The relation between functional magnetic resonance imaging activations and single-cell selectivity in the macaque intraparietal sulcus.

NeuroImage 113 (2015) 86–100

Contents lists available at ScienceDirect

NeuroImage journal homepage: www.elsevier.com/locate/ynimg

The relation between functional magnetic resonance imaging activations and single-cell selectivity in the macaque intraparietal sulcus Ilse C.L. Van Dromme a, Wim Vanduffel a,b,c, Peter Janssen a,⁎ a b c

Laboratorium voor Neuro-en Psychofysiologie, KU Leuven, Belgium Athinoula A Martinos Ctr. Biomed Imaging, MGH, Charlestown, MA, USA Radiology, Harvard Univ., Med. Sch., Boston, MA, USA

a r t i c l e

i n f o

Article history: Received 31 October 2014 Accepted 10 March 2015 Available online 18 March 2015 Keywords: Disparity Anterior intraparietal cortex Electrophysiology Functional magnetic resonance imaging Depth structure

a b s t r a c t Previous functional magnetic resonance (fMRI) studies in humans and monkeys have demonstrated that the anterior intraparietal sulcus (IPS) is sensitive to the depth structure defined by binocular disparity. However, in the macaque monkey, a single large activation was measured in the anterior lateral bank of the IPS, whereas in human subjects two separate regions were sensitive to depth structure from disparity. We performed fMRI and single-cell experiments in the same animals, in a large number of recording sites in the lateral bank of the IPS. The fMRI interaction effect between the factors curvature (curved or flat) and disparity (stereo or control) correctly predicted the location of higher-order disparity selective neurons that encoded the depth structure of objects. However the large region in the IPS activated by depth structure consisted of two patches of higherorder disparity-selective neurons, one in the anterior IPS and one located more posteriorly, surrounded by regions lacking such selectivity. Thus the IPS region activated by curved surfaces consists of at least two patches of higher-order disparity selective neurons, which may reconcile previous fMRI studies in monkeys and humans. © 2015 Elsevier Inc. All rights reserved.

1. Introduction The primate brain computes the depth structure of objects in a large number of cortical areas in the temporal (Janssen et al., 2000), parietal (Srivastava et al., 2009; Orban et al., 2006) and frontal cortices (Theys et al., 2012b). Functional magnetic imaging (fMRI) studies in macaque monkeys have demonstrated that a large region in the anterior lateral bank of the intraparietal sulcus (IPS) is activated more strongly by disparity-defined curved surfaces than by flat surfaces at different disparities (Durand et al., 2007), which was therefore considered sensitive to the depth structure of objects (Fig. 1A). However, Georgieva et al. (2009) used the same stimuli and task design as those of Durand et al. (2007) in humans, but reported two distinct activations related to the depth structure in the anterior IPS (termed DIPSA and DIPSM, Fig. 1B), which questioned the homology between the human and the macaque anterior IPS. Subsequent electrophysiological recordings in monkeys using the stimuli of Durand et al. (2007) (Srivastava et al., 2009) reported that neurons selective for disparity-defined depth structure were indeed present in the most posterior part of the anterior intraparietal area (AIP). The latter study, however, did not extensively map the anterior IPS with single-cell recordings. Moreover the monkeys used ⁎ Corresponding author at: Laboratorium voor Neuro-en Psychofysiologie, Leuven Medical School Herestraat 49, bus 1021, B-3000 Leuven, Belgium. E-mail address: [email protected] (P. Janssen).

http://dx.doi.org/10.1016/j.neuroimage.2015.03.023 1053-8119/© 2015 Elsevier Inc. All rights reserved.

in Srivastava et al. (2009) were not used in the fMRI experiments of Durand et al. (2007). Therefore the apparent discrepancy between the human and monkey data could be related to (low) resolution of the monkey fMRI data, insufficient micro-electrode sampling in the singlecell experiments, and differences in 3D perception across animals etc. Alternatively, genuine interspecies differences in the organization of the anterior IPS may exist between human and non-human primates (Peeters et al., 2009). fMRI in awake, behaving rhesus monkeys (Vanduffel et al., 2001) is invaluable for the investigation of human–non-human primate homologies (Koyama et al., 2004; Mantini and Vanduffel, 2013; Nakahara et al., 2002; Vanduffel et al., 2002). Rhesus monkeys also provide the opportunity to perform single-cell recordings, which can provide detailed information on the neural selectivity at a much finer scale. Therefore, we performed fMRI-guided electrophysiological experiments in the lateral bank of the IPS and recorded single-unit (SUA), multi-unit (MUA) and local field potential (LFP) activity in and near the sites of fMRI activation elicited by curved surfaces in the same subjects. We found that the fMRI activations related to the depth structure in the IPS consisted of two patches of the depth structure selective neurons surrounded by regions showing no neural selectivity. Hence the macaque anterior lateral bank of the IPS consists of two subsectors showing selectivity for the disparity-defined depth structure, which may correspond to the two regions previously described in the human anterior IPS.

I.C.L. Van Dromme et al. / NeuroImage 113 (2015) 86–100

87

Fig. 1. A: Depth structure sensitivity in the macaque parietal cortex. Intraparietal (IPS) regions sensitive to the curvature and orientation of 3D stereo-surfaces projected onto a flattened representation of the left IPS taken from Durand et al. (2007). Red regions are sensitive to curvature whereas orange regions are sensitive to zero order disparity. The pink outline delineates the regions sensitive 3D orientation. Reproduced with permission from Durand et al. (2007). B: Depth structure sensitivity in the human parietal cortex. Statistical parametric maps (single subject) showing the depth structure from disparity-driven activation sites projected onto the flattened left hemisphere. Colored voxels represent areas significantly (p b 0.05 FDR corrected) activated in the interaction [(CS–CM) − (FS–FM)], masked with the main effect of disparity at p b 0.05 uncorrected. Reproduced with permission from Georgieva et al. (2009).

2. Materials and methods 2.1. Subjects, surgery and training procedures Two male rhesus monkeys (monkey K: 4 kg; monkey M: 6 kg) participated in both the fMRI and single-cell experiments. A magnetic resonance imaging (MRI)-compatible head fixation post was implanted on the skull using ceramic screws and dental acrylic. Six weeks after surgery, the monkeys started training to fixate a spot on a display inside a one degree, electronically-defined window while 3D stimuli were presented at the center of the display. Stereoscopic presentation was achieved using liquid crystal shutters (Janssen et al., 2000). To verify that the monkeys were able to see depth in random-dot stereograms, both monkeys were first trained to discriminate convex and concave curved surfaces defined by binocular disparity (Verhoef et al., 2010). The concave and convex 3D surfaces were presented for 800 ms at the fixation point, after which two choice targets appeared on either side of the fixation point. Monkeys were then required to make an eye movement to the left for the concave surfaces or to the right for the convex surfaces in order to obtain a liquid reward. In this 3D-structure discrimination task, the contour of all 3D stimuli was circular and disparity coherence was 100%. The concave and convex surfaces (a two-dimensional Gaussian profile) were presented at three different positions in the depth (near, at the fixation plane, and far) to encourage discrimination of the 3D profile rather than absolute disparity. The disparity gradient was present along the surface of the stimulus but not on the boundary (Theys et al., 2012b) so that monocular cues were absent. Once the monkeys obtained a discrimination performance of at least 80% correct (typically after three to four weeks of training), they were scanned during passive fixation. After the fMRI-experiments, a recording chamber was implanted above the anterior IPS under isoflurane anesthesia and aseptic conditions. The orientation of the cylinder was vertical over the right

hemisphere for monkey K. and oblique over the left hemisphere for monkey M., since in this monkey the strongest fMRI activations were stronger in the left hemisphere (Figs. 2B and 3B) and located more laterally, on the shoulder of the lateral bank of the IPS, extending into PFG and PG. Although the overall locations of the fMRI activation related to the depth structure in the IPS were highly similar in the two monkeys, we chose an oblique approach in monkey M. to avoid potential damage to the recording area that might be caused by the insertion of the guiding tube. The position of the recording chamber was centered on the local maximum of the fMRI activation in the IPS at Horsley Clark coordinates 2 A and 19 L for monkey K. and 3 P and 19 L for monkey M. Animal care and experimental procedures complied with the national and European guidelines (Directive 2010/63/EU) and were approved by the Ethical Committee of the KU Leuven. 2.2. Stimuli The stimulus set of the fMRI and single-cell experiments consisted of random-dot stereograms in which the depth was defined by horizontal disparity (dot size 0.08°, dot density 50%, vertical extent 5.5°) presented on a gray background. All stimuli were generated using MATLAB (R2010a, MathWorks) and were gamma-corrected. We used a 2 by 2 design (Fig. 2A) with factors curvature (curved vs flat) and disparity (stereo vs control), as described in Durand et al. (2007) and Joly et al. (2009). The stereo-curved condition consisted of three types of smoothly-curved depth profiles (1, 1/2, or 1/4 vertical sinusoidal cycle) together with their antiphase counterparts obtained by interchanging the monocular images between right and left eyes (disparity amplitude within the surface: 0.5°). Each of the six depth profiles was combined with one of four different circumference shapes (example in Fig. 2A) and appeared at two different positions in the depth (mean disparity + or − 0.5°), creating a set of 48 curved surfaces. In the stereo-flat condition, flat surfaces (using the same four circumference

88


Fig. 2. Methods. A: An example of stimuli and fMRI design. The red-green anaglyphs are examples of the stimuli used in the fMRI and single-cell experiments. The icons above the anaglyphs in the ‘Stereo’ row represent 3D renderings of the depth structure of the curved and flat surfaces. The fMRI design was a 2 by 2 factorial design with the factors disparity (stereo vs control) and curvature (curved vs flat). B: Recording sites in monkey M. Anatomical horizontal (left) and coronal (right) MR images of monkey M. The recording grid (yellow) over the left hemisphere is shown on the horizontal sections. The coronal MR image shows the fMRI activation in the intraparietal sulcus (IPS) elicited by curved surfaces. The thin white lines (arrows) represent copper sulfate capillaries inserted into key grid positions. The bottom row shows an example of coronal MR images at different anterior–posterior levels spaced 2 mm apart to illustrate the extent of the activation. C: Recording sites in monkey K. Same conventions as in B. D: Anatomical MRI with microelectrode inserted into the anterior lateral bank of the IPS (arrowheads).

shapes) were presented at 12 different positions in the depth, such that the disparity content was identical to that in the stereo curved condition. Finally the control conditions (stereo-control and flat-control) consisted of the presentation of one of the monocular images (either belonging to one of the stereo-curved stimuli or to one of the stereoflat stimuli) to both eyes simultaneously. Each control condition consisted of exactly the same monocular images as the corresponding stereo condition, hence the binocular input was identical in the stereo conditions and in the control conditions.

2.3. fMRI scanning procedures Monkeys were trained to sit in a sphinx position in a plastic MRIcompatible monkey chair. A translucent screen was placed in front of the monkey at a distance of 57 cm. The stimuli were rear-projected from a Barco 6300 LCD projector. During fMRI scanning, dichoptic presentation of the stimuli was provided by means of red/green filter stereoglasses that were placed in front of the eyes of the monkey. Eye position was monitored at 120 Hz during scanning using


89

Fig. 3. IPS region sensitive to depth structure, block design. T-score activation maps (thresholded at p b 0.05 FWE corrected) for the curvature × disparity interaction effect ([curved stereo– curved control] − [flat stereo–flat control]) are projected onto the folded cortical surface of the left and right hemispheres of the template anatomy (M12, top view), for the two monkeys (A: monkey M., B: monkey K.) independently. The maximum T-value is 15 for monkey M. and 20 for monkey K. A zoomed in window of the local maxima of this T-score activation map thresholded at the 5% highest t-scores is presented below panels A and B. Gray outlines indicate the anatomical ROIs of AIP and LIP and the purple circle indicates the recording chamber.

a pupil–corneal reflection tracking system (ISCAN). The monkeys were continuously rewarded for maintaining fixation within a 1.5 (horizontally) × 2° (vertically), electronically-defined window around the fixation point. To encourage long uninterrupted sequences of fixation, the inter-reward-interval was systematically decreased from 2000 to 900 ms when the monkey maintained fixation within the window. A contrast agent (monocrystalline iron oxide nanoparticle, MION; Feraheme, AMAG Pharmaceuticals) was injected before each scanning session to enhance the signal-to-noise ratio (Leite et al., 2002; Vanduffel et al., 2001) and the spatial selectivity of the MR signal (Vanduffel et al., 2001; Zhao et al., 2006). MION measurements depend solely on cerebral blood volume (CBV) whereas BOLD signals depend on cerebral blood volume, blood flow and oxygen extraction. Since an increase in brain activation produces a decrease in MR signal in MION CBV maps, the polarity of all signal-change values was inverted to account for the difference between MION CBV and BOLD activation maps. A radial transmit-only surface coil and custom-built eightchannel phased-array receive coil were positioned around the monkeys' head. Functional images were acquired with a 3.0 Tesla full-body scanner (TIM Trio, Siemens), using a gradient-echo single-shot T2*weighted echo-planar imaging sequence (40 horizontal slices, TR = 2 s, TE = 17 ms, 1.25 mm isotropic). In the block design experiment, 12 functional volumes were acquired for every block (or condition, each 24 s long) and these were embedded in a time series of 222 volumes (444 s). Stimuli were presented at a frequency of 1 Hz. The presentation order of the conditions was pseudo-randomized and each condition was presented twice in each time series. Voxels showing a significant interaction between the factors curvature and disparity, i.e. (curved-stereo vs

curved-control) greater than flat-stereo vs flat-control, were deemed to be sensitive to the depth structure of surfaces (Durand et al., 2007). In separate scan sessions, we also measured fMRI activity in an eventrelated design using the same stimulus conditions as in the block design. In the event-related design, the stimulus presentation was also 1 s in duration, with an interstimulus interval ranging from 3 to 4.5 s (99 runs in monkey M., 92 runs in monkey K.). Each stimulus was repeated twice within a time series and each time series consisted of 245 volumes (490 s). In separate scanning sessions, we measured the fMRI activations associated with saccadic eye movements using a block design, similar to that of Durand et al. (2007). The monkeys performed either a passive fixation task (fixation of a single spot in the center of the screen while visual distractors were flashed for 1200 ms: the fixation condition) or a visually-guided saccade task to a spot that appeared either to the left or to the right of fixation along the horizontal meridian (±7°; saccade condition). The contrast saccade–fixation was used to identify activations related to saccadic eye movements. Both monkeys were also scanned with the lateral occipital (LO) localizer, in which grayscale images of intact objects and scrambled images of objects were presented at the fixation point (Denys et al., 2004; Kourtzi and Kanwisher, 2000). The contrast (images of intact objects–scrambled images of objects) was used to map shape-related activations, as in Durand et al. (2007). 2.4. Single-cell recording procedures Single-cell recordings were begun after the fMRI experiments and used the same 3D and control stimuli as in the fMRI experiments. In

90


the recording setup, ferroelectric liquid crystal shutters (Displaytech), each operating at 60 Hz, were used to generate dichoptic presentations. The shutters were synchronized with the vertical retrace of the display monitor (VRG, P46 phosphor) operating at 120 Hz. There was no measurable cross-talk between the two eyes (Srivastava et al., 2009). After 200 ms of fixation, the stimulus was presented at the fixation point for 1 s. A drop of apple juice was given to the monkey as a reward if he maintained fixation throughout the entire trial. In a subset of the grid positions we also recorded neural activity during visually-guided saccades. In this task, monkeys were rewarded for making an eye movement to a target (a small spot) presented at one of 10 different positions in the contralateral hemifield after the dimming of the fixation point (the go-signal). Eye movements were recorded by means of an infrared-based camera system sampling at 500 Hz (EyeLink1000; SR Research). A bright square appearing at the right bottom of the screen (occluded for the monkey) simultaneously with the stimulus was detected and registered by a photodiode attached to the screen. The neuronal activity, eye position signals and photocell pulses were digitized and processed at 20 kHz on a digital signal processor (DSP) (C6000 series; Texas Instruments). Spiking activity was amplified and filtered between 500 Hz and 5 kHz (Alpha Omega Engineering) and local field potential (LFP) activity was filtered between 1 and 200 Hz. Additionally, a notch filter of 50 Hz was applied to the LFP signal. A dual time-window discriminator (operating in LabVIEW and using custom software) was used to sort spikes online on the DSP. Tungsten microelectrodes (FHC or MicroProbes) were used for standard extracellular recordings (impedance at 1 kHz: 1 MΩ). The electrodes were inserted with a hydraulic microdrive (FHC) in a stainless-steel guiding tube that was placed inside a standard grid (spacing 1 mm; Crist Instruments). Before each recording session the grid was rigidly attached to the recording chamber using a plastic screw that entered an opening in the grid, thereby ensuring that different recording sessions in the same grid position were targeting the same recording area. To target the voxels in the lateral bank of the IPS showing a significant interaction between curvature and disparity, we first obtained an anatomical MRI (resolution 0.6 mm isotropic), with glass capillaries filled with a 2% copper sulfate solution inserted into key grid positions. The fMRI activation maps were then warped onto this MRI template using non-rigid matching software (BrainMatch Software; Chef d'hotel et al., 2002) to remove echo-planar distortions in the images and interindividual anatomical differences. The algorithm minimizes a local correlation criterion in the conformations of small displacements to construct a dense deformation field. The dense deformation field is constrained by low-pass filtering. We verified the accuracy of this warping procedure by comparing the warped fMRI activations on the T1 image with the activations plotted on the mean EPI image (see the example sections on the T1 images and on the mean EPI-images in Figs. 2B and C). The close correspondence between the activation maps on the anatomical MRI and the activation maps on the mean EPI-image confirmed the accuracy of the warping procedure. During the recordings, the pattern of active and silent zones (white and gray matter) and the depth measurements on the microdrive and on the MRI template were helpful in identifying the location of the lateral bank of the IPS. In addition, we also visualized the electrode in one of the recording positions in the anterior lateral bank of the IPS using anatomical MRI (Fig. 2D). To relate the fMRI activations to neural activity, we wanted to record spiking and LFP activity in the most unbiased way possible. Therefore, after entering the lateral bank (hence when spiking activity was present), we started single- or multi-unit recordings at different recording depths spaced 200 μm apart irrespective of the presence of responses to the stimuli. If no spiking activity was present, the electrode was advanced until either SUA or MUA could be recorded. In the remainder of the paper, the term ‘grid position’ will refer to a position in the recording grid that was targeted on different recording days

(median number of recording days per grid position: 7, with a range of 2 to 12 days) to acquire sufficient data, and the term ‘recording site’ will refer to a single recording session (typically lasting 1–1.5 h, MUA or SUA) at a particular depth in a given grid position. Each grid position was investigated by recording in a number of recording sites ranging from 12 to 32 (median: 20). The total recording depth explored on any single recording day ranged between 1000 and 2000 μm. In the search test, all stimuli (stereo and control, curved and flat) were presented randomly interleaved at the center of the display, and at one near and one far position in depth (average disparity + and − 0.5°). Each recording site was either multi-unit (217/425, 51%) or single-unit (208/425, 49%) during the search test. We acquired at least 4 trials per stimulus (96 different stimuli) in the search test. If a site was visually responsive, we isolated single neurons online by moving the electrode over a small distance (typically less than 100 μm), and tested these neurons in more detail for higher-order disparity–selectivity (i.e. selectivity for gradients of disparity) in the position-in-depth test (Janssen et al., 2000). In this test, the stimulus (a combination of a depth profile and a circumference shape) evoking the highest response in the search test was selected together with its antiphase counterpart, and presented at five different positions in the depth ranging from − 0.5° (near) to + 0.5° (far) disparity in equal steps (Janssen et al., 2000). Note that this range of positions is similar to the range of positions in the depth used in the fMRI experiment. To delineate the border with neighboring area LIP, we recorded single- and multiunit activities during two different saccade tasks: leftward and rightward saccades to a single target (the same task as in the fMRI), and a delayed visually-guided saccade task with a single target appearing in one of 10 positions in the contralateral hemifield. Finally, to assess the spatial selectivity we also measured single-unit responses to simple two-dimensional shapes (one measuring 5° and a second one measuring 1°; Janssen et al., 2008) presented during passive fixation at 10 positions in the contralateral hemifield (the same positions as in the delayed saccade task). 2.5. Data analysis 2.5.1. fMRI scanning To eliminate body-motion artifacts, an off-line SENSE (sensitivity encoding) reconstruction (Pruessmann et al., 1999) of the images was conducted (Kolster et al., 2009). Correction for higher-order distortion was performed using a non-rigid slice-by-slice distortion correction. A voxel-based analysis was performed with SPM5 as previously described (Friston et al., 1995; Leite et al., 2002; Vanduffel et al., 2001, 2002) to fit a fixed-effect general linear model (GLM). Six motion-realignment parameters were used, and three eye movement parameters were added to the general linear model as covariates of no interest. Eye traces were thresholded within the 1.5° (horizontally) × 2° (vertically) window, convolved with the MION response function and subsampled to the TR of 2 s. The functional volumes were resliced to 1 mm3 isotropic and smoothed with an isotropic Gaussian kernel (FWHM: 1.5 mm). For illustrative purposes (Fig. 3) the SPM5 activation maps were plotted on inflated representations of the M12 anatomical template, using Caret software (version 5.64; http://brainvis.wustl.edu/wiki/index.php/ Caret:About). As in previous studies (Durand et al., 2007; Joly et al., 2009) the voxels showing a significant interaction (p b 0.05 corrected for multiple comparisons on the entire brain) between the factors curvature (curved or flat) and disparity (present or absent) in the block design were considered sensitive to the depth structure. Percent signal changes in the interaction effect curvature × disparity were calculated for every voxel in the anterior lateral bank of the IPS and averaged per grid position along the direction of the electrode track (the number of voxels for each grid position varied between 1 and 6). These percentages (all of them significantly larger than zero at p b 0.05 corrected) were then mapped onto a 2D representation of the anterior IPS. We


calculated the same [curvature × disparity] interaction effect on the data acquired with the event-related design. 2.5.2. Electrophysiological recordings We examined three physiological parameters: the proportion of higher-order disparity-selective neurons, the population spiking response, and the local field potential (LFP). All data analyses were performed in MATLAB (MathWorks). A recording site was considered responsive if the average neural activity after stimulus onset in the search test differed significantly (either increased or decreased) from neural activity before stimulus onset (300–0 ms, t-test p b 0.05). We used two time windows to assess neural responsiveness: an early interval (50–450 ms), as in previous single-cell studies (Srivastava et al., 2009), and a late interval (450–850 ms after stimulus onset). Furthermore, we also considered recording sites that were significantly less active (inhibited) during stimulus presentation as being responsive. Restricting the analysis to the sites with excitatory responses did not alter the main results. Net neuronal responses were calculated by subtracting the mean activity in the 300 ms immediately preceding stimulus onset from the mean activity between 50 ms and 450 ms (early responsive neurons) and 450 to 850 ms (late responsive neurons) after stimulus onset. Consistent with Janssen et al. (2000) and Srivastava et al. (2009) neurons were defined as disparity-selective when they showed a significant response difference between the preferred depth profile and its antiphase counterpart in the position-in-depth test in the early [50–450 ms] epoch (ANOVA p b 0.05). We also adopted the same criterion as in previous studies (Janssen et al., 2000, 2000; Srivastava et al., 2009; Theys et al., 2012a, 2012b) for determining the presence of higherorder selectivity: neurons for which the response to the non-preferred stimulus never significantly exceeded the response to the preferred stimulus at any position in depth were considered higher-order disparity-selective. For every recording position in the grid we calculated the proportion of higher-order selective neurons with respect to the total number of responsive neurons (early or late responsive, inhibitory or excitatory responsive), and mapped those proportions onto the fMRI activations (curvature × disparity interaction) on a flat map of the anterior IPS. To test whether the proportion of higher-order neurons differed as a function of recording depth, we divided our recording sites into two equal subpopulations based on the median recording depth (382 μm) for each grid position (i.e., one ‘upper’ subpopulation and one ‘lower’ subpopulation), and computed a two-way ANOVA for these proportions with factors recording depth (upper versus lower) and grid position. This distinction was not intended to investigate the differences between cortical layers (since neither single-cell recordings nor fMRI at this resolution can reliably distinguish between the cortical layers), but was merely intended to investigate whether the proportion of higher-order neurons differed significantly as a function of recording depth. Because the fMRI contrast we used was designed to identify regions responding more strongly to curved surfaces than to flat surfaces (which does not necessarily correspond to neural selectivity for concave versus convex surfaces), we also analyzed the population spiking responses to curved and flat surfaces as a second electrophysiological parameter. To compare the total spiking activity with the corresponding fMRI activations, we constructed population peristimulus-time histograms (PSTHs) by averaging activity at all recording sites (SUA and MUA, irrespective of the presence of a significant response) during the search test, for each condition, and for each grid position. We then calculated the interaction effect curvature × disparity (as with the fMRI contrast) using the net population spiking responses in the interval [50–450 ms] after stimulus onset, per grid position and summed across recording depths, using an ANOVA to test for significance (p b 0.05). The results were virtually identical if we restricted the analysis to responsive units. To assess the spatial selectivity (in the 2D plane of the monitor) of neurons we performed separate ANOVAs on the net responses during

91

saccades and during the presentation of simple 2D shapes (passive fixation). Finally we also analyzed the LFP response for each grid position. Morlet's wavelet analysis technique (Tallon-Baudry and Bertrand, 1999) was used to analyze the LFP signal with a spectro-temporal resolution of 7. To remove artifacts, we excluded trials with the most extreme 5% of the values (maximum and minimum) in the LFP and in the power of the time–frequency spectra. The net high gamma (50–150 Hz) band response was calculated per trial by dividing the average high-gamma power between 50 and 450 ms after stimulus onset by the average power between 300 and 0 ms before stimulus onset. We computed the mean power of the high gamma response over time by averaging the high gamma response over trials and frequencies (50–150 Hz). We then combined all the trials of all recording sites for each grid position, and computed the same curvature × disparity interaction effect as that derived in the fMRI design. These interaction effects were tested for significance using a 2way repeated measure ANOVA. The same analyses were performed for the other frequency bands alpha (8–12 Hz), beta (12–25 Hz) and low gamma (25–50 Hz). 3. Results 3.1. fMRI results: curvature × disparity interaction effect in the IPS To identify intraparietal regions involved in processing the depth structure from stereo, a 2 × 2 factorial block design was used with factors curvature (curved vs flat) and disparity (stereo vs control); regions showing a significant interaction effect between curvature and disparity (i.e. where the difference in activation evoked by curved surfaces compared to the control stimuli was larger than that for flat surfaces compared to controls) were considered sensitive to the depth structure. In addition, we verified that the [curvature × disparity] interaction effect indeed reflected stronger activations by curved surfaces compared to flat surfaces by masking with the visual activations (i.e. all visual conditions compared to fixation). Negative interactions (stronger activations by flat surfaces compared to curved surfaces) were not observed in the IPS. We scanned two monkeys and included 202 runs (113 runs for monkey M. and 89 runs for monkey K.) collected in 11 scan sessions (5 for monkey M. and 6 for monkey K.) in the fixedeffect analyses. Runs in which the monkey fixated less than 90% of the time (12 runs in monkey M. and 11 runs in monkey K.) within a 1.5 × 2 deg window were excluded from the data set. Fig. 3 shows the fMRI interaction contrast (curvature × disparity) of the block design experiment, in an overhead view of the inflated brain for each monkey at a threshold of p b 0.05 FWE corrected. In a large region in the anterior part of the lateral bank of the IPS in both monkeys, the difference between stereo and control stimuli was significantly greater for the curved surfaces compared to the flat surfaces at different positions in the depth. This activation extended 10 to 12 mm along the IPS of both hemispheres, starting 2–3 mm posterior to the rostral tip of the IPS. These results are consistent with those of Durand et al. (2007); Joly et al. (2009). Note that in both monkeys — in contrast to Durand et al. (2007) — the curved surfaces also elicited significantly stronger activations compared to the flat surfaces more posteriorly in the IPS, in a region possibly corresponding to the caudal intraparietal area (CIP). To test whether two separate activations would emerge when using a more stringent threshold, we also plotted the 5% most strongly activated voxels in both monkeys (inset in Figs. 2A and B). However, in both monkeys this more stringent criterion yielded a single activation in the anterior lateral bank of the IPS. Thus, with a higher-field scanner (3 T compared to 1.5 T), an 8-channel coil (instead of a single-channel coil) and higher spatial resolution (2 mm3 instead of 8 mm3), we replicated and extended the findings of previous studies showing an extensive activation by the curved surfaces in the anterior lateral bank of the IPS.

92


3.2. Assessment of higher-order disparity–selectivity We recorded in 424 sites (196 in monkey M. and 228 in monkey K.) in 22 grid positions (10 in monkey M. and 12 in monkey K.) in the anterior lateral bank of the IPS, in and around the fMRI activation evoked by the curved surfaces. The reproducibility of the recording penetrations was evident from the large differences in the proportions of 3D-shape selective neurons observed in adjacent grid positions (see below). To assess higher-order disparity–selectivity, we tested every responsive single-unit recording site with presentations of the preferred and non-preferred 3D shapes (i.e. the antiphase counterpart consisting of the same monocular images exchanged between the eyes) at five different positions in the depth. Two examples of higher-order disparityselective neurons (one from each monkey) are illustrated in Figs. 4A and B. Both neurons responded to the convex depth profile at every position in the depth tested (top row) and the response to the concave depth profile never exceeded the response to the preferred depth profile (bottom row). Hence both neurons displayed selectivity for changes of disparity over the surface of the stimulus (i.e. higher-order disparity– selectivity). In contrast, the two examples of neurons in Figs. 4C and D were disparity-selective in view of their significant response differences between preferred and non-preferred 3D shape at the middle position in the depth. However, these neurons also responded at one or more positions in the depth more strongly to the non-preferred 3D shape

than to the preferred 3D shape (e.g. position near2, non-preferred 3D shape compared to position far2, preferred, for the left neuron, Fig. 4C). These neurons were labeled as non-higher-order disparityselective. Note that some of these non-higher-order neurons appeared to respond to absolute disparity (e.g. near disparity in the case of the neuron in Fig. 4C), whereas for other neurons (e.g. the neuron in Fig. 4D) we could not identify a particular disparity that drove the responses. Similar observations have been made in area V4 (Hegde and Van Essen, 2005). In total 109 (26%, 46 in monkey M. and 63 in monkey K.) higher-order and 53 (13%, 28 in monkey M. and 25 in monkey K.) non-higher-order disparity-selective neurons were recorded in these two monkeys. 3.3. Patches of higher-order disparity-selective neurons We mapped the proportions of higher-order neurons onto the fMRI activation of the block design in the anterior IPS (Fig. 5). Sixteen grid positions (7 in monkey M. and 9 in monkey K.) targeted the fMRI activations while 6 additional positions (3 in monkey M. and 3 in monkey K.) were located outside the fMRI activations in the IPS. We first computed the percent signal change in the interaction effect of the fMRI activations per voxel (after subsampling to 1 mm isotropic) in the lateral bank of the IPS. These percentages were then averaged across the cortex along the direction of the electrode tracks (i.e. in the vertical direction for

Fig. 4. Assessment of higher-order disparity–selectivity. A: An example of higher-order disparity-selective neuron of monkey M. The top row represents the peristimulus-time histograms (PSTHs) of the responses to the preferred depth profile at five different positions in depth (near 2–far 2). The bottom row represents the responses to the non-preferred depth profile at the same positions in depth. Stimulus duration was from 0 to 1000 ms. An icon illustrating the perceived 3D structure is presented in the middle. B: An example of higher-order disparityselective neuron of monkey K. Same conventions as in A. C: An example of non-higher-order disparity-selective neuron of monkey M. At the fixation plane, the response to the preferred depth profile was significantly higher than the response to the non-preferred depth profile, but at the nearest position in depth (near2), the response to the non-preferred depth profile exceeded the responses to the preferred shape at position far2. Same conventions as in A. D: An example of non-higher-order disparity-selective neuron in monkey K. Same conventions as in A. The scale bar in each panel indicates the maximum responses of the neuron, which were 80, 12, 58 and 32 spikes/s for panels A, B, C and D respectively.


93

Fig. 5. Relation between the fMRI interaction effect and the proportion of higher-order disparity-selective neurons. The left and right panels are a 2D representation of the anterior IPS for monkeys M. and K., respectively (grid coordinates indicated to the left and at the bottom of each panel). The white contour in each panel demarcates the shoulder of the IPS, approximately at the transition between the lateral bank of the IPS and the parietal convexity. The gray area represents the percent signal change (PSC) of the fMRI interaction effect (curved × disparity) in the block design averaged along the direction of the electrode tracks (i.e. in the vertical direction for monkey K. and in an oblique direction orthogonal to the cortical surface for monkey M.). Voxels that did not show a significant interaction effect in the fMRI contrast are indicated in black. Colored circles indicate the proportions of higher-order neurons measured in these grid positions (hot color scale, white arrows indicate high proportions of higher-order neurons). Green dotted lines between the circles indicate non-significant differences between the proportions in neighboring grid positions. The red contours illustrate the extent of the curvature × disparity interaction effect in the event-related design (p b 0.05 corrected). The coronal MR images illustrate the anterior–posterior level corresponding to three grid positions (the anterior higher-order patch, the posterior higher-order patch and a grid position with saccaderelated activity). White arrows on the coronal MR images indicate the direction of the electrode tracks.

monkey K. and in an oblique direction orthogonal to the cortical surface for monkey M., as indicated by white arrows in Figs. 2B and C). In all of these voxels, the percent signal change was significantly greater than zero (at p b 0.05 corrected), and varied between 0.30 and 0.45% in monkey M. (average SD in percent signal change across voxels = 0.0647; t-scores ranging from 4.92 to 12.6) and between 0.4 and 0.9% in monkey K. (average SD in percent signal change across the voxels = 0.1723; t-scores ranging from 4.97 to 13.5). The gray areas in Fig. 5 represent these mean percent signal changes for the interaction effects for each monkey individually. We plotted the proportion of higher-order disparity-selective neurons on a map of the fMRI interaction effect for each monkey in Fig. 5 (hot color scale). In both monkeys we observed the grid positions with high proportions of higher-order disparity-selective cells (4/16 positions within the fMRI activation, and 5 out of 22 grid positions in total, 23%; proportions ranging from 38% to 65%, significantly different from zero, p b 0.01). The data of both monkeys in Fig. 5 suggest that we recorded in two patches containing many higher-order neurons, indicated with arrows in Fig. 5. One higher-order patch was located anteriorly in the IPS, the other more posteriorly in the lateral bank of the IPS (in monkey K. the more posterior patch consisted of two grid positions). The coronal MR images in Fig. 5 illustrate the estimated anatomical locations of these higher-order patches: the anterior patch was clearly located in anterior AIP, approximately 2 or 3 mm posterior to the tip of the IPS, whereas the posterior patch was located close to area LIP (saccade responses will be discussed below). Note that the posterior higher-order patch of monkey M. was located outside the fMRI activation at the p b 0.05 corrected level (see Discussion) section, although the temporal SNR in this voxel was in the same range as the temporal SNR in the neighboring voxels. We also observed 4 out of the 16 grid

positions within the fMRI activation with significant but low (17 to 29%) proportions of higher-order disparity-selective neurons, whereas more than half of the recording sites (13/22, 59%; 8/16 grid positions within the fMRI activation) contained almost no (15% or less, not significantly different from zero using the Z-test for proportions) higherorder disparity-selective neurons. In monkey M., the most anterior higher-order patch was separated from the most posterior patch by a number of sites with very low proportions of higher-order neurons (p b 0.05 for all comparisons between the proportion in the anterior grid position and proportions in the neighboring grid positions). Similarly in monkey K., two patches (the most posterior one consisting of two grid positions) contained high proportions of higher-order cells, surrounded by grid positions with very low proportions of higherorder neurons (p b 0.05 for all comparisons between the proportion in the anterior grid position and proportions in the neighboring grid positions). Overall, in 50% of the recording sites within the fMRI activation, the proportion of higher-order neurons did not differ significantly from zero, compared to 83% outside the fMRI activation. Calculated across all grid positions tested, the presence of an interaction effect in the fMRI signal was not related to the proportion of higher-order neurons (Fisher exact test, p = 0.63). Hence, although all the voxels were significantly more activated by the curved surfaces compared to the flat surfaces in the fMRI experiment, these activations corresponded to small patches with high proportions of higher-order disparityselective neurons. Note that the large difference in the proportion of higher-order neurons between the two most anterior grid positions in monkey K. (45% vs 5%, p = 0.0015) illustrates that our recording sessions (6 sessions in the lateral position and 10 sessions in the medial position) were reliably targeting the same cortical sites on different recording days.

94


The red outlines in Fig. 5 indicate the extent of the fMRI interaction effect in the event-related design (p b 0.05 FWE corrected). The extent of the fMRI interaction effect in the event-related design was smaller than that of the block design in both monkeys, but in both animals a single activation related to the depth structure was measured in the anterior IPS. Thus the results obtained with the event-related design were qualitatively similar to the results obtained with the block design. We recorded neural activity in the lateral bank of the IPS along electrode penetrations that ran approximately orthogonal to the cortex in monkey M. and at an oblique angle in monkey K. (Fig. 2), and noted the depth at which we entered the lateral bank as a reference point. Although it is potentially misleading to draw conclusions about cortical layer effects in recording experiments with a single electrode, we tested whether the proportion of higher-order neurons differed as a function of recording the depth within the lateral bank. To that end, we divided our recording sites into two equal subpopulations based on the median recording depth (382 μm from the point of entering the lateral bank) for each grid position (i.e., one ‘upper’ subpopulation and one ‘lower’ subpopulation), and computed a repeated-measures ANOVA on the proportions of higher-order neurons with factors recording depth (upper versus lower) and grid position. The main effect of the grid position was highly significant (p = 0.001), but not that of the recording depth (p = 0.32). Since we explored up to 2000 μm of the cortex in one recording session, it is highly unlikely that we missed the clusters of higher-order neurons in the deeper layers. Moreover, a recently published study (Verhoef et al., 2014) investigated the clustering of higher-order AIP neurons along the vertical extent axis (but not the anterior–posterior or medio-lateral extent) more systematically using 3D curved surfaces and did not observe differences in higher-order selectivity as a function of recording depth. Thus, consistent with previous observations (Verhoef et al., 2014), the proportion of higher-order neurons appeared relatively homogeneous across the recording depth. The low proportions of higher-order neurons at several grid positions could be due to either the absence of responses or the absence

of neural selectivity for the depth structure in those grid positions. Therefore we plotted the proportion of excitatory responsive neurons on the fMRI activations in Fig. 6A. For almost all the grid positions the proportion of excitatory responsive cells was rather high, ranging from 26 to 90% (mean = 53%), even in the grid positions with low proportions of higher-order cells. Fig. 6B illustrates that we rarely observed sites with high proportions of inhibitory responsive neurons (one in each monkey, arrows). The posterior higher-order patch in monkey M. contained a high proportion of neurons with an inhibitory response but also a high proportion of higher-order cells. This seemingly paradoxical finding can be explained by the difference between the search test (which contained many different curved surfaces and was used to classify the responsiveness of the recording site) and the position-in-depth test (which contained the preferred 3D stimulus and its antiphase counterpart and was used to determine higher-order selectivity): neurons in this grid position were inhibited by most stimuli in the search test but showed very selective excitatory responses to a small number of curved surfaces, so that the net response averaged across all curved stimuli in the search test was negative. It is noteworthy that this grid position was indeed located outside the fMRI activation, hence the fMRI contrast correctly predicted the observed neural responses. In any case, we did not observe widespread inhibition evoked by our visual stimuli; although many grid positions contained few or no higher-order disparity-selective cells, most of these positions contained visually (excitatory) responsive neurons. It could be argued that at least some of the non-higher-order neurons may have contributed to the fMRI interaction effect: for example both neurons in Fig. 4B preserved their selectivities at some positions in the depth (near1, fix and far1 for the neuron in the left panel in Fig. 4B). Although this neuron would also be activated by the flat surfaces presented at the near disparities in the flat stereo condition (see also Janssen et al., 2000), other non-higher-order neurons may provide information about the depth profile, albeit at a limited number of positions in the depth (like the example of neuron in the right panel

Fig. 6. Relationship between the fMRI interaction effect and the proportion of responsive neurons. A: Proportions of excitatory responsive neurons in monkey M. (left panel) and monkey K. (right panel). B: Proportions of inhibitory responsive neurons in monkey M. (left panel) and monkey K. (right panel). White arrows indicate grid positions with a high proportion of inhibitory responses. Same conventions as in Fig. 5.


of Fig. 4B). Since only 2 grid positions outside the higher-order patches contained more than 20% non-higher-order neurons, it is unlikely that our results could be accounted for by any significant contribution to the fMRI interaction effect arising from non-higher-order neurons. We analyzed the latency of the depth structure selectivity in the anterior and in the posterior higher-order patch separately by calculating the t-tests on consecutive bins of the averaged response of all higher-order neurons in each of the patches. In both monkeys, the selectivity latency (defined as the first of three consecutive bins (one bin is 10 ms) with a significant (t-test; p b 0.001) response difference between preferred and non-preferred 3D surfaces) was 10 ms shorter in the posterior higher-order patch (70 ms) compared to the anterior higher-order patch (80 ms). 3.4. Population spiking responses A significant fMRI interaction effect predicts stronger responses to curved surfaces than to flat surfaces at the population level, which does not necessarily correspond to the presence or absence of higherorder disparity-selective neurons: the average population response of a group of very selective neurons may actually be lower for the preferred stimulus category compared to control stimuli (Scannell and Young, 1999). To investigate the relation between the fMRI activation and the average neural response in this anterior IPS region, we calculated the average population spiking responses in the search test using the same stimulus set as in the fMRI experiment. For every grid position we calculated the curvature × disparity interaction effect using population responses averaged across recording sites (both MUA and SUA, time interval 50 to 450 ms) in the search test. In panels A and B of Fig. 7, red dots represent grid positions in which the interaction effect was significant (ANOVA, curvature × disparity interaction p b 0.05), whereas

95

blue dots represent grid positions lacking such an effect. We found 7 (6/16 within and 1/6 outside the fMRI activation) grid positions that showed a significant interaction effect between curvature and disparity (examples in Fig. 7B). Note that one of the most posterior grid positions in monkey M. showed a significant interaction effect caused by stronger responses to the flat surfaces than to the curved surfaces, indicated in orange in the left panel of Fig. 7A, and that the interaction effect in the most posterior grid position of monkey M. was marginally significant (p = 0.04), with very low average responses. Calculating the interaction effect on the entire stimulus interval (50–1000 ms) instead of the interval [50–450 ms] after the stimulus onset did not change the results. The size of the interaction effect in the population spiking response (expressed in spikes/s) differed significantly across the grid positions in both animals (one-way ANOVA, main effect of position, p = 0.03 for monkey M. and p = 0.005 for monkey K.). Furthermore the presence of an interaction effect in the fMRI signal was not related to the presence or absence of an interaction effect in the population spiking response (Fisher exact test, p = 0.66). Although the population interaction effect was quite variable within the fMRI activation in the IPS, averaging across all grid positions within the fMRI activation revealed a significant interaction effect in the population spiking response for both monkeys independently (ANOVA, p b 0.05). Moreover, averaging all sites outside the fMRI activation did not show a significant interaction effect (data not shown). Hence, although the fMRI activation globally corresponded to the population spiking response (averaged across the grid positions), the IPS region more strongly activated by the curved surfaces than by the flat surfaces consisted of highly heterogeneous populations of neurons. Our data also allow us to compare the relationship between the single-unit selectivity (the proportion of higher order neurons,

Fig. 7. Relationship between the fMRI interaction effect and the population spiking response. A: The presence or absence of a significant interaction effect in the population spiking response is indicated on the 2D representation of the fMRI interaction effect. Red circles indicate grid positions with a significant (curvature × disparity) positive interaction effect in the population spiking response, the orange circle is a grid position with a significant negative interaction effect and blue circles indicate grid positions without a significant interaction effect, for monkey M. (left panel) and monkey K. (right panel). Otherwise same conventions as in Fig. 5. B: An example of population spiking responses in the curved stereo (red), flat stereo (green), curved control (blue) and flat control (black) conditions. Zero is the time of stimulus onset. The left panel shows a grid position with a significant interaction effect (the location is indicated by the arrow) in monkey M., the right panel shows an example of the grid position without a significant interaction effect (location indicated with the arrow) in monkey K.

96


Fig. 5A) and the average population response (Fig. 7A). In 3 out of 5 grid positions showing high proportions of higher-order neurons, the average response to curved surfaces was stronger than to the flat surfaces at the population level. However, stronger population responses to the curved surfaces than to the flat surfaces could also be measured in sites with a very low proportion of these higher-order neurons (e.g. the most anterior-medial position in monkey K.). Hence the proportion of higher-order neurons was not related to the magnitude of the interaction effect in the population spiking responses (Fisher exact test, p = 0.18). Therefore, despite the overall correspondence between the interaction effect measured with fMRI and the interaction effect measured in the population response, the relation between the single-unit selectivity and the population response was not straightforward. The fMRI [curvature × disparity] interaction effect measured in the event-related design (red outline in Fig. 7) also consisted of the grid positions with significant interaction effects in the population spiking response and the grid positions without such interaction effects. Thus similar to the results obtained for the proportions of higher-order neurons, the relationship between the population spiking responses and fMRI activations was qualitatively similar for the event-related and block design data. 3.5. Local field potential responses LFP signals, and particularly the gamma band activity, may correlate better with the fMRI signal than the spiking activity (Logothetis et al., 2001). Therefore, we also examined the high gamma band (50– 150 Hz) responses of every grid position and calculated the interaction effect between the factors curvature and disparity. Fig. 8A illustrates for each grid position whether the interaction effect in the high gamma band response was significant (red dot) or not (blue dot). The gamma response in recording the sites of several grid positions (9/22) was stronger for the curved surfaces than for the flat surfaces, but we also

observed many grid positions within and outside the fMRI activation with no significant interaction effect in the high gamma band response (13/22). As for the population spiking responses, the presence or absence of an fMRI interaction effect was not associated with the presence or absence of an interaction effect in the gamma responses (Fisher exact test, p = 0.14). An example of the grid position with a significant interaction effect in high gamma band response is presented in the left panel B of Fig. 8 (F = 0.058; p = 0.011). In contrast, the grid position in the right panel B of Fig. 8 showed a significant gamma response but no difference between the high gamma band responses in the different stimulus conditions (F = −0.006; p = 0.332). The grid positions with a high proportion of higher-order disparity selective neurons tended to show stronger responses to the curved surfaces than to the flat surfaces in their population high gamma responses (Fisher exact test p = 0.0003). However, surrounding grid positions with a low proportion of higher-order disparity selective neurons also showed a significant interaction between the factors curvature and disparity. Therefore the interaction effect in the gamma band responses was more extended in each monkey compared to the SUA. As for the population spiking responses, the presence or absence of an fMRI interaction effect was not associated with the presence or absence of an interaction effect in the gamma responses (Fisher exact test, p = 0.14). Perhaps surprisingly, given the close relationship between gamma band activity and population activity (Ray and Maunsell, 2011), the interaction effect in the gamma responses was also not significantly associated with the interaction effect in the population spiking responses (Fisher exact test, p = 0.38). In the lower frequency bands (alpha, beta and low gamma), we frequently observed negative interactions (i.e. less response to the curved surfaces than to the flat surfaces), especially in monkey M. (Supplementary Fig. 1). However, the spatial distribution of the responses in these lower frequency bands did not correspond to the two patches of higher-order disparity-selective neurons we measured with single-unit recordings, nor to the fMRI activation.

Fig. 8. Relationship between the fMRI interaction effect and the gamma response in the LFP. A: The presence (red circles) or absence (blue circles) of an interaction effect is indicated on the 2D representation of the fMRI interaction effect, for monkey M. (left panel) and monkey K. (right panel). B: An example of the grid positions with a significant interaction effect in the gamma response (left panel, location indicated with the arrow) and without such an interaction effect in the gamma response (right panel). Otherwise same conventions as in Fig. 7.


3.6. Localizer experiments In an effort to characterize the anterior IPS region more thoroughly and to compare with previous studies, we performed two additional localizer experiments: the LO localizer (Kourtzi and Kanwisher, 2000) and the saccade localizer. We mapped the activations evoked by the contrast (intact images of objects–scrambled images of objects) onto the 3D-structure activation in Fig. 9A. In both monkeys this LO localizer activation proved to be very extensive, measuring 11 mm in the anterior–posterior direction and comprising almost the entire anterior lateral bank of the IPS. Both patches with higher-order neurons were clearly located within the LO localizer activation. The functional border between AIP and neighboring area LIP is illdefined, and the question remains as to where the functional border between these two areas was located at the single-cell level. To that end, we searched for spatially-selective saccadic activity during singleunit recordings acquired in the most posterior grid positions of both monkeys (2 in monkey M. and 3 in monkey K.). Overall, 36/112 neurons (32%) recorded in these grid positions showed spatially-selective saccadic activity (example of neurons in Fig. 9C), with the proportion of saccade-selective neurons varying between 12 and 60%. In contrast, we did not observe clear spatially-selective saccadic activity in the most posterior higher-order patches of either monkey (9 neurons

97

recorded). We also assessed the spatial selectivity of neurons in the most posterior grid positions during the presentation of simple shapes (the same stimuli as in a previous LIP study, Janssen et al., 2008) at 10 positions in the contralateral hemifield. A large proportion of the neurons (54/115, 47%) were significantly selective for the spatial position of these shapes (ANOVA, p b 0.05), and the overwhelming majority of these neurons (106/115, 92%) responded maximally at coordinates outside the foveal position, consistent with the LIP study of Janssen et al. (2008). Therefore given the single-cell properties of the neurons and the anatomical location of the recording sites (coronal MR image 3 in Fig. 5 left and right panels), the most posterior grid positions were most likely located in the dorsal portion of area LIP. Furthermore, these single-unit data confirm that the posterior higher-order patch was located immediately anterior to area LIP. We also scanned both the monkeys used in the single-cell recordings and a third monkey (monkey R.) during visually-guided saccades, and plotted the contrast [saccade–fixation] (p b 0.05 corrected) on the 3Dstructure activations in the IPS in Fig. 9B (red area). The fMRI activation elicited by saccades was very extensive in monkey M. (Fig. 9B, left panel), comprising the entire lateral bank of the IPS up to the most anterior part of the 3D-structure activation. However in monkey K. the saccade-related activation was much more restricted (Fig. 8B, middle panel), overlapping with the most posterior grid positions where the

Fig. 9. Localizer experiments. A: LO localizer experiment. The red area indicates the fMRI activation for the contrast (intact grayscale images of object–scrambled images of objects) at p b 0.05 corrected. Colored circles indicate the proportions of higher-order disparity-selective neurons, as in Fig. 5. B: The saccade localizer (saccade–fixation, red area) is plotted on the 2D representation of the fMRI interaction effect evoked by curved surfaces, for monkey M. (left panel), monkey K. (middle panel), and monkey R. (right panel). The colored circles in the left and middle panel indicate the proportions of neurons with spatially-selective saccadic activity in the most posterior grid positions. Below these panels are PSTHs of the two examples of neurons tested during visually-guided saccades to 10 positions in the contralateral hemifield. Each PSTH represents the activity for saccades towards that position, the fixation point was in the center (indicated with a red dot). Time zero indicates target onset.

98


proportion of higher-order neurons was very low. Because of the large discrepancy between the two monkeys regarding the extent of the saccade-related fMRI activation, we scanned a third monkey (R.) using the same stimuli and task (i.e. the [curvature × disparity] interaction and the saccade localizer experiments). The saccade-related fMRI activation was — similar to monkey M. — very extensive and almost reached the anterior border of the 3D-structure activation (Fig. 9B, right panel). Thus, in two of the three monkeys tested, the fMRI contrast [saccade–fixation] showed a very large activation comprising both LIP and AIP. However, previous studies have demonstrated that area AIP is not saccade-related (Murata et al., 2000; Sakata et al., 1995; Taira et al., 1990). Hence, despite considerable interindividual differences between our animals and the inherent limitations of measuring saccade-related activity with fMRI (e.g. the difference between presaccadic and post-saccadic activity or the presence of retinal slip cannot be dissociated with fMRI), the saccade localizer may, in some cases, overestimate the region where saccadic activity can be recorded (for the group average of the saccade localizer plotted onto the stereo activations see Supplementary Fig. 2). To summarize our findings, we plotted the estimated location of the two higher-order patches onto each monkeys' IPS, together with the fMRI [curvature × disparity] interaction effect and the anatomical ROIs for AIP and LIP (Fig. 10). In both monkeys the anterior higher-order patch was clearly located in AIP, whereas the posterior higher-order patch was located near (monkey M.) or within the anterior part of the anatomical ROI for area LIP (see Discussion) section. 4. Discussion We targeted the fMRI activation elicited by the disparity-defined curved surfaces in the lateral bank of the IPS and recorded the SUA, MUA and LFP responses to the same stimuli in the same animals. Although the fMRI contrast was correct in predicting the location of the 3D-structure selective neurons in the anterior lateral bank of the IPS, we observed two patches of the 3D-structure selective neurons within the IPS activation. These results may reconcile previous fMRI studies in monkeys and humans. The patchy distribution of higher-order neurons in the macaque anterior IPS undoubtedly contributed to the large fMRI activation elicited by the curved surfaces. In fact, we discovered two higherorder patches in each monkey, one in the anterior AIP and one more posteriorly. The latter patch was located anterior to the grid positions with a high number of saccade cells (most likely corresponding to dorsal

LIP). Previous studies using 3D and 2D shapes recorded mainly from this more posterior IPS patch have never observed clear spatially-selective saccadic activity at this location (Romero et al., 2013). However, other studies did observe grasping activity (Romero et al., 2014; Theys et al., 2013), 3D-shape selectivity (Srivastava et al., 2009) and foveal RFs (Romero et al., 2012) in this region. Although it is difficult to distinguish this region from the foveal representation of area LIP (Arcaro et al., 2011; Ben et al., 2001), we propose — based on the neuronal properties and its location immediately anterior to the saccade-related cells — the term posterior AIP to indicate the most posterior patch of higher-order neurons in the anterior lateral bank of the IPS. The presence of two patches of higher-order neurons in the macaque is consistent with the human fMRI study of Georgieva et al. (2009), describing two regions (DIPSA and DIPSM) sensitive to the depth structure in the anterior IPS. Although we cannot be sure that more than two patches with higher-order neurons exist in the macaque anterior IPS, our single-cell findings may reconcile previous monkey (Durand et al., 2007) and human fMRI (Georgieva et al., 2009) studies, thereby adding new evidence with respect to the homology between the human and monkey anterior IPS (Orban et al., 2006). The more medial displacement of DIPSM in humans compared to pAIP in macaques (which is still located in the lateral bank) is consistent with the superior and medial displacement of the human LIP + complex compared to the macaque area LIP, which may be related to the larger size of the angular gyrus component of the ‘default mode network’ in humans compared to monkeys (Sereno and Huang, 2014). The advent of fMRI has revolutionized cognitive neuroscience (Logothetis, 2008), but despite more than two decades of research, the relationship between the fMRI signal and neural activity remains somewhat unclear. The fMRI response is a hemodynamic signal with specific spatial and temporal constraints, resulting from a complex sum of all (pre- and postsynaptic) excitatory and inhibitory activities within a voxel. Hence its relationship to neuronal selectivity at the single-cell level is likely to be very complex (Popivanov et al., 2014; Scannell and Young, 1999). The fMRI contrast used here and in the previous studies (the interaction between the factors curvature and disparity) assumes that the clusters of neurons that encode the depth structure of objects would respond more to the curved surfaces than to the flat surfaces at different positions in the depth. However our population response data clearly showed several sites within the activation that did not respond more to the curved surfaces than to the flat surfaces. Overall, however, the fMRI contrast was correct in localizing higher-order disparity selective neurons (similar to premotor area

Fig. 10. Summary of results. The estimated locations of the anterior and posterior higher-order patches (cyan circles) are projected onto the flattened brain surfaces. We also projected the T-score activation maps (thresholded at p b 0.05 FWE corrected) for the curvature × disparity interaction effect ([curved stereo–curved control] − [flat stereo–flat control]) onto the folded cortical surface of the left and right hemispheres of the template anatomy (M12, top view), for the two monkeys (A: monkey M., B: monkey K.) independently. Gray outlines delineate the anatomical ROIs of LIP and AIP. Same conventions as in Fig. 3.


F5a; Theys et al., 2012a), but the patchy distribution of these neurons along the lateral bank of the anterior IPS was not detected by fMRI. Many factors may explain why monkey fMRI experiments measured a single large activation in the anterior IPS: difference in statistical power between fMRI and population spiking responses, spatial smoothing, insufficient resolution or the close proximity of the two patches (3 mm in one monkey). It remains to be determined whether highresolution high-sensitive fMRI would be able to detect the two subsectors of AIP in monkeys (Janssens et al., 2012). Previous fMRI-single-cell studies in monkeys have measured either the BOLD signal or CBV using MION as a contrast agent. In the primary visual cortex (V1), the BOLD response correlates better with the lower frequency bands of the LFP compared to the higher frequency bands and spikes during perceptual suppression. Bartolo et al. (2011) and Maier et al. (2008) showed that the fMRI BOLD response to plaid stimuli in V1 followed the LFP gamma band responses, but not the multi-unit activity, consistent with that of Logothetis et al. (2001). Even more remarkably, increases in CBV can occur in anticipation of the trial onset in the absence of any neural response, be it spikes or LFP (Sirotin and Das, 2009). However, our LFP data did not provide a better account for the fMRI activation compared to the single-unit or population spiking data (which included SUA and MUA). Our observation of highly localized patches of higher-order neurons surrounded by regions without the depth structure selectivity within the fMRI activation elicited by the curved surfaces contrasts with previous fMRI-singlecell studies in the IT cortex that also used MION as a contrast agent. For example the face patches defined by the fMRI contrast [faces– objects] appear to be very homogeneous, consisting almost entirely of neurons that respond more to faces than to objects (Tsao et al., 2006). More recent studies (Popivanov et al., 2012; Popivanov et al., 2014) have identified body patches in the superior temporal sulcus of the IT cortex using the fMRI contrast [bodies–objects]. Although the neural population in these body patches is more heterogeneous (exhibiting clear within-category selectivity) compared to the face patches, every grid position within the body patches showed a greater average response to bodies than to objects (Popivanov et al., 2014). Finally, Issa et al. (2013) measured fMRI and single-cell responses to images of the faces and objects in the IT cortex, and obtained the best correspondence between fMRI and electrophysiology when the neural activity was spatially smoothed with a 2D kernel of 3.5 mm. Although we may not have recorded in a sufficient number of the grid positions to obtain an exact estimate of the degree of spatial smoothing in the IPS, both the stereo experiment and the localizer experiments suggest that more extensive spatial smoothing may have been required to achieve correspondence between the single-cell responses and the fMRI activations. The discrepancy between the extensive fMRI activation evoked by the curved surfaces in the IPS and the very focal patches of higher-order neurons was not related to the use of disparity-defined 3D stimuli per se. Indeed, Joly et al. (2009) reported a very localized fMRI activation by the curved surfaces in a subsector of the ventral premotor cortex (area F5a), and subsequent electrophysiological experiments confirmed the presence of higher-order neurons in F5a but not in the surrounding regions in the arcuate sulcus (Theys et al., 2012a). Hence the anterior IPS region may be more heterogeneous than frontal or temporal areas. The previous fMRI studies as well as our current fMRI results are based on traditional univariate analysis of the fMRI data and therefore rely exclusively on the information embedded in the time courses of the individual voxels. However, the techniques that implement a multivariate analysis can extract information embedded in the activity pattern of the multiple voxels (e.g. multivariate pattern analysis or MVPA), which may be more closely related to neuronal selectivity (Murayama et al., 2010). Several studies (Cox and Savoy, 2003; Haxby et al., 2001; Haynes and Rees, 2006; Kamitani and Tong, 2005; Norman et al., 2006; Preston et al., 2008) have applied multivariate analysis to fMRI data and have shown the possibility to discriminate

99

between different stimuli (e.g. orientation or near-far disparity), which is not possible with a univariate analysis. Accordingly, it is possible that by applying MVPA to the current (event-related) fMRI dataset we could have achieved a better correspondence with the higher-order disparity selective patches based on electrophysiology. Future studies will have to determine to what extent the most informative voxels in the anterior IPS as determined with MVPA correspond to the two patches of higher-order neurons we observed. We mapped the fMRI [curvature × disparity] interaction effect onto each monkeys' anatomical MRI with the recording grid and glass capillaries inserted into the key grid positions and verified the correctness of these warping procedure on the EPI images. Therefore, any shifts in the position of these activations were in all likelihood small. Furthermore, in view of the short distance between the bottom of the recording grid (which was rigidly anchored to the recording cylinder) and the lateral bank of the IPS (guide tube length: 10 mm, electrode track length outside the guide tube: 5–6 mm), any deviation of the electrode along its trajectory would have been small. In support of this assertion, we were able to record markedly different proportions of higher-order neurons in neighboring grid positions spaced only 1 mm apart. Despite all our precautions, one of the higher-order patches (the posterior one in monkey M.) was located outside the fMRI activation. It is important to note that this grid position was the only location where the average population response to the curved surfaces was inhibitory (caused by excitatory responses to a small number of the curved surfaces and inhibitory responses to most other curved surfaces), and that therefore this grid position should be located outside the fMRI interaction effect. Although both monkeys were trained to discriminate the depth structure using saccadic eye movements to the left or to the right, we deliberately used a passive fixation task in both the fMRI and singlecell experiments. A visual depth structure discrimination task during fMRI would certainly have resulted in additional activations related to the operant response (either saccades or hand movements), which would render the interpretation of the fMRI data more difficult. Moreover, Durand et al. (2007) demonstrated by means of a demanding high-acuity fixation task (detection of a change in orientation of a small fixation bar) that the IPS activations related to the depth structure are not caused by attention, and Verhoef et al. (2010, 2011) observed similar selectivity for the depth structure during passive fixation and during visual discrimination in the area AIP at the single-cell level. Thus it is highly unlikely that our results were strongly affected by attention or other task-related factors. Our results support the awake monkey fMRI as a crucial technique for guiding single-cell recordings and focal perturbation experiments, to investigate human–monkey homologies and to clarify the relation between fMRI responses and neural activity (Vanduffel et al., 2014). Indeed, our single-cell recording data could have important implications for many imaging studies in humans and monkeys. Several lines of evidence using different stimuli (3D surfaces, images of objects) and tasks (passive fixation, saccades) suggest that fMRI activations in the anterior IPS — though broadly correct in localizing specific singlecell properties — can overestimate the spatial distribution of neurons with properties corresponding to the fMRI contrast under study. Thus, fMRI activations in the IPS may furnish an estimate of the underlying neuronal responses, and more detailed investigations using single-cell recordings are necessary to chart the neuronal properties on a finer scale. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.neuroimage.2015.03.023. Acknowledgments This work was supported by Geconcerteerde Onderzoeksacties (GOA 2010/19), Fonds voor Wetenschappelijk Onderzoek Vlaanderen grants (G062208N10, G083111N10 and Odysseus grant G.0007.12), Programmafinanciering (PFV/10/008), IUAP P6/29, and ERC-2010-

100


StG-260607. We thank Piet Kayenbergh, Gerrit Meulemans, Stijn Verstraeten, Wouter Depuydt, Inez Puttemans, Sara De Pril, Anne Coeman, Chantal Fransen and Camille Van Eupen for technical assistance and Steve Raiguel for comments on the manuscript. References Arcaro, M.J., Pinsk, M.A., Li, X., Kastner, S., 2011. Visuotopic organization of macaque posterior parietal cortex: a functional magnetic resonance imaging study. J. Neurosci. 31, 2064–2078. Bartolo, M.J., Gieselmann, M.A., Vuksanovic, V., Hunter, D., Sun, L., Chen, X., Delicato, L.S., Thiele, A., 2011. Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the resonance blood oxygen leveldependent signal in macaque primary visual cortex. Eur. J. Neurosci. 34, 1857–1870. Ben, Hamed S., Duhamel, J.R., Bremmer, F., Graf, W., 2001. Representation of the visual field in the lateral intraparietal area of macaque monkeys: a quantitative receptive field analysis. Exp. Brain Res. 140, 127–144. Chef d'hotel, C., Hermosillo, G., Faugeras, O., 2002. Flows of diffeomorphisms for multimodal image registration. Proc. IEEE Int. Symp. Biomed. Imaging 7, 753–756. Cox, D.D., Savoy, R.L., 2003. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19, 261–270. Denys, K., Vanduffel, W., Fize, D., Nelissen, K., Peuskens, H., Van, E.D., Orban, G.A., 2004. The processing of visual shape in the cerebral cortex of human and nonhuman primates: a functional magnetic resonance imaging study. J. Neurosci. 24, 2551–2565. Durand, J.B., Nelissen, K., Joly, O., Wardak, C., Todd, J.T., Norman, J.F., Janssen, P., Vanduffel, W., Orban, G.A., 2007. Anterior regions of monkey parietal cortex process visual 3D shape. Neuron 55, 493–505. Friston, K.J., Frith, C.D., Frackowiak, R.S., Turner, R., 1995. Characterizing dynamic brain responses with fMRI: a multivariate approach. NeuroImage 2, 166–172. Georgieva, S., Peeters, R., Kolster, H., Todd, J.T., Orban, G.A., 2009. The processing of threedimensional shape from disparity in the human brain. J. Neurosci. 29, 727–742. Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P., 2001. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430. Haynes, J.D., Rees, G., 2006. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7, 523–534. Hegde, J., Van Essen, D.C., 2005. Role of primate visual area V4 in the processing of 3-D shape characteristics defined by disparity. J. Neurophysiol. 94, 2856–2866. Issa, E.B., Papanastassiou, A.M., DiCarlo, J.J., 2013. Large-scale, high-resolution neurophysiological maps underlying FMRI of macaque temporal lobe. J. Neurosci. 33, 15207–15219. Janssen, P., Vogels, R., Orban, G.A., 2000. Three-dimensional shape coding in inferior temporal cortex. Neuron 27, 385–397. Janssen, P., Srivastava, S., Ombelet, S., Orban, G.A., 2008. Coding of shape and position in macaque lateral intraparietal area. J. Neurosci. 28, 6679–6690. Janssens, T., Keil, B., Farivar, R., McNab, J.A., Polimeni, J.R., Gerits, A., Arsenault, J.T., Wald, L.L., Vanduffel, W., 2012. An implanted 8-channel array coil for high-resolution macaque MRI at 3 T. NeuroImage 62, 1529–1536. Joly, O., Vanduffel, W., Orban, G.A., 2009. The monkey ventral premotor cortex processes 3D shape from disparity. NeuroImage 47, 262–272. Kamitani, Y., Tong, F., 2005. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679–685. Kolster, H., Mandeville, J.B., Arsenault, J.T., Ekstrom, L.B., Wald, L.L., Vanduffel, W., 2009. Visual field map clusters in macaque extrastriate visual cortex. J. Neurosci. 29, 7031–7039. Kourtzi, Z., Kanwisher, N., 2000. Cortical regions involved in perceiving object shape. J. Neurosci. 20, 3310–3318. Koyama, M., Hasegawa, I., Osada, T., Adachi, Y., Nakahara, K., Miyashita, Y., 2004. Functional magnetic resonance imaging of macaque monkeys performing visually guided saccade tasks: comparison of cortical eye fields with humans. Neuron 41, 795–807. Leite, F.P., Tsao, D., Vanduffel, W., Fize, D., Sasaki, Y., Wald, L.L., Dale, A.M., Kwong, K.K., Orban, G.A., Rosen, B.R., Tootell, R.B., Mandeville, J.B., 2002. Repeated fMRI using iron oxide contrast agent in awake, behaving macaques at 3 Tesla. NeuroImage 16, 283–294. Logothetis, N.K., 2008. What we can do and what we cannot do with fMRI. Nature 453, 869–878. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A., 2001. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157. Maier, A., Wilke, M., Aura, C., Zhu, C., Ye, F.Q., Leopold, D.A., 2008. Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey. Nat. Neurosci. 11, 1193–1200. Mantini, D., Vanduffel, W., 2013. Emerging roles of the brain's default network. Neuroscientist 19, 76–87. Murata, A., Gallese, V., Luppino, G., Kaseda, M., Sakata, H., 2000. Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP. J. Neurophysiol. 83, 2580–2601.

Murayama, Y., Biessmann, F., Meinecke, F.C., Muller, K.R., Augath, M., Oeltermann, A., Logothetis, N.K., 2010. Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA. Magn. Reson. Imaging 28, 1095–1103. Nakahara, K., Hayashi, T., Konishi, S., Miyashita, Y., 2002. Functional MRI of macaque monkeys performing a cognitive set-shifting task. Science 295, 1532–1536. Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V., 2006. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424–430. Orban, G.A., Janssen, P., Vogels, R., 2006. Extracting 3D structure from disparity. Trends Neurosci. 29, 466–473. Peeters, R., Simone, L., Nelissen, K., Fabbri-Destro, M., Vanduffel, W., Rizzolatti, G., Orban, G.A., 2009. The representation of tool use in humans and monkeys: common and uniquely human features. J. Neurosci. 29, 11523–11539. Popivanov, I.D., Jastorff, J., Vanduffel, W., Vogels, R., 2012. Stimulus representations in body-selective regions of the macaque cortex assessed with event-related fMRI. NeuroImage 63, 723–741. Popivanov, I.D., Jastorff, J., Vanduffel, W., Vogels, R., 2014. Heterogeneous single-unit selectivity in an fMRI-defined body-selective patch. J. Neurosci. 34, 95–111. Preston, T.J., Li, S., Kourtzi, Z., Welchman, A.E., 2008. Multivoxel pattern selectivity for perceptually relevant binocular disparities in the human brain. J. Neurosci. 28, 11315–11327. Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P., 1999. SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952–962. Ray, S., Maunsell, J.H., 2011. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9 e1000610. Romero, M.C., Van Dromme, I., Janssen, P., 2012. Responses to two-dimensional shapes in the macaque anterior intraparietal area. Eur. J. Neurosci. 36, 2324–2334. Romero, M.C., Van Dromme, I.C., Janssen, P., 2013. The role of binocular disparity in stereoscopic images of objects in the macaque anterior intraparietal area. PLoS ONE 8 e55340. Romero, M.C., Pani, P., Janssen, P., 2014. Coding of shape features in the macaque anterior intraparietal area. J. Neurosci. 34, 4006–4021. Sakata, H., Taira, M., Murata, A., Mine, S., 1995. Neural mechanisms of visual guidance of hand action in the parietal cortex of the monkey. Cereb. Cortex 5, 429–438. Scannell, J.W., Young, M.P., 1999. Neuronal population activity and functional imaging. Proc. Biol. Sci. 266, 875–881. Sereno, M.I., Huang, R.S., 2014. Multisensory maps in parietal cortex. Curr. Opin. Neurobiol. 24, 39–46. Sirotin, Y.B., Das, A., 2009. Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. Nature 457, 475–479. Srivastava, S., Orban, G.A., De Maziere, P.A., Janssen, P., 2009. A distinct representation of three-dimensional shape in macaque anterior intraparietal area: fast, metric, and coarse. J. Neurosci. 29, 10613–10626. Taira, M., Mine, S., Georgopoulos, A.P., Murata, A., Sakata, H., 1990. Parietal cortex neurons of the monkey related to the visual guidance of hand movement. Exp. Brain Res. 83, 29–36. Tallon-Baudry, C., Bertrand, O., 1999. Oscillatory gamma activity in humans and its role in object representation. Trends Cogn. Sci. 3, 151–162. Theys, T., Pani, P., van, L.J., Goffin, J., Janssen, P., 2012a. Selectivity for three-dimensional shape and grasping-related activity in the macaque ventral premotor cortex. J. Neurosci. 32, 12038–12050. Theys, T., Srivastava, S., van, L.J., Goffin, J., Janssen, P., 2012b. Selectivity for three-dimensional contours and surfaces in the anterior intraparietal area. J. Neurophysiol. 107, 995–1008. Theys, T., Pani, P., van, L.J., Goffin, J., Janssen, P., 2013. Three-dimensional shape coding in grasping circuits: a comparison between the anterior intraparietal area and ventral premotor area F5a. J. Cogn. Neurosci. 25, 352–364. Tsao, D.Y., Freiwald, W.A., Tootell, R.B., Livingstone, M.S., 2006. A cortical region consisting entirely of face-selective cells. Science 311, 670–674. Vanduffel, W., Fize, D., Mandeville, J.B., Nelissen, K., Van, H.P., Rosen, B.R., Tootell, R.B., Orban, G.A., 2001. Visual motion processing investigated using contrast agentenhanced fMRI in awake behaving monkeys. Neuron 32, 565–577. Vanduffel, W., Tootell, R.B., Schoups, A.A., Orban, G.A., 2002. The organization of orientation selectivity throughout macaque visual cortex. Cereb. Cortex 12, 647–662. Vanduffel, W., Zhu, Q., Orban, G.A., 2014. Monkey cortex through fMRI glasses. Neuron 83, 533–550. Verhoef, B.E., Vogels, R., Janssen, P., 2010. Contribution of inferior temporal and posterior parietal activity to three-dimensional shape perception. Curr. Biol. 20, 909–913. Verhoef, B.E., Vogels, R., Janssen, P., 2011. Synchronization between the end stages of the dorsal and the ventral visual stream. J. Neurophysiol. 105, 2030–2042. Verhoef, B.E., Michelet, P., Vogels, R., Janssen, P., 2014. Choice-related Activity in the anterior intraparietal area during 3-D structure categorization. J. Cogn. Neurosci. 1–12. Zhao, F., Wang, P., Hendrich, K., Ugurbil, K., Kim, S.G., 2006. Cortical layer-dependent BOLD and CBV responses measured by spin-echo and gradient-echo fMRI: insights into hemodynamic regulation. NeuroImage 30, 1149–1160.

Representation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence.

Visual selectivity for heading in the macaque ventral intraparietal area.

Effective connectivity of depth-structure-selective patches in the lateral bank of the macaque intraparietal sulcus.

Functional magnetic resonance imaging of the lung.

Functional Magnetic Resonance Imaging Methods.

Hue Selectivity in Human Visual Cortex Revealed by Functional Magnetic Resonance Imaging.

Coding of attention across the human intraparietal sulcus.

Reduced Risk-Taking following Disruption of the Intraparietal Sulcus.

Pretherapeutic functional magnetic resonance imaging in children.

Functional magnetic resonance imaging in anaesthesia research.

Functional magnetic resonance imaging and brain functional connectivity in migraine.

Functional magnetic resonance imaging in oncology: state of the art.

Morphological patterns of the intraparietal sulcus and the anterior intermediate parietal sulcus of Jensen in the human brain.

Improving functional magnetic resonance imaging reproducibility.

Functional nanoparticles for magnetic resonance imaging.

Pre-clinical functional Magnetic Resonance Imaging Part I: The kidney.

A case of endometriosis in the macaque diagnosed by nuclear magnetic resonance imaging.

Illusion-related brain activations: a new virtual reality mirror box system for use during functional magnetic resonance imaging.

Mapping the macaque superior temporal sulcus: functional delineation of vergence and version eye-movement-related activity.

Relation of functional echocardiographic parameters to infarct scar transmurality by magnetic resonance imaging.

Functional magnetic resonance imaging in medicine and physiology.

Epileptic kinetopsia localizes to superior parietal lobule and intraparietal sulcus.

Connections between intraparietal sulcus and a sensorimotor network underpin sustained tactile attention.

Functional magnetic resonance imaging (FMRI) neurofeedback: implementations and applications.