK

    (2008) Neurophysiology of the BOLD fMRI signal in awake monkeys

    Current Biology 18:631–640.

    https://doi.org/10.1016/j.cub.2008.03.054

    • PubMed
    • Google Scholar
53. 1. Gorgolewski KJ
    2. Varoquaux G
    3. Rivera G
    4. Schwarz Y
    5. Ghosh SS
    6. Maumet C
    7. Sochat VV
    8. Nichols TE
    9. Poldrack RA
    10. Poline JB
    11. Yarkoni T
    12. Margulies DS

    (2015) NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

    Frontiers in Neuroinformatics 9:8.

    https://doi.org/10.3389/fninf.2015.00008

    • Google Scholar
54. 1. Green T
    2. Hosseini H
    3. Piccirilli A
    4. Ishak A
    5. Grill-Spector K
    6. Reiss AL

    (2019) X-Chromosome Insufficiency Alters Receptive Fields across the Human Early Visual Cortex

    The Journal of Neuroscience 39:8079–8088.

    https://doi.org/10.1523/JNEUROSCI.2745-18.2019

    • Google Scholar
55. 1. Greene CA
    2. Dumoulin SO
    3. Harvey BM
    4. Ress D

    (2014) Measurement of population receptive fields in human early visual cortex using back-projection tomography

    Journal of Vision 14:17.

    https://doi.org/10.1167/14.1.17

    • PubMed
    • Google Scholar
56. 1. Griffis JC
    2. Elkhetali AS
    3. Burge WK
    4. Chen RH
    5. Bowman AD
    6. Szaflarski JP
    7. Visscher KM

    (2017) Retinotopic patterns of functional connectivity between V1 and large-scale brain networks during resting fixation

    NeuroImage 146:1071–1083.

    https://doi.org/10.1016/j.neuroimage.2016.08.035

    • PubMed
    • Google Scholar
57. 1. Griffiths BJ
    2. Mayhew SD
    3. Mullinger KJ
    4. Jorge J
    5. Charest I
    6. Wimber M
    7. Hanslmayr S

    (2019) Alpha/beta power decreases track the fidelity of stimulus-specific information

    eLife 8:e49562.

    https://doi.org/10.7554/eLife.49562

    • Google Scholar
58. 1. Haak KV
    2. Winawer J
    3. Harvey BM
    4. Renken R
    5. Dumoulin SO
    6. Wandell BA
    7. Cornelissen FW

    (2013) Connective field modeling

    NeuroImage 66:376–384.

    https://doi.org/10.1016/j.neuroimage.2012.10.037

    • PubMed
    • Google Scholar
59. 1. Hartline HK

    (1938) The response of single optic nerve fibers of the vertebrate eye to illumination of the retina

    American Journal of Physiology-Legacy Content 121:400–415.

    https://doi.org/10.1152/ajplegacy.1938.121.2.400

    • Google Scholar
60. 1. Harvey BM
    2. Vansteensel MJ
    3. Ferrier CH
    4. Petridou N
    5. Zuiderbaan W
    6. Aarnoutse EJ
    7. Bleichner MG
    8. Dijkerman HC
    9. van Zandvoort MJE
    10. Leijten FSS
    11. Ramsey NF
    12. Dumoulin SO

    (2013) Frequency specific spatial interactions in human electrocorticography: V1 alpha oscillations reflect surround suppression

    NeuroImage 65:424–432.

    https://doi.org/10.1016/j.neuroimage.2012.10.020

    • PubMed
    • Google Scholar
61. 1. Harvey BM
    2. Fracasso A
    3. Petridou N
    4. Dumoulin SO

    (2015) Topographic representations of object size and relationships with numerosity reveal generalized quantity processing in human parietal cortex

    PNAS 112:13525–13530.

    https://doi.org/10.1073/pnas.1515414112

    • Google Scholar
62. 1. Harvey BM
    2. Dumoulin SO
    3. Fracasso A
    4. Paul JM

    (2020) A Network of Topographic Maps in Human Association Cortex Hierarchically Transforms Visual Timing-Selective Responses

    Current Biology 30:1424–1434.

    https://doi.org/10.1016/j.cub.2020.01.090

    • PubMed
    • Google Scholar
63. 1. He D
    2. Wang Y
    3. Fang F

    (2019) The Critical Role of V2 Population Receptive Fields in Visual Orientation Crowding

    Current Biology 29:2229–2236.

    https://doi.org/10.1016/j.cub.2019.05.068

    • PubMed
    • Google Scholar
64. 1. Hikosaka O
    2. Sakamoto M
    3. Usui S

    (1989a) Functional properties of monkey caudate neurons. II. Visual and auditory responses

    Journal of Neurophysiology 61:799–813.

    https://doi.org/10.1152/jn.1989.61.4.799

    • Google Scholar
65. 1. Hikosaka O
    2. Sakamoto M
    3. Usui S

    (1989b) Functional properties of monkey caudate neurons. III. Activities related to expectation of target and reward

    Journal of Neurophysiology 61:814–832.

    https://doi.org/10.1152/jn.1989.61.4.814

    • Google Scholar
66. 1. Hubel DH
    2. Wiesel TN

    (1959) Receptive fields of single neurones in the cat’s striate cortex

    The Journal of Physiology 148:574–591.

    https://doi.org/10.1113/jphysiol.1959.sp006308

    • PubMed
    • Google Scholar
67. 1. Hubel DH
    2. Wiesel TN

    (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

    The Journal of Physiology 160:106–154.

    https://doi.org/10.1113/jphysiol.1962.sp006837

    • PubMed
    • Google Scholar
68. 1. Hubel DH
    2. Wiesel TN

    (1968) Receptive fields and functional architecture of monkey striate cortex

    The Journal of Physiology 195:215–243.

    https://doi.org/10.1113/jphysiol.1968.sp008455

    • PubMed
    • Google Scholar
69. 1. Hubel DH
    2. Wiesel TN

    (1974) Uniformity of monkey striate cortex: a parallel relationship between field size, scatter, and magnification factor

    The Journal of Comparative Neurology 158:295–305.

    https://doi.org/10.1002/cne.901580305

    • PubMed
    • Google Scholar
70. 1. Hubel DH
    2. Wiesel TN

    (1998) Early exploration of the visual cortex

    Neuron 20:401–412.

    https://doi.org/10.1016/S0896-6273(00)80984-8

    • PubMed
    • Google Scholar
71. 1. Hughes AE
    2. Greenwood JA
    3. Finlayson NJ
    4. Schwarzkopf DS

    (2019) Population receptive field estimates for motion-defined stimuli

    NeuroImage 199:245–260.

    https://doi.org/10.1016/j.neuroimage.2019.05.068

    • PubMed
    • Google Scholar
72. 1. Janssens T
    2. Zhu Q
    3. Popivanov ID
    4. Vanduffel W

    (2014) Probabilistic and Single-Subject Retinotopic Maps Reveal the Topographic Organization of Face Patches in the Macaque Cortex

    The Journal of Neuroscience 34:10156–10167.

    https://doi.org/10.1523/JNEUROSCI.2914-13.2013

    • PubMed
    • Google Scholar
73. 1. Jenkinson M
    2. Bannister P
    3. Brady M
    4. Smith S

    (2002) Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

    NeuroImage 17:825–841.

    https://doi.org/10.1016/s1053-8119(02)91132-8

    • PubMed
    • Google Scholar
74. 1. Kajikawa Y
    2. Schroeder CE

    (2011) How local is the local field potential?

    Neuron 72:847–858.

    https://doi.org/10.1016/j.neuron.2011.09.029

    • PubMed
    • Google Scholar
75. 1. Katzner S
    2. Nauhaus I
    3. Benucci A
    4. Bonin V
    5. Ringach DL
    6. Carandini M

    (2009) Local origin of field potentials in visual cortex

    Neuron 61:35–41.

    https://doi.org/10.1016/j.neuron.2008.11.016

    • PubMed
    • Google Scholar
76. 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
77. 1. Keliris GA
    2. Li Q
    3. Papanikolaou A
    4. Logothetis NK
    5. Smirnakis SM

    (2019) Estimating average single-neuron visual receptive field sizes by fMRI

    PNAS 116:6425–6434.

    https://doi.org/10.1073/pnas.1809612116

    • Google Scholar
78. 1. Klink PC
    2. Dagnino B
    3. Gariel-Mathis MA
    4. Roelfsema PR

    (2017) Distinct Feedforward and Feedback Effects of Microstimulation in Visual Cortex Reveal Neural Mechanisms of Texture Segregation

    Neuron 95:209–220.

    https://doi.org/10.1016/j.neuron.2017.05.033

    • Google Scholar
79. Software

    1. Klink PC

    (2021) NHP_Freesurfer, version swh:1:rev:8d4b89337b865fb194e196cf1b2af4967e14d607

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:d8a1a76a4f91921900b4f0ede328f5acdeb03c37;origin=https://github.com/VisionandCognition/NHP-Freesurfer;visit=swh:1:snp:7b5338bc3ee68190df2c473e45629d3ce2640840;anchor=swh:1:rev:8d4b89337b865fb194e196cf1b2af4967e14d607
80. 1. Knierim JJ
    2. van Essen DC

    (1992) Neuronal responses to static texture patterns in area V1 of the alert macaque monkey

    Journal of Neurophysiology 67:961–980.

    https://doi.org/10.1152/jn.1992.67.4.961

    • PubMed
    • Google Scholar
81. 1. Kolster H
    2. Mandeville JB
    3. Arsenault JT
    4. Ekstrom LB
    5. Wald LL
    6. Vanduffel W

    (2009) Visual field map clusters in macaque extrastriate visual cortex

    The Journal of Neuroscience 29:7031–7039.

    https://doi.org/10.1523/JNEUROSCI.0518-09.2009

    • PubMed
    • Google Scholar
82. 1. Kolster H
    2. Peeters R
    3. Orban GA

    (2010) The retinotopic organization of the human middle temporal area MT/V5 and its cortical neighbors

    The Journal of Neuroscience 30:9801–9820.

    https://doi.org/10.1523/JNEUROSCI.2069-10.2010

    • PubMed
    • Google Scholar
83. 1. Kolster H
    2. Janssens T
    3. Orban GA
    4. Vanduffel W

    (2014) The retinotopic organization of macaque occipitotemporal cortex anterior to V4 and caudoventral to the middle temporal (MT) cluster

    The Journal of Neuroscience 34:10168–10191.

    https://doi.org/10.1523/JNEUROSCI.3288-13.2014

    • PubMed
    • Google Scholar
84. 1. Kreiman G
    2. Hung CP
    3. Kraskov A
    4. Quiroga RQ
    5. Poggio T
    6. DiCarlo JJ

    (2006) Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex

    Neuron 49:433–445.

    https://doi.org/10.1016/j.neuron.2005.12.019

    • PubMed
    • Google Scholar
85. 1. Larsson J
    2. Heeger DJ

    (2006) Two retinotopic visual areas in human lateral occipital cortex

    The Journal of Neuroscience 26:13128–13142.

    https://doi.org/10.1523/JNEUROSCI.1657-06.2006

    • PubMed
    • Google Scholar
86. 1. Lerma-Usabiaga G
    2. Winawer J
    3. Wandell BA

    (2021) Population Receptive Field Shapes in Early Visual Cortex Are Nearly Circular

    The Journal of Neuroscience 41:2420–2427.

    https://doi.org/10.1523/JNEUROSCI.3052-20.2021

    • PubMed
    • Google Scholar
87. 1. Leski S
    2. Lindén H
    3. Tetzlaff T
    4. Pettersen KH
    5. Einevoll GT

    (2013) Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential

    PLOS Computational Biology 9:e1003137.

    https://doi.org/10.1371/journal.pcbi.1003137

    • PubMed
    • Google Scholar
88. 1. Li X
    2. Morgan PS
    3. Ashburner J
    4. Smith J
    5. Rorden C

    (2016) The first step for neuroimaging data analysis: DICOM to NIfTI conversion

    Journal of Neuroscience Methods 264:47–56.

    https://doi.org/10.1016/j.jneumeth.2016.03.001

    • PubMed
    • Google Scholar
89. 1. Lima B
    2. Cardoso MMB
    3. Sirotin YB
    4. Das A

    (2014) Stimulus-Related Neuroimaging in Task-Engaged Subjects Is Best Predicted by Concurrent Spiking

    The Journal of Neuroscience 34:13878–13891.

    https://doi.org/10.1523/JNEUROSCI.1595-14.2014

    • PubMed
    • Google Scholar
90. 1. Logothetis NK
    2. Pauls J
    3. Augath MA
    4. Trinath T
    5. Oeltermann A

    (2001) Neurophysiological investigation of the basis of the fMRI signal

    Nature 412:150–157.

    https://doi.org/10.1038/35084005

    • PubMed
    • Google Scholar
91. 1. Logothetis NK

    (2003) The underpinnings of the BOLD functional magnetic resonance imaging signal

    The Journal of Neuroscience 23:3963–3971.

    https://doi.org/10.1523/JNEUROSCI.23-10-03963.2003

    • PubMed
    • Google Scholar
92. 1. Logothetis NK
    2. Wandell BA

    (2004) Interpreting the BOLD Signal

    Annual Review of Physiology 66:735–769.

    https://doi.org/10.1146/annurev.physiol.66.082602.092845

    • PubMed
    • Google Scholar
93. 1. Logothetis NK

    (2010) Neurovascular uncoupling: Much ado about nothing

    Frontiers in Neuroenergetics 2:2.

    https://doi.org/10.3389/fnene.2010.00002

    • PubMed
    • Google Scholar
94. 1. Maier A
    2. Wilke M
    3. Aura C
    4. Zhu C
    5. Ye FQ
    6. Leopold DA

    (2008) Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey

    Nature Neuroscience 11:1193–1200.

    https://doi.org/10.1038/nn.2173

    • PubMed
    • Google Scholar
95. 1. Mantini D
    2. Gerits A
    3. Nelissen K
    4. Durand JB
    5. Joly O
    6. Simone L
    7. Sawamura H
    8. Wardak C
    9. Orban GA
    10. Buckner RL
    11. Vanduffel W

    (2011) Default mode of brain function in monkeys

    The Journal of Neuroscience 31:12954–12962.

    https://doi.org/10.1523/JNEUROSCI.2318-11.2011

    • PubMed
    • Google Scholar
96. 1. Merkel C
    2. Hopf JM
    3. Schoenfeld MA

    (2018) Spatial elongation of population receptive field profiles revealed by model-free fMRI back-projection

    Human Brain Mapping 39:2472–2481.

    https://doi.org/10.1002/hbm.24015

    • PubMed
    • Google Scholar
97. 1. Messinger A
    2. Sirmpilatze N
    3. Heuer K
    4. Loh KK
    5. Mars RB
    6. Sein J
    7. Xu T
    8. Glen D
    9. Jung B
    10. Seidlitz J
    11. Taylor P
    12. Toro R
    13. Garza-Villarreal EA
    14. Sponheim C
    15. Wang X
    16. Benn RA
    17. Cagna B
    18. Dadarwal R
    19. Evrard HC
    20. Garcia-Saldivar P
    21. Giavasis S
    22. Hartig R
    23. Lepage C
    24. Liu C
    25. Majka P
    26. Merchant H
    27. Milham MP
    28. Rosa MGP
    29. Tasserie J
    30. Uhrig L
    31. Margulies DS
    32. Klink PC

    (2021) A collaborative resource platform for non-human primate neuroimaging

    NeuroImage 226:117519.

    https://doi.org/10.1016/j.neuroimage.2020.117519

    • PubMed
    • Google Scholar
98. 1. Mo C
    2. He D
    3. Fang F

    (2018) Attention priority map of face images in human early visual cortex

    The Journal of Neuroscience 38:149–157.

    https://doi.org/10.1523/JNEUROSCI.1206-17.2017

    • Google Scholar
99. 1. Mukamel R
    2. Gelbard H
    3. Arieli A
    4. Hasson U
    5. Fried I
    6. Malach R

    (2005) Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex

    Science 309:951–954.

    https://doi.org/10.1126/science.1110913

    • PubMed
    • Google Scholar
100. 1. Nelson MJ
     2. Pouget P

     (2010) Do electrode properties create a problem in interpreting local field potential recordings?

     Journal of Neurophysiology 103:2315–2317.

     https://doi.org/10.1152/jn.00157.2010

     • PubMed
     • Google Scholar
101. 1. Newsome WT
     2. Maunsell JH
     3. Van Essen DC

     (1986) Ventral posterior visual area of the macaque: visual topography and areal boundaries

     The Journal of Comparative Neurology 252:139–153.

     https://doi.org/10.1002/cne.902520202

     • PubMed
     • Google Scholar
102. 1. Niessing J
     2. Ebisch B
     3. Schmidt KE
     4. Niessing M
     5. Singer W
     6. Galuske RAW

     (2005) Hemodynamic Signals Correlate Tightly with Synchronized Gamma Oscillations

     Science 309:948–951.

     https://doi.org/10.1126/science.1110948

     • PubMed
     • Google Scholar
103. 1. Nir Y
     2. Fisch L
     3. Mukamel R
     4. Gelbard-Sagiv H
     5. Arieli A
     6. Fried I
     7. Malach R

     (2007) Coupling between Neuronal Firing Rate, Gamma LFP, and BOLD fMRI Is Related to Interneuronal Correlations

     Current Biology 17:1275–1285.

     https://doi.org/10.1016/j.cub.2007.06.066

     • PubMed
     • Google Scholar
104. 1. Oleksiak A
     2. Klink PC
     3. Postma A
     4. van der Ham IJM
     5. Lankheet MJ
     6. van Wezel RJA

     (2011) Spatial summation in macaque parietal area 7a follows a winner-take-all rule

     Journal of Neurophysiology 105:1150–1158.

     https://doi.org/10.1152/jn.00907.2010

     • PubMed
     • Google Scholar
105. 1. Palmer C
     2. Cheng S-Y
     3. Seidemann E

     (2007) Linking neuronal and behavioral performance in a reaction-time visual detection task

     The Journal of Neuroscience 27:8122–8137.

     https://doi.org/10.1523/JNEUROSCI.1940-07.2007

     • PubMed
     • Google Scholar
106. Book

     1. Papageorgiou TD
     2. Christopoulos GI
     3. Smirnakis SM

     (2014) Advanced Brain Neuroimaging Topics in Health and Disease

     Methods and Applications.

     https://doi.org/10.5772/58256

     • Google Scholar
107. 1. Patel GH
     2. Shulman GL
     3. Baker JT
     4. Akbudak E
     5. Snyder AZ
     6. Snyder LH
     7. Corbetta M

     (2010) Topographic organization of macaque area LIP

     PNAS 107:4728–4733.

     https://doi.org/10.1073/pnas.0908092107

     • PubMed
     • Google Scholar
108. 1. Poltoratski S
     2. Maier A
     3. Newton AT
     4. Tong F

     (2019) Figure-Ground Modulation in the Human Lateral Geniculate Nucleus Is Distinguishable from Top-Down Attention

     Current Biology 29:2051–2057.

     https://doi.org/10.1016/j.cub.2019.04.068

     • Google Scholar
109. 1. Poltoratski S
     2. Tong F

     (2020) Resolving the spatial profile of figure enhancement in human V1 through population receptive field modeling

     The Journal of Neuroscience 40:3292–3303.

     https://doi.org/10.1523/JNEUROSCI.2377-19.2020

     • Google Scholar
110. 1. Poort J
     2. Raudies F
     3. Wannig A
     4. Lamme VAF
     5. Neumann H
     6. Roelfsema PR

     (2012) The role of attention in figure-ground segregation in areas v1 and v4 of the visual cortex

     Neuron 75:143–156.

     https://doi.org/10.1016/j.neuron.2012.04.032

     • PubMed
     • Google Scholar
111. 1. Priebe NJ

     (2008) The relationship between subthreshold and suprathreshold ocular dominance in cat primary visual cortex

     The Journal of Neuroscience 28:8553–8559.

     https://doi.org/10.1523/JNEUROSCI.2182-08.2008

     • PubMed
     • Google Scholar
112. 1. Puckett AM
     2. Bollmann S
     3. Junday K
     4. Barth M
     5. Cunnington R

     (2020) Bayesian population receptive field modeling in human somatosensory cortex

     NeuroImage 208:116465.

     https://doi.org/10.1016/j.neuroimage.2019.116465

     • PubMed
     • Google Scholar
113. 1. Rees G
     2. Friston K
     3. Koch C

     (2000) A direct quantitative relationship between the functional properties of human and macaque V5

     Nature Neuroscience 3:716–723.

     https://doi.org/10.1038/76673

     • PubMed
     • Google Scholar
114. 1. Reveley C
     2. Gruslys A
     3. Ye FQ
     4. Glen D
     5. Samaha J
     6. E Russ B
     7. Saad Z
     8. K Seth A
     9. Leopold DA
     10. Saleem KS

     (2017) Three-Dimensional Digital Template Atlas of the Macaque Brain

     Cerebral Cortex 27:4463–4477.

     https://doi.org/10.1093/cercor/bhw248

     • PubMed
     • Google Scholar
115. 1. Rima S
     2. Cottereau BR
     3. Héjja-Brichard Y
     4. Trotter Y
     5. Durand JB

     (2020) Wide-field retinotopy reveals a new visuotopic cluster in macaque posterior parietal cortex

     Brain Structure & Function 225:2447–2461.

     https://doi.org/10.1007/s00429-020-02134-2

     • PubMed
     • Google Scholar
116. 1. Rolls ET
     2. Thorpe SJ
     3. Maddison SP

     (1983) Responses of striatal neurons in the behaving monkey. 1. Head of the caudate nucleus

     Behavioural Brain Research 7:179–210.

     https://doi.org/10.1016/0166-4328(83)90191-2

     • Google Scholar
117. 1. Rosa MGP
     2. Sousa APB
     3. Gattass R

     (1988) Representation of the visual field in the second visual area in the Cebus monkey

     The Journal of Comparative Neurology 275:326–345.

     https://doi.org/10.1002/cne.902750303

     • PubMed
     • Google Scholar
118. 1. Rosa MGP
     2. Piñon MC
     3. Gattass R
     4. Sousa APB

     (2000) “Third tier” ventral extrastriate cortex in the New World monkey, Cebus apella

     Experimental Brain Research 132:287–305.

     https://doi.org/10.1007/s002210000344

     • PubMed
     • Google Scholar
119. 1. Rousche PJ
     2. Normann RA

     (1998) Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex

     Journal of Neuroscience Methods 82:1–15.

     https://doi.org/10.1016/s0165-0270(98)00031-4

     • PubMed
     • Google Scholar
120. 1. Saygin AP
     2. Sereno MI

     (2008) Retinotopy and Attention in Human Occipital, Temporal, Parietal, and Frontal Cortex

     Cerebral Cortex 18:2158–2168.

     https://doi.org/10.1093/cercor/bhm242

     • PubMed
     • Google Scholar
121. 1. Schneider KA
     2. Richter MC
     3. Kastner S

     (2004) Retinotopic Organization and Functional Subdivisions of the Human Lateral Geniculate Nucleus: A High-Resolution Functional Magnetic Resonance Imaging Study

     The Journal of Neuroscience 24:8975–8985.

     https://doi.org/10.1523/JNEUROSCI.2413-04.2004

     • PubMed
     • Google Scholar
122. 1. Schölvinck ML
     2. Maier A
     3. Ye FQ
     4. Duyn JH
     5. Leopold DA

     (2010) Neural basis of global resting-state fMRI activity

     PNAS 107:10238–10243.

     https://doi.org/10.1073/pnas.0913110107

     • PubMed
     • Google Scholar
123. 1. Schwarzkopf DS
     2. Anderson EJ
     3. de Haas B
     4. White SJ
     5. Rees G

     (2014) Larger Extrastriate Population Receptive Fields in Autism Spectrum Disorders

     The Journal of Neuroscience 34:2713–2724.

     https://doi.org/10.1523/JNEUROSCI.4416-13.2014

     • PubMed
     • Google Scholar
124. 1. Seidlitz J
     2. Sponheim C
     3. Glen D
     4. Ye FQ
     5. Saleem KS
     6. Leopold DA
     7. Ungerleider L
     8. Messinger A

     (2018) A population MRI brain template and analysis tools for the macaque

     NeuroImage 170:121–131.

     https://doi.org/10.1016/j.neuroimage.2017.04.063

     • PubMed
     • Google Scholar
125. 1. Sereno MI
     2. Dale AM
     3. Reppas JB
     4. Kwong KK
     5. Belliveau JW
     6. Brady TJ
     7. Rosen BR
     8. Tootell RB

     (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging

     Science 268:889–893.

     https://doi.org/10.1126/science.7754376

     • PubMed
     • Google Scholar
126. 1. Shao Y
     2. Keliris GA
     3. Papanikolaou A
     4. Fischer MD
     5. Zobor D
     6. Jägle H
     7. Logothetis NK
     8. Smirnakis SM

     (2013) Visual cortex organisation in a macaque monkey with macular degeneration

     The European Journal of Neuroscience 38:3456–3464.

     https://doi.org/10.1111/ejn.12349

     • PubMed
     • Google Scholar
127. 1. Shen L
     2. Han B
     3. de Lange FP

     (2020) Apparent motion induces activity suppression in early visual cortex and impairs visual detection

     The Journal of Neuroscience 40:5471–5479.

     https://doi.org/10.1523/JNEUROSCI.0563-20.2020

     • PubMed
     • Google Scholar
128. 1. Sherrington CS

     (1906) Observations on the scratch-reflex in the spinal dog

     The Journal of Physiology 34:1–50.

     https://doi.org/10.1113/jphysiol.1906.sp001139

     • PubMed
     • Google Scholar
129. 1. Shmuel A
     2. Augath M
     3. Oeltermann A
     4. Logothetis NK

     (2006) Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1

     Nature Neuroscience 9:569–577.

     https://doi.org/10.1038/nn1675

     • PubMed
     • Google Scholar
130. 1. Siegel M
     2. Donner TH
     3. Oostenveld R
     4. Fries P
     5. Engel AK

     (2008) Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention

     Neuron 60:709–719.

     https://doi.org/10.1016/j.neuron.2008.09.010

     • PubMed
     • Google Scholar
131. 1. Silson EH
     2. Reynolds RC
     3. Kravitz DJ
     4. Baker CI

     (2018) Differential Sampling of Visual Space in Ventral and Dorsal Early Visual Cortex

     The Journal of Neuroscience 38:2294–2303.

     https://doi.org/10.1523/JNEUROSCI.2717-17.2018

     • PubMed
     • Google Scholar
132. 1. Sirotin YB
     2. Das A

     (2009) Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity

     Nature 457:475–479.

     https://doi.org/10.1038/nature07664

     • PubMed
     • Google Scholar
133. 1. Smith AT
     2. Williams AL
     3. Singh KD

     (2004) Negative BOLD in the visual cortex: Evidence against blood stealing

     Human Brain Mapping 21:213–220.

     https://doi.org/10.1002/hbm.20017

     • PubMed
     • Google Scholar
134. 1. Stoll S
     2. Finlayson NJ
     3. Schwarzkopf DS

     (2020) Topographic signatures of global object perception in human visual cortex

     NeuroImage 220:116926.

     https://doi.org/10.1016/j.neuroimage.2020.116926

     • Google Scholar
135. 1. Supèr H
     2. Roelfsema PR

     (2005) Chronic multiunit recordings in behaving animals: advantages and limitations

     Progress in Brain Research 147:263–282.

     https://doi.org/10.1016/S0079-6123(04)47020-4

     • PubMed
     • Google Scholar
136. 1. Szinte M
     2. Knapen T

     (2020) Visual Organization of the Default Network

     Cerebral Cortex 30:3518–3527.

     https://doi.org/10.1093/cercor/bhz323

     • Google Scholar
137. 1. Tani N
     2. Joly O
     3. Iwamuro H
     4. Uhrig L
     5. Wiggins CJ
     6. Poupon C
     7. Kolster H
     8. Vanduffel W
     9. Le Bihan D
     10. Palfi S
     11. Jarraya B

     (2011) Direct visualization of non-human primate subcortical nuclei with contrast-enhanced high field MRI

     NeuroImage 58:60–68.

     https://doi.org/10.1016/j.neuroimage.2011.06.019

     • PubMed
     • Google Scholar
138. 1. Thomas JM
     2. Huber E
     3. Stecker GC
     4. Boynton GM
     5. Saenz M
     6. Fine I

     (2015) Population receptive field estimates of human auditory cortex

     NeuroImage 105:428–439.

     https://doi.org/10.1016/j.neuroimage.2014.10.060

     • PubMed
     • Google Scholar
139. 1. van Beest EH
     2. Mukherjee S
     3. Kirchberger L
     4. Schnabel UH
     5. van der Togt C
     6. Teeuwen RRM
     7. Barsegyan A
     8. Meyer AF
     9. Poort J
     10. Roelfsema PR
     11. Self MW

     (2021) Mouse visual cortex contains a region of enhanced spatial resolution

     Nature Communications 12:4029.

     https://doi.org/10.1038/s41467-021-24311-5

     • PubMed
     • Google Scholar
140. 1. van Es DM
     2. van der Zwaag W
     3. Knapen T

     (2019) Topographic Maps of Visual Space in the Human Cerebellum

     Current Biology 29:1689–1694.

     https://doi.org/10.1016/j.cub.2019.04.012

     • Google Scholar
141. 1. Van Essen DC
     2. Newsome WT
     3. Maunsell JHR

     (1984) The visual field representation in striate cortex of the macaque monkey: Asymmetries, anisotropies, and individual variability

     Vision Research 24:429–448.

     https://doi.org/10.1016/0042-6989(84)90041-5

     • PubMed
     • Google Scholar
142. 1. van Kerkoerle T
     2. Self MW
     3. Dagnino B
     4. Gariel-Mathis MA
     5. Poort J
     6. van der Togt C
     7. Roelfsema PR

     (2014) Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

     PNAS 111:14332–14341.

     https://doi.org/10.1073/pnas.1402773111

     • PubMed
     • Google Scholar
143. 1. Vanduffel W
     2. Fize D
     3. Mandeville JB
     4. Nelissen K
     5. Van Hecke P
     6. Rosen BR
     7. Tootell RB
     8. Orban GA

     (2001) Visual motion processing investigated using contrast agent-enhanced fMRI in awake behaving monkeys

     Neuron 32:565–577.

     https://doi.org/10.1016/S0896-6273(01)00502-5

     • PubMed
     • Google Scholar
144. 1. Victor JD
     2. Purpura K
     3. Katz E
     4. Mao B

     (1994) Population encoding of spatial frequency, orientation, and color in macaque V1

     Journal of Neurophysiology 72:2151–2166.

     https://doi.org/10.1152/jn.1994.72.5.2151

     • PubMed
     • Google Scholar
145. 1. Viswam V
     2. Obien MEJ
     3. Franke F
     4. Frey U
     5. Hierlemann A

     (2019) Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies

     Frontiers in Neuroscience 13:385.

     https://doi.org/10.3389/fnins.2019.00385

     • PubMed
     • Google Scholar
146. 1. Viswanathan A
     2. Freeman RD

     (2007) Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity

     Nature Neuroscience 10:1308–1312.

     https://doi.org/10.1038/nn1977

     • PubMed
     • Google Scholar
147. 1. Wandell BA
     2. Dumoulin SO
     3. Brewer AA

     (2007) Visual field maps in human cortex

     Neuron 56:366–383.

     https://doi.org/10.1016/j.neuron.2007.10.012

     • PubMed
     • Google Scholar
148. 1. Wandell BA
     2. Winawer J

     (2011) Imaging retinotopic maps in the human brain

     Vision Research 51:718–737.

     https://doi.org/10.1016/j.visres.2010.08.004

     • PubMed
     • Google Scholar
149. 1. Wandell BA
     2. Winawer J

     (2015) Computational neuroimaging and population receptive fields

     Trends in Cognitive Sciences 19:349–357.

     https://doi.org/10.1016/j.tics.2015.03.009

     • PubMed
     • Google Scholar
150. 1. Ward MP
     2. Rajdev P
     3. Ellison C
     4. Irazoqui PP

     (2009) Toward a comparison of microelectrodes for acute and chronic recordings

     Brain Research 1282:183–200.

     https://doi.org/10.1016/j.brainres.2009.05.052

     • PubMed
     • Google Scholar
151. 1. Welbourne LE
     2. Morland AB
     3. Wade AR

     (2018) Population receptive field (pRF) measurements of chromatic responses in human visual cortex using fMRI

     NeuroImage 167:84–94.

     https://doi.org/10.1016/j.neuroimage.2017.11.022

     • PubMed
     • Google Scholar
152. 1. Winawer J
     2. Kay KN
     3. Foster BL
     4. Rauschecker AM
     5. Parvizi J
     6. Wandell BA

     (2013) Asynchronous Broadband Signals Are the Principal Source of the BOLD Response in Human Visual Cortex

     Current Biology 23:1145–1153.

     https://doi.org/10.1016/j.cub.2013.05.001

     • PubMed
     • Google Scholar
153. 1. Winder AT
     2. Echagarruga C
     3. Zhang Q
     4. Drew PJ

     (2017) Weak correlations between hemodynamic signals and ongoing neural activity during the resting state

     Nature Neuroscience 20:1761–1769.

     https://doi.org/10.1038/s41593-017-0007-y

     • Google Scholar
154. 1. Womelsdorf T
     2. Fries P

     (2007) The role of neuronal synchronization in selective attention

     Current Opinion in Neurobiology 17:154–160.

     https://doi.org/10.1016/j.conb.2007.02.002

     • PubMed
     • Google Scholar
155. 1. Woolrich MW
     2. Behrens TEJ
     3. Smith SM

     (2004) Constrained linear basis sets for HRF modelling using Variational Bayes

     NeuroImage 21:1748–1761.

     https://doi.org/10.1016/j.neuroimage.2003.12.024

     • PubMed
     • Google Scholar
156. 1. Worden MS
     2. Foxe JJ
     3. Wang N
     4. Simpson GV

     (2000) Anticipatory Biasing of Visuospatial Attention Indexed by Retinotopically Specific α-Bank Electroencephalography Increases over Occipital Cortex

     The Journal of Neuroscience 20:RC63.

     https://doi.org/10.1523/JNEUROSCI.20-06-j0002.2000

     • Google Scholar
157. 1. Xing D
     2. Yeh CI
     3. Shapley RM

     (2009) Spatial spread of the local field potential and its laminar variation in visual cortex

     The Journal of Neuroscience 29:11540–11549.

     https://doi.org/10.1523/JNEUROSCI.2573-09.2009

     • PubMed
     • Google Scholar
158. 1. Yoshor D
     2. Bosking WH
     3. Ghose GM
     4. Maunsell JHR

     (2007) Receptive Fields in Human Visual Cortex Mapped with Surface Electrodes

     Cerebral Cortex 17:2293–2302.

     https://doi.org/10.1093/cercor/bhl138

     • PubMed
     • Google Scholar
159. 1. Zeidman P
     2. Silson EH
     3. Schwarzkopf DS
     4. Baker CI
     5. Penny W

     (2018) Bayesian population receptive field modelling

     NeuroImage 180:173–187.

     https://doi.org/10.1016/j.neuroimage.2017.09.008

     • Google Scholar
160. 1. Zhu Q
     2. Vanduffel W

     (2019) Submillimeter fMRI reveals a layout of dorsal visual cortex in macaques, remarkably similar to New World monkeys

     PNAS 116:2306–2311.

     https://doi.org/10.1073/pnas.1805561116

     • Google Scholar
161. 1. Zuiderbaan W
     2. Harvey BM
     3. Dumoulin SO

     (2012) Modeling center-surround configurations in population receptive fields using fMRI

     Journal of Vision 12:10.

     https://doi.org/10.1167/12.3.10

     • PubMed
     • Google Scholar
162. 1. Zuiderbaan W
     2. Harvey BM
     3. Dumoulin SO

     (2017) Image identification from brain activity using the population receptive field model

     PLOS ONE 12:e0183295.

     https://doi.org/10.1371/journal.pone.0183295

     • PubMed
     • Google Scholar

Author details

1. P Christiaan Klink

   1. Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands
   2. Psychiatry Department, Amsterdam UMC, Amsterdam, Netherlands

   Contribution

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

   For correspondence

   c.klink@nin.knaw.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6784-7842

2. Xing Chen

   Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands

   Contribution

   Formal analysis, Funding acquisition, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3589-1750

3. Wim Vanduffel

   1. Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, Belgium
   2. Massachusetts General Hospital, Martinos Ctr. for Biomedical Imaging, Charlestown, United States
   3. Leuven Brain Institute, KU Leuven, Leuven, Belgium
   4. Harvard Medical School, Boston, United States

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Pieter R Roelfsema

   1. Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands
   2. Psychiatry Department, Amsterdam UMC, Amsterdam, Netherlands
   3. Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands

   Contribution

   Conceptualization, Funding acquisition, Resources, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1625-0034

• 3,017

  views

• 414

  downloads

• 51

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Citations by DOI

• 51

  citations for umbrella DOI https://doi.org/10.7554/eLife.67304