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.

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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).

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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).

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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).

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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.

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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.

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Figure 7

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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).

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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

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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).

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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

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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).

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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.

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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

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