fMRI reveals a preference for near viewing in the human parieto-occipital cortex

www.elsevier.com/locate/ynimg NeuroImage 36 (2007) 167 – 187

fMRI reveals a preference for near viewing in the human parieto-occipital cortex D.J. Quinlan a,∗ and J.C. Culham a,b a

Neuroscience Program, Social Science Centre, The University of Western Ontario, London, Ontario, Canada N6A 5C2 Department of Psychology, University of Western Ontario, London, ON, Canada

b

Received 10 August 2006; revised 12 February 2007; accepted 13 February 2007 Available online 28 February 2007 Posterior parietal cortex in primates contains several functional areas associated with visual control of body effectors (e.g., arm, hand and head) which contain neurons tuned to specific depth ranges appropriate for the effector. For example, the macaque ventral intraparietal area (VIP) is involved in head movements and is selective for motion in nearspace around the head. We used functional magnetic resonance imaging to examine activation in the putative human VIP homologue (pVIP), as well as parietal and occipital cortex, as a function of viewing distance when multiple cues to target depth were available (Expt 1) and when only oculomotor cues were available (Expt 2). In Experiment 1, subjects viewed stationary or moving disks presented at three distances (with equal retinal sizes). Although activation in pVIP showed no preference for any particular spatial range, the dorsal parieto-occipital sulcus (dPOS) demonstrated a near-space preference, with activation highest for near viewing, moderate for arm’s length viewing, and lowest for far viewing. In Experiment 2, we investigated whether the near response alone (convergence of the eyes, accommodation of the lens and pupillary constriction) was sufficient to elicit this same activation pattern. Subjects fixated lights presented at three distances which were illuminated singly (with luminance and visual angle equated across distances). dPOS displayed the same gradient of activation (Near > Medium > Far) as that seen in Experiment 1, even with reduced cues to depth. dPOS seems to reflect the status of the near response (perhaps driven largely by vergence angle) and may provide areas in the dorsal visual stream with spatial information useful for guiding actions toward targets in depth. © 2007 Elsevier Inc. All rights reserved.

Introduction Human neuroimaging research has provided a wealth of findings that reflect how the visual world is spatially mapped in the cortex. This research, however, has focused mainly on the retinotopic organization of brain areas, primarily utilizing frontoparallel, two-

* Corresponding author. Fax: +1 519 661 3961. E-mail address: [email protected] (D.J. Quinlan). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2007.02.029

dimensional stimuli. These techniques have shown that the left and right visual fields are represented in opposite hemispheres and that the upper and lower visual fields also have distinct cortical representations (e.g., Previc, 1990, 1998; Skrandies, 1987). Even finer spatial divisions can be observed with retinotopic mapping of occipital cortex (Grill-Spector and Malach, 2004). While these 2D spatial representations can tell us the direction of an object, they do little to tell us the distance of that object. This third dimension, depth, is a neglected area of neuroimaging research that is crucially important to understanding how we guide our actions. Within the third dimension of depth, a critical distinction exists between peripersonal space, the region immediately surrounding the body that can be acted upon and the extrapersonal space that lies beyond it. This distinction has a long history (see Previc, 1998 and Marshall and Fink, 2001 for reviews) and has recently gained credence from a growing number of neuropsychological studies (Halligan and Marshall, 1991; Berti and Frassinetti, 2000; Vuilleumier et al., 1998; Longo and Lourenco, 2006; Bjoertomt et al., 2002). The distinction between peripersonal and extrapersonal space is particularly important because only objects within peripersonal space can be acted upon by the body’s effectors, such as the arm, hand and head. Given that the dorsal visual stream (from occipital to posterior parietal cortex, PPC) plays a particularly important role in the visual guidance of actions (Goodale and Milner, 1992, Milner and Goodale, 1995), peripersonal space may be particularly relevant for cortical areas within the dorsal stream. Moreover, there are numerous regions within PPC that are specialized for guiding actions with a particular effector (see Andersen et al., 1997 for review), and each of these regions may be tuned to the optimal spatial range for that effector (see Iriki et al., 1996). Other regions of the PPC may be specialized for the range of space around other body parts; in particular, within the fundus of the intraparietal sulcus (IPS) of the macaque, the ventral intraparietal (VIP) area appears to be specialized for near peripersonal space around the head and face. Electrophysiological studies conducted by Colby et al. (1993) found that VIP neurons responded to the presentation of moving objects and displayed higher activation when objects were moved near the face than when they were moved further away.

168

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

Given the similarities seen in visual areas across species, one might expect to find a similar region in the human PPC. A putative functional equivalent of macaque VIP (pVIP) has been proposed by Bremmer et al. (2001) who used fMRI to demonstrate polymodal (visual, tactile and auditory) responses in the human intraparietal sulcus (IPS). Preliminary results from our group (Goltz et al., 2001) also found responses in the same vicinity for looming objects. More recently, Sereno and Huang (2006) have reported that the area contains congruent visual and tactile maps for the space around the face similar to those seen in the macaque (Duhamel et al., 1998). These findings suggest that the human parietal cortex contains a region that shares some properties with macaque VIP. However, if this human region is truly a functional equivalent of macaque VIP, it should also display a clear preference for moving stimuli in near vs. far space. We used functional magnetic resonance imaging (fMRI) to investigate whether any areas of the human brain, including pVIP and perhaps other areas within the dorsal stream, would show a stronger response for stimuli in peripersonal space than extrapersonal space. We began by identifying pVIP based on an independent localizer, which contrasted the response to a stimulus looming toward the face against a stationary stimulus (as in Goltz et al., 2001). We then investigated whether this area would demonstrate greater activation for stimuli moving near the head than stimuli moving at farther distances. This was achieved by presenting participants with a disk that moved toward and away from their faces at different distances: one distance was near the face, one distance was just within reach, and one distance was outside of reach. While we were interested in the response pattern in pVIP, we also performed voxelwise contrasts between near motion and far motion to determine whether any functional area(s) outside pVIP would also show a near-space preference. For example, we expected that areas involved in reaching and grasping (for recent reviews see Culham et al., 2006; Culham and Valyear, 2006) might also demonstrate a preference for stimuli within reach over those beyond reach. To our surprise, the results of our first experiment revealed no reliable depth preference in pVIP; however, the voxelwise analyses found a region in the dorsal parieto-occipital sulcus (dPOS) that showed a robust and consistent gradient of activation (Near > Medium > Far) for moving, as well as stationary stimuli. In addition, we observed diffuse activation for near motion more generally within occipital cortex. The preference we observed for stimuli in near-space could have arisen from several available cues to depth. Because our participants looked directly at the looming object, one strong cue to depth was the near response based on oculomotor cues. The near response occurs when subjects gaze at a close object, causing the eyes to rotate inward (convergence), the lens to thicken (accommodation), and the pupils to dilate. These three yoked responses – vergence, accommodation and pupillary diameter – are sometimes referred to as the near triad. In addition to these oculomotor cues, there were also differences in binocular disparity between the stimulus and the background at the three testing distances and these could have also provided a strong cue to depth. We conducted a second experiment to determine whether the near response alone was sufficient to drive the preference seen in dPOS and occipital cortex, in the absence of disparity and monocular cues. Participants simply verged their eyes to maintain fixation on a small spot of light at one of the three distances. In the absence of any other visual stimulation, we found a robust preference for near fixation over medium and far fixation in dPOS and other occipital regions.

Taken together, although we did not observe a near-space preference in the putative human equivalent of macaque VIP, it appears that humans do have a functional area that can reflect object distance based on oculomotor cues alone. As we will discuss, this region may form part of the dorsal stream and provide depth-related information to higher-tier areas involved in the visual control of actions. Experiment 1: near-space preference for moving stimuli Method Participants Eight healthy adult volunteers (two males, six females, ages 23 through 28, mean age = 24.8 years) were recruited as participants and financially compensated. For all participants, visual acuity was normal or corrected-to-normal (with contact lenses) for the spatial ranges being tested. Participants had no known depth perception abnormalities and performed normally on tests of stereoscopic discrimination (with disparity thresholds of 40 arc sec or less on 3D Vectographs by Stereo Optical Co., Inc, Chicago, IL). All participants provided informed written consent and completed a form to ensure they met the safety requirements for fMRI participation. All participants were naive with respect to the experimental hypothesis and were only informed of the required experimental tasks. Setup Participants were placed in a high-field (4 T) fMRI scanner, resting in a semi-reclined position so that their natural line of gaze was directly down the bore of the scanner (toward their feet) without the need for a mirror. To achieve the necessary head tilt, the scanner bed and its track were removed, allowing the participant’s body to lie as low as possible within the scanner bore (on the bore liner). The participant’s head was tilted within a head coil and positioned at magnet isocenter (see Fig. 1A). Participant’s head position and motion were controlled by packing the space between head and coil with foam wedges and small bags of foam beads. Procedure: pVIP localization Our localizer paradigm was based on preliminary data from Goltz et al. (2001), who reported activation in an area they proposed as a likely candidate for pVIP. They hypothesized that a human functional equivalent of VIP would show similar properties as in the macaque (Colby et al., 1993), namely a response to visual motion toward the face, as well as a response to tactile stimulation of the face. They presented participants with a 3D ball that could either remain stationary at a distance of 20 cm from the face or could loom toward the face and then recede to the starting position (20 cm away). They found a region in the human IPS that was more activated during three motion conditions than during the stationary control condition: (a) motion toward the face without contact; (b) motion toward the face with gentle contact on the left cheek; (c) motion with contact in complete darkness. All three conditions yielded more activity than stationary presentation of the ball. Activation for (a) and (b) was higher than that for (c). The coordinates of their IPS region was in the vicinity of (but not identical to) those reported as pVIP by Bremmer et al. (2001) during their investigation of polymodal sensory processing.

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

169

(namely the cheek, chin and lips). Participants were instructed to keep their eyes on the ball throughout all conditions. The localizer paradigm consisted of 21 epochs, each lasting 16 s, starting with a stationary state and alternating with either a touch or no-touch motion state.

Fig. 1. (A) Subjects rested in a semi-reclined position with their head tilted such that they could direct their gaze down the bore of the scanner. Patterned disks (2, 5, and 11 cm) were presented to subjects at three size-dependent distances (15, 38 and 84 cm) so that each disk subtended the same visual angle when stationary (7.5°), as well as when looming and receding (near point = 8.6°; far point = 6.6°). (B) Each disk was mounted to a pole and pole handle, which as a whole, rested in a pole brace equipped with an “aiming” gimbal. This apparatus allowed the trajectory of disk movement to be directed toward and away from the point between the subjects' eyes. Restraint collars fitted to the pole handles controlled how far or near a disk could travel, thereby controlling maximum and minimum disk visual angle. (C) Ten to fifteen oblique slices, approximately parallel to the calcarine sulcus, were used during functional scanning. The area of cortex common to all subjects is highlighted within the red box.

Because our goal was simply to localize pVIP and to do so as quickly and efficiently as possible, we compared only the most effective conditions from Goltz et al. (2001). We contrasted two conditions with looming-and-receding motion (at 0.5 Hz) against a condition in which the ball remained stationary (at 20 cm distance). In one motion condition, the ball moved toward and away from the participant’s face without touching it; in the other motion condition, the ball gently touched the lower half of the participant’s face

Procedure: near-space preference for moving stimuli After the localizer had been used to identify pVIP, we then examined responses to looming and receding motion at three distances: near the face, near the hand and outside of reach. We hypothesized that human pVIP (Bremmer et al., 2001; Goltz et al., 2001), like macaque VIP (Colby et al., 1993), would show the strongest response to stimuli moving near the face. For this first experiment, we decided that viewing conditions should be as natural as possible and should include the full range of possible depth cues. If a near-space preference was observed under these natural viewing conditions, we planned to perform control experiments to determine the role of individual depth cues, such as the near response or binocular disparity, on depth-specific responses. Indeed, in Experiment 2, we performed such a control experiment. Participants viewed disks of differing size that either remained stationary or loomed-and-receded (Fig. 1A). Three disks (2, 5, and 11 cm in diameter) were presented at size-specific distances from the participant’s face, such that all three disks subtended the same visual angle when the object remained stationary (7.5°) and the same range of visual angles as each disk moved (6.6–8.6°). During the loomingand-receding motion, the frequency of oscillation (0.5 Hz) was constant across the three distances. Because disk size was calibrated to presentation distance, the rate of expansion and contraction (in degrees of visual angle per second) was also equivalent between conditions. The nearest viewing distance (movement range = 13– 17 cm) from the face was chosen to be as near to the face as possible while still allowing the eyes to properly accommodate (i.e., remain in focus) over long periods, the medium distance (33–43 cm) to be just within reach, and the far distance (73–95 cm) to be outside of reach. The stationary position of each disk was halfway along these ranges of motion. To maximize salience, each disk was decorated with black “polka dots” (1/10 of disk size) on a white background, also equated for viewing distance, such that the disk patterns would be visually identical on the retina. The central dot of all disks was red and served as a fixation point. All other areas and equipment within the interior of the fMRI scanner were painted flat black or covered with black material in order to minimize visible textures and contours. The interior was also diffusely illuminated such that all three disks were equiluminant to one another. Each disk was mounted at the end of a pole, which could be moved toward and away from the participant’s face by the experimenter (see Fig. 1B). To ensure that disk motion would be consistent between conditions, the poles were supported by a custom-built brace that was firmly attached to the scanner bore. This brace ensured that each pole would move along a consistent trajectory toward and away from the same point on the participant’s face. Each pole was fitted with restraints to control the minimum and maximum disk distance from the participant. The pole brace could be attached at any point along the bore liner so that disk distance from the participant’s face could be set for each individual and would be consistent across participants. The pole brace was adjustable, allowing the movement trajectory of the stimuli to be aimed toward a point between the participant’s eyes. This aiming process ensured that, when participants visually tracked the looming/receding disks, that the eyes would make strictly vergence-based movements

170

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

without any changes in elevation when switching from one viewing distance to another. Black cloth was draped behind the pole brace such that participants could not see any movements made by the experimenter during the experiment. Great lengths were taken to ensure that all visual aspects of the stimuli, presentation and environment were equivalent across viewing distances. To maintain a consistent level of attention across all presentation distances (e.g., Huk and Heeger, 2000), participants were required to respond with a key press whenever they detected a small rotation of the disk (< 5°) which occurred unpredictably every 3–5 s. By monitoring participants’ performance level on this detection task, we could determine whether they were visually tracking the presented disks and therefore engaging their attention equally across presentation distances. Each testing acquisition consisted of 29 epochs, lasting 16 s each, starting and ending with a “baseline” condition in which there was no motion. Participants had one of the three disks present during baseline epochs to serve as a fixation point in order to maintain stable vergence eye position. After the initial baseline epoch, the presentation disk remained stationary (stationary state) for one epoch and was followed by an epoch of motion toward and away from the participant’s face (motion state). At the end of the motion state, the experimenter removed one disk and replaced it with another (Switch state), a process which took approximately 6 s, though we allowed 16 s to ensure the BOLD response would return to a stable level before the next state began. This “stationary/ motion/switch” cycle was repeated for each of the three disk sizes, in pseudo-random order by size, until each disk size was presented three times during a given acquisition. This object-viewing paradigm was repeated utilizing different quasi-randomized presentation orders until at least four acquisitions were obtained per participant.

were screened for head movements using a three-step method. First, we inspected 2D cineloop movies and motion correction parameters of our data for any visible signs of abrupt or large movements (>1° rotation and/or 1 mm translation). If movement was observed, we removed the trials in which the movement occurred from the original uncorrected data set. We also inspected the statistical maps from each individual subject, and from the group analyses, to ensure that there was no evidence of residual motion artifacts, which are typically observed as rims of activation at tissue boundaries and the edges of the brain or activation foci in implausible locations (e.g., ventricles, white matter, outside the head). We chose to use the screened and truncated uncorrected data for our analysis based on the finding that the application of motion correction to motion-free functional data can actual result in poorer data quality (Freire and Mangin, 2001). This problem may be particularly salient at high field strengths, such as our 4 T magnet, where artifacts of mass motion (including motions other than the head, e.g., respiration, bodily movements) can mislead motion correction algorithms (Culham, 2006). We realize that some have shown that even small movements (< 1 mm and < 1°) with modest correlation to the testing paradigm can cause false activations (Field et al., 2000), however these activations appear to occur at the boundaries between “tissue types” (see Fig. 6, Field et al., 2000) and we saw no evidence of such problems even within single subjects. Prior to analysis, time courses within each voxel underwent linear trend removal to eliminate signal drift. Each participant’s functional data were aligned with their anatomical image and transformed into standard stereotaxic space (Talairach and Tournoux, 1988).

Data acquisition Functional magnetic resonance imaging (fMRI) was used to measure the blood-oxygenation level dependent (BOLD) signal, which provides a measure of local neural activity (Kwong et al., 1992, Ogawa et al., 1992). We used a 4 T Varian-Siemens (Palo Alto, CA; Erlangen, Germany) UNITY INOVA whole-body imaging system with a full-head radio frequency (RF) coil (inner diameter of 265 mm) for transmission and reception. Given the head tilt, only the posterior two-thirds of brain could be imaged with clear signal in all participants; the anterior frontal cortex was too far from the center of the coil to provide a useable signal. Oblique slices (10–15 slices, approximately parallel to the calcarine sulcus) were selected to sample parietal, occipital, posterior frontal and posterior temporal cortex (see Fig. 1C for coverage) with a slice thickness of 6 mm and an in-plane voxel resolution of 3 mm. Functional data were collected using T2*weighted segmented gradient echo echoplanar imaging to sample the entire 10–15 slice volume every 2.0 s, using a time to repeat (TR) of 500 ms with 4 segments/plane [time to echo (TE) = 15 ms, flip angle = 30°, navigator-echo-corrected]. For anatomical coregistration, high-resolution T1-weighted scans were collected at the same slice orientation but at higher resolution (0.75 * 0.75 * 3 mm) [TR = 12 ms, TE = 6 ms, flip angle = 180°] and over the full extent of the cerebral hemispheres.

The results of this experiment show that the region identified as pVIP, while showing clear motion selectivity, did not exhibit the near-space preference expected of a macaque VIP functional homologue. Voxelwise analyses on both individual and group data also failed to identify any parietal regions with a preference for near-space. However, we did identify a region at the dorsal end of the parieto-occipital sulcus (dPOS) that displayed a clear preference for objects in near-space, regardless of whether moving or stationary.

Data analysis Data were analyzed using Brain Voyager 2000 Version 4.9 (Brain Innovation, Maastricht, The Netherlands). Functional scans

Results: Experiment 1

Localization of putative ventral intraparietal area (pVIP) Localizer acquisitions were used to identify pVIP within each individual participant based on a greater response during motion toward and away from the face than during stationary presentation. Motion-related activation was computed using a linear correlation between the time course in each voxel and a function derived by convolving the hemodynamic response function (HRF) with a square wave function that had higher activation in motion states (with and without touch) than the stationary states. We raised the threshold to identify the hot spots of activation within the IPS (using values at least as conservative as p < 10− 5). This threshold led to a reliable cluster in the anterior IPS that was at least 350 mm3 in each subject (see Fig. 2A). This level is highly unlikely to be significant due to chance alone based on Monte Carlo simulations performed with AlphaSim software, courtesy of Douglas Ward, Medical College of Wisconsin. The average Talairach coordinates of this focus (Talairach coordinates: left, X = − 27 ± 2, Y= −52 ± 3, Z = 50 ±

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

171

Fig. 2. (A) Location of pVIP as defined by near-space motion-related localizer trials (representative subject). (B) Average (from all subjects) pVIP % BOLD signal time course collected during the stationary, moving and switch states for each of the three presentation distances. The average activation level for the Stationary state (last 6 volumes of state used to compute the average; pink dashed line) and Moving state (last 4 volumes used to compute average; yellow dashed line) were calculated for statistical analysis. (C) % BOLD signal for near, medium and far presentation distances during the stationary and moving conditions (averaged from all subjects).

1 mm; right, X = 26 ± 2, Y= − 50 ± 2, Z = 52 ± 1 mm) were very close to the site identified in the study by Goltz et al. (2001) who tested subjects on the left side of their face, resulting in unilateral right hemisphere activity (Talairach coordinates: X = 27 ± 2, Y= −53 ± 5, Z = 42 ± 11). The coordinates both from the present study and from Goltz et al. were somewhat more medial, superior and posterior to the Talairach coordinates from a prior study by Bremmer et al. (2001): X = ± 39, Y= −42, Z = 44. Our pVIP region was present bilaterally in 7 of 8 participants and unilaterally in the left hemisphere of the eighth participant. In all participants, the activation was in the anterior IPS, just posterior to the superior postcentral sulcus. This location was consistently posterior to an activated region within the postcentral gyrus, which was primarily activated by the motion with touch condition and therefore is likely within somatosensory cortex. However, within pVIP, there was no significant difference between the “motion with touch” and “motion without touch” states (paired t(6) = 1.690, p = .142), suggesting that its response could be driven by vision alone.

Region of interest (ROI) analysis: evaluation of pVIP activation at varying distances Once pVIP was identified, we could then investigate how its activity changed in response to the presentation of moving and stationary disks at different distances. For each individual participant, we extracted the average event-related activation time course (in units of percent of BOLD signal change). In each subject, activation was higher during moving states than stationary states (see Fig. 2B). For each distance, we then computed the average sustained activation in the stationary state (removing the first two volumes where hemodynamic rise occurs) and the average of the last four volumes of the motion state, where activation was stable. These average activation levels were then subjected to a repeated-measures analysis of variance (ANOVA) using two within-participant variables: Motion State (Stationary versus Moving) and Disk Presentation Distance (Near, Medium and Far). Although pVIP was motion-selective, it did not exhibit the near-space preference expected of a macaque VIP functional homologue (see Fig. 2C). The moving state exhibited a higher average BOLD signal than the stationary state (F(1,7) = 11.044, p < 0.05), but there was no significant effect of Disk Presentation Distance (F(2,12) = 0.551, p = 0.59) as would be expected of a

functional VIP equivalent. Furthermore, there was no significant interaction between Motion State and Disk Presentation Distance (F(2,14) = 1.269, p = 0.312).

Depth-specific responses in other brain regions To identify additional areas with a preference for near-space, we also conducted exploratory analyses using voxelwise contrasts within a general linear model (GLM) for individual subjects and for the group data averaged in stereotaxic space. These GLMs were composed of predictors for each of the presentation states, formed by a square wave function convolved by the standard HRF. In a GLM, at least one condition must be declared as a “baseline” or the model will be over-determined; however, the specific condition selected to be the baseline is not critical to the outcome. We arbitrarily selected the Medium-Stationary Condition to be the baseline and generated predictor functions for the five other conditions (Near-Stationary, Far-Stationary, Near-Moving, Medium-Moving and Far-Moving). Voxelwise contrasts in individual subjects A contrast between Near-Moving vs. Far-Moving state in individual subjects did not identify any near-space preferences in the expected vicinity of pVIP or indeed anywhere within parietal cortex. However, in 7 of 8 participants it identified an area of activation located in the occipital lobe, at the dorsal end of the parieto-occipital sulcus, an area we refer to as dPOS (see Fig. 3— left and Table 1) [Talairach coordinates: left, X = − 8 ± 1, Y= − 83 ± 2, Z = 27 ± 2; right, X = 9 ± 3, Y= − 83 ± 2, Z = 31 ± 3]. Each participant’s dPOS was identified using p values at least as conservative as p < 10− 5. This threshold produced an activation cluster size of at least 1000 mm3 in each subject. Again, Monte Carlo simulations indicated that activation clusters of this size were highly unlikely to occur by chance at this statistical threshold. At more liberal thresholds, diffuse activation was observed throughout the medial occipital cortex. However, unlike dPOS, in which the activation was highly consistent in its anatomical location with respect to sulcal landmarks, the diffuse occipital activation was considerably more variable across subjects (and will be discussed further after the second experiment using more controlled stimuli). The BOLD signal time course for area dPOS showed several consistent features within individuals (Fig. 3—right) and for the

172

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

average response across all subjects (Fig. 4A). First, dPOS activation is consistently higher when looking at objects in near-space than when looking at objects in medium- and far-space, regardless of whether the stimuli were stationary or moving. Second, there is higher activation throughout the moving condition compared to the stationary condition, regardless of viewing distance. Third, at the

Table 1 Areas significantly active for comparisons of Near-moving vs. Far-moving in Experiment 1 and Near vs. Far Fixation in Experiment 2 (both for individual and group voxelwise comparisons) Area Expt 1 Individual data pVIP Left Right dPOS Left Right Group data dPOS Expt 2 Individual data dPOS Left Right Group data dPOS Right posterior occipital (superior) Left posterior occipital Right posterior occipital (inferior)

mm3

X

Y

Z

Threshold

− 27 ± 2 26 ± 2

− 52 ± 3 − 50 ± 2

50 ± 1 52 ± 1

p < 10− 5

−8±1 9±3

− 83 ± 2 − 83 ± 2

27 ± 2 31 ± 3

p < 10− 5

>1000

−2

− 77

21

p < 10− 12

7525

−8±1 12 ± 1

− 86 ± 2 − 87 ± 2

28 ± 4 33 ± 2

p < 10− 4

>600

0 12

− 77 − 91

25 17

p < 10− 12 p < 10− 12

488 262

− 14

− 92

19

p < 10− 12

112

20

− 92

2

p < 10− 12

421

>350

onset of the moving state the activation rises sharply, peaking at approximately 4–6 s into the moving state, then subsides slightly while maintaining a sustained plateau of activation through the latter half of the moving condition. Notice that this peak characteristic is strongest for the near-space condition, becoming progressively less pronounced with distance. Lastly, note that the highest activation occurs during the Switch state, immediately following the moving condition. To verify the significance of these observations, we extracted the % BOLD signal change from each subject’s data for the stationary condition (last 6 data points averaged), the peak response of the moving condition and the sustained portion within the moving state (last 4 data points averaged, Fig. 4). These data were subjected to a 3 × 3 repeated-measures analysis of variance (ANOVA). While there were significant main effects of both motion state (F(2,14) = 63.1, p < .001) and viewing distance (F(2,14) = 122.51, p < .001), a significant interaction suggested a more complicated relationship (F(4,28) = 13.63, p < .001) that required further investigation. We performed contrasts between certain pairs of conditions to determine the nature of the interaction and to test a priori hypotheses about the effects of distance on different phases of the fMRI response. Even though a spatial preference was not found in pVIP, we expected that a near-space preference within the dorsal Fig. 3. The left column displays dPOS activation (circled in red) of all subjects individually. For clarity, the parieto-occipital sulcus has been identified in each subject with a black dashed line. We performed intersession alignment of subject's current data to high-resolution anatomical scans if available (subjects A and D). The right column displays the average % BOLD signal time course for dPOS in each subject. As in Fig. 2, the dashed pink and yellow lines indicate the period used for computation activation levels for the stationary state and the sustained phase of the moving state, respectively.

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

visual pathway would be most pronounced for moving stimuli. Thus, we examined activation differences between distances (Near vs. Medium vs. Far) for the responses during the Stationary phase, the peak of the Moving phase and the sustained plateau of the

173

Moving phase (see Figs. 4B and 4C). Although typical error bars based on the standard of the mean show the variability of subjects’ activation levels, in a repeated-measures design, intersubject variability of differences between conditions is more relevant (Loftus and Masson, 1994). Moreover, error bars are often misunderstood by researchers (Belia et al., 2005; Cumming and Finch, 2005) and error bars based on repeated-measures variability can be complex to display and understand (Masson and Loftus, 2003). Thus, as requested by a reviewer, to illustrate the magnitude of differences between conditions and their significance, we have plotted the average difference scores and their confidence intervals (99.44%). These confidence intervals have been Bonferroni corrected for a number of comparisons (9) such that if the error bars do not include zero, the difference is significant at p < .05 (as determined by post hoc paired t-tests with 7 degrees of freedom). In general, activation during Near-space presentations was significantly higher than both Medium and Far presentations in all conditions (Stationary, Peak Moving and Sustained Moving; Fig. 4C). Activation during Medium-space presentations was significantly higher than Far-space presentations for the Peak Moving and Sustained Moving conditions, but not for the Stationary condition. The Motion × Distance interaction identified by the repeated-measures ANOVA is likely related to the fact that the differences between distances were greater for the Sustained Moving and especially the Peak Moving conditions compared to the Stationary condition. Given prior evidence that dPOS responds to transients (Hari and Salmelin, 1997; Portin et al., 1998), we also expected that dPOS may display higher activation during the Moving phase in comparison to the Stationary phase for each distance tested. However, based on our observations of the average time course (Fig. 4A), we concluded that it could be very important to test for Fig. 4. (A) Average % BOLD signal time course of area dPOS (calculated from all subjects). The average activation level for the Stationary state (last 6 volumes of state used to compute the average; pink dashed line), the Peak Moving state (highest signal reported during moving state) and Sustained Moving state (last 4 volumes of moving state used to compute average; yellow dashed line) were calculated for statistical analysis. (B) dPOS activation during the Stationary, Peak Moving and Sustained Moving portions for each presentation distance (averaged across all subjects). (C) The differences between the combinations of Near, Medium and Far viewing distances for the Stationary, Sustained Moving and Peak Moving states (plotted with 99.44% confidence interval bars, which equates to p = .05 when corrected for multiple comparisons) to illustrate the results of the post hoc analyses. The p values for the post hoc analyses are included above each respective confidence interval bar. Post hoc results for the Stationary phase: Near > Medium (t(7) = 6.40, p < .01), Medium = Far (t(7) = 1.56, p = .50) and Near > Far (t(7) = 7.42, p < .001); for the Sustained Moving phase: Near > Medium (t(7) = 6.96, p < .01), Medium > Far (t(7) = 4.78, p < .01) and Near > Far (t(7) = 10.46, p < .001); and for the Peak Moving phase: Near > Medium (t(7) = 8.64, p < .001), Medium > Far (t(7) = 6.71, p < .01) and Near > Far (t(7) = 13.02, p < .001). (D) Differences between Sustained Moving vs. Stationary and Peak Moving vs. Sustained Moving for each of the three viewing distances (plotted with 99.17% confidence interval bars, which equates to p = .05 when corrected for multiple comparisons). The p values for the post hoc analyses are included above each respective confidence interval bar. Post hoc results for the Near distance: Sustained Moving > Stationary (t(7) = 5.92, p < .01) and Peak Moving > Sustained Moving (t(7) = 7.78, p < .001); the Medium distance: Sustained Moving > Stationary (t(7) = 5.78, p < .01) and Peak Moving = Sustained Moving (t(7) = 2.90, p = .071); and for the Far distance: Sustained Moving > Stationary (t(7) = 5.44, p < .01) and Peak Moving = Sustained Moving (t(7) = 1.65, p < .42).

174

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

differences between the two phases of the Moving state, the transient peak response and the sustained plateau. Based on these a priori hypotheses, we evaluated activation differences between each phase of the time course (Stationary, Peak Moving and Sustained Moving) at each viewing distance (Near, Medium and Far). Again, these results are summarized as difference plots in Fig. 4D, showing the 99.17% confidence interval bars, which equate to p = .05 when corrected for multiple comparisons (6). Activation in the Sustained Moving condition was higher than in the Stationary condition for all presentation distances. However, the Peak Moving condition was significantly higher than the Sustained Moving condition only for the Near distance. The greater differences among conditions for the nearer presentation distances likely contribute to the significant interaction between these factors. Although the Switch state was not a major theoretical focus of the experiment, it clearly yielded the strongest response in dPOS, as well as in pVIP. We believe this was due to a much greater range of motion (and thus also vergence changes if the eyes follow the stimulus) in the Switch phase (with the removal of one stimulus and replacement with another) than the Moving phase. Note that the peak of the switch response was comparable in magnitude for switches for each of the three initial distances (F(2,14) = 0.19, p = .83). However, the time of the peak response occurred earlier for the switches from the Near distance (to Medium and Far distances) than for the switch from Medium or Far to the other distances (F (2,14) = 11.49, p < .01, with a significant linear trend to earlier responses in transition from nearer distance, p < .01). Presumably, this is because the largest movement (removal of the near stimulus) occurred at the beginning of the transition from Near (to Far or Medium) and at the end of the transition from the other two states (which were followed by the insertion of the near stimulus on half of the trials). Voxelwise analysis of group data We also conducted a voxelwise analysis of group data to examine whether any additional areas were reliably activated by stimuli in near-space. Individual subjects’ data were normalized in stereotaxic space (Talairach and Tournoux, 1988) and a group GLM was generated using separate predictors for each subject (using fixed effects because the sample size was less than the recommended ten or more subjects for random effects analyses). A contrast of the Near-Moving state with the Far-Moving state produced a cluster of activation in the anterior cuneus (the medial division of the occipital lobe posterior to the parieto-occipital sulcus and superior to the calcarine sulcus), as shown in Fig. 5 (left) and Table 1 (Talairach coordinates: X = − 2, Y= − 77, Z = 21). This cluster included dPOS but also extended inferior and posterior to it. The time course of activation in this large cluster (t(8160) =

7.2, p < 10− 12) confirmed a gradient of activation dependent upon the distance of the stimulus from the participant (see Fig. 5—right) and showed a pattern comparable to that observed in dPOS from the individual subjects. Although the voxelwise analyses for the group confirmed the results from individuals, we believe that the individual data are more informative. We found that the location of the parietooccipital sulcus in Talairach space was highly variable between subjects. Thus even though our dPOS region was clearly present in 7 of 8 subjects, the region of overlap in the group analyses tended to fall more along the midline of the cortex, rather than the more lateral location suggested in individuals (Talairach: X = − 2 vs. X = − 8 ± 1 and 9 ± 3, respectively). Behavioral performance Subjects performed well (85% correct or better) at identifying the rotation of the disks in all conditions. Planned comparisons between distances revealed a significant difference in accuracy between the Near-Moving and Far-Moving conditions but not between any other pairs of conditions. Thus it appears unlikely that the response observed in dPOS is merely the result of attentional factors, a suggestion that is further bolstered by the absence of distance selectivity in any other areas that are known to be modulated by attention, including the middle temporal motion complex, MT+ (O’Craven, 1997; Huk and Heeger, 2000) and a range of areas within parietal cortex (e.g., Wojciulik and Kanwisher, 1999). Summary of Experiment 1 results In summary, we observed that dPOS had: (1) higher activation for Near viewing than for Medium and Far viewing across all phases of the time course (Near > Medium in all phases of the time course and Near > Far in both phases of the Moving condition); (2) higher activation for the sustained phase of the Moving condition compared to the Stationary condition at all distances; (3) higher activation during the peak phase compared to the sustained phase of the Moving condition only at the Near distance; and (4) high activation during the Switch state, likely because of the large amplitude of motion during the removal and insertion of stimuli (larger movements than in the actual experimental manipulation). Discussion: Experiment 1 Absence of a near-space preference in pVIP We were surprised not to observe a near-space preference within putative VIP, either with a localizer-based or voxelwise approach.

Fig. 5. Left: activation cluster resulting from a voxelwise group analysis contrasting Near-Moving vs. Far-Moving in Experiment 1. For clarity, the parietooccipital sulcus has been identified with a dashed black line in the sagittal view. Right: activation in the area identified by voxelwise group analysis.

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

There are several possible explanations for the absence of the expected response based on the nature of the population response in fMRI, the methodology we employed and the challenges of proposing interspecies relationships regarding brain areas. There may be difficulties in generalizing from the responses of single units in electrophysiology to the population response measured by fMRI. Of the macaque VIP neurons that were tested for depth tuning (Colby et al., 1993), slightly more than half were distance-selective (22/41, 54%) and the majority of those (14/41, 34% of total) displayed optimal responses at a distance of 5 cm, with fewer neurons preferring objects within 20 cm (6/41, 15% of total) or beyond (2/41, 5% of total). However, not all VIP neurons in this study were tested for depth selectivity. The subsets that were investigated for depth tuning were selected because they were not strongly responsive at the standard distance of 57 cm (C. Colby, personal communication, June 2006). Thus the proportion of total neurons tuned to near-space (< 20 cm) may have been considerably less than half. It is likely that such a small proportion of near-tuned neurons is not large enough to yield a significant difference in fMRI at the population level (see Scannell and Young, 1999 for an extended discussion about the challenges of comparing single-unit and population measures of activity). It is also possible that we would have gotten a near-specific result if our closest distance had been shorter (e.g., < 5 cm, rather than the 13–17 cm we used). However, nearer objects would have had many confounds that would have made any activation differences difficult to interpret (e.g., lack of vergence and focus, diplopia). Thus we chose the near range to be as close as possible while avoiding such confounds and still remaining within 20 cm, where the majority of macaque VIP neurons demonstrated tuning (Colby et al., 1993). Other methodological factors specific to our testing paradigm may have reduced the likelihood of finding a near-space preference in human pVIP. First, we employed looming–receding motion instead of the lateral (frontoparallel) motion used in the original VIP study (Colby et al., 1993). It is possible that large lateral motion would have recruited more neurons in pVIP, but given that VIP neurons appear to be even more strongly activated by looming motion than lateral motion (Colby et al., 1993), this is unlikely. Second, in our display, the objects always followed a consistent trajectory and never actually touched the subject (except in the localizer). It may be that subjects quickly learned that the looming objects would never contact them (although macaque VIP neurons responded even without contact, Colby et al., Fig. 12A). Third, brain areas involved in coding motion toward the head may have quickly adapted to the repeated trajectory. fMRI adaptation to repeated motion stimuli has been observed in other motion-selective areas (see review by Krekelberg et al., 2006). Fourth, the subjects never prepared any action in response to the target – they were unable to move their heads and never reached toward or grasped the targets – and distance-selective areas in the dorsal stream may be most activated when the potential for action exists. One neuroimaging study that had subjects using a laser pointer to perform a line bisection task in near vs. far space reported activation in the anterior parietal cortex, in the vicinity of pVIP (Weiss et al., 2000) as defined by Bremmer et al. (2001). However, the “near” distance employed in that study was still quite far (70 vs. 170 cm), beyond the preferred range expected in a VIP equivalent. Moreover, the coordinates of pVIP overlap with those for a grasping-related area in the anterior intraparietal sulcus, pAIP (Culham et al., 2006), and the area in question may be more concerned with stimuli in reachable space than ultra-near-space. Consistent with this sugges-

175

tion, a recent paper has also found activation in the vicinity of both pVIP and pAIP for motion near the hand compared to motion far from the hand (Makin et al., 2007). In that study, subjects had to judge whether the moving target would hit a stationary landmark. Considering both of these studies (Weiss et al., 2000; Makin et al., 2007), it may be that pVIP prefers near-space only when the subject is performing an action or an attentionally demanding task. Another possibility, of course, is that the area proposed as human pVIP is not in fact functionally equivalent to macaque VIP. To date, the most compelling evidence for human VIP comes from its multimodal responses (Bremmer et al., 2001) and congruent visual– tactile maps (Sereno and Huang, 2006); however, other parietal areas may share these properties or the “functional equivalence” may be incomplete. Interspecies homologies or functional equivalents are uncertain even for lower-tier visual areas (such as V3 and V3A; Tootell et al., 1997). The relationships become even trickier to establish for higher-order association areas such as those within parietal cortex (see discussion in Culham et al., 2006). Near-space preference in dPOS Our results demonstrate that dPOS seems to reflect the object distance from the viewer. A number of possible explanations for this finding exist. First, dPOS could be driven by stimuli in near-space, regardless of the particular cues that are used to determine depth. Second, dPOS could be driven by just one of the available cues to depth, namely monocular cues, binocular disparity or the near response. Below we review the possibility of each of these sources contributing to the near-space preference seen in dPOS. Binocular disparity In our experiment, subjects viewed the stimuli with both eyes and thus had binocular disparity information. Although the subject, bore liner and equipment were covered in black material or paint, subjects could still detect edges at the end of the bore. Therefore, the amount of disparity for those edges differed with viewing distance such that there was near zero disparity for far viewing and increasing disparity as viewing distance became closer to the subject’s face. This disparity gradient may seem a plausible explanation for our results, however it is not likely. Disparity as a cue to depth does not appear to be useful when disparity is too great, such as during our medium and near viewing conditions. When subjects gazed at the near or medium stimulus, they would have had a very large degree of disparity for features in the background (specifically, very large uncrossed disparities), which may have provided cues that the stimulus was in front of the background, but without a sense of how far in front. Near response: vergence, accommodation, and pupil size Given that subjects were gazing at and visually tracking the stimuli as they moved in depth, the near response could serve as a powerful cue to object distance. The three components of the near response, vergence, accommodation and pupil size, are strongly linked to viewing distance and have a reciprocal relationship that ensures that retinal images of a fixated object are in clear, sharp focus, with zero disparity. Within the near response, vergence appears to be a much stronger cue to object distance than accommodation or pupil size (e.g., Swenson, 1932; Heinemann et al., 1959; Kunnapas, 1968; Foley, 1980; Morrison and Whiteside, 1984).

176

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

Based on these considerations presented above, the near-space preference we observed in dPOS is unlikely the result of binocular disparity, but most likely depends on the near response, particularly vergence. We conducted two pilot experiments to test the likelihood of each of these possible explanations. The first of these pilot studies investigated whether differences in disparity could elicit a preference for distance. In this experiment, three of the original subjects repeated Experiment 1 while maintaining fixation on a light emitting diode (LED) that was adjacent either the near or far disk. If dPOS showed a preference for near objects regardless of vergence distance, the same pattern would be observed with Near and Far Fixation (Near > Medium > Far). In contrast, if dPOS encoded the amplitude of binocular disparity, we expected the highest activation for the stimulus furthest from fixation (i.e., Far > Medium > Near with Near Fixation and Near > Medium > Far with Far Fixation). We inspected the individual data from each of the three pilot subjects and found no clear evidence for either of these hypotheses in any of the subjects. Given the lack of any reliable trend, we did not test any further subjects. A second pilot experiment suggested that dPOS activity was higher when subjects fixated a near vs. far point, such that only oculomotor cues to depth were available. The results of a second pilot experiment were considerably more promising and thus formed the basis for Experiment 2, which confirmed the initial suggestion, as described in greater detail below. In summary, we believe that the results of Experiment 1 can be largely accounted for by the near response (as will be seen from Experiment 2). We acknowledge that object distance or disparity effects may contribute; however, the pilot data from three subjects were not sufficiently promising to justify the expense to conclusively rule out these influences. Experiment 2: near viewing preference—vergence eye position and accommodation The results of Experiment 1 show that not only is dPOS capable of representing the distance of a moving object, similar to macaque VIP, but can also discriminate distance when objects remain stationary. Based on the results of our pilot experiment on three subjects, the near-space preference in dPOS is likely to be driven by the near response rather than by binocular disparity, monocular cues or object distance alone. To test this hypothesis more rigorously, we designed an experiment in which subjects simply maintained vergence on small targets (LEDs) at each of the three distances in otherwise total darkness, so that other cues to depth such as interposition and disparity were completely removed. Method Participants Eight healthy adult volunteers (four males, four females, ages 23 through 40, mean age = 29.1 years) participated in this experiment. Participants met the same inclusion criteria and underwent the same consent and screening procedures as in Experiment 1. Setup All participants were placed in the scanner in the supine position (i.e., no head tilt) with their head placed in a quadrature surface coil. Participant head movement was restrained by use of a comfortable, foam-lined, head vise. Participants viewed fixation LEDs through a mirror, such that the image of the LEDs appeared directly in front of the subject’s face (Fig. 6A), along their natural

Fig. 6. (A) Subjects rested in a supine position with their head fixed in place by a quadrature coil equipped with a foam-lined head vise to minimize head movement. This coil was also equipped with a mirror, allowing subject to direct their gaze toward 3 LEDs that were positioned behind their head at three different distances (15, 26 and 84 cm). LEDs were arranged vertically with respect to one another such that only pure horizontal eye movements were necessary when changing fixation distances. LEDs were viewed in darkness and LED brightness levels were set so that only the LEDs themselves were visible. (B) As in Experiment 1, 10–15 oblique slices, approximately parallel to the calcarine sulcus, were used during functional scanning. The area of cortex common to all subjects is highlighted within the red box.

line of gaze. The position of this mirror was set so that subjects could not see either their forehead or nose, images that could introduce disparity cues. We used the same scan parameters as Experiment 1 except that a quadrature surface coil was used for both transmission and reception of the RF signal. We opted to use a quadrature coil in this experiment as it offers a higher signal to noise ratio than the head coil for our region of interest, namely dPOS. Second, this coil is equipped with a mirror which allows participants to lie in a typical scanning posture, while viewing fixation LEDs set at different distances behind their head (see Fig. 6A). The area of cortex imaged in this experiment was similar to that of Experiment 1, even though different coils and body positions were used (see Figs. 1C and 6B for comparison). Procedure The stimuli for this experiment consisted of three fixation light emitting diodes (LEDs) placed at three distances from the eyes. The retinal size (LEDs subtended ~ 0.7°) and luminance of these LEDs were equated across distances such that they were indistinguishable when viewed monocularly. The near and far LED positions were the same as the stationary positions used in Experiment 1 (~ 15 and 84 cm, respectively). The medium fixation distance, however, was moved from ~ 38 cm to ~ 26 cm, such that the angle of vergence eye

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

movements required to switch from near to medium fixation distance equaled that of movements from medium to far (~ 5°). The LEDs were mounted to the bore liner and their heights were adjusted so that only inward/outward vergence movements were required (without any vertical or saccadic components) (Fig. 6A). All light sources other than the fixation LEDs themselves were extinguished during scanning, such that only the LEDs were visible. This lighting eliminated all possible disparity cues and monocular depth cues from the testing environment. Participants were informed that they were required to maintain fixation on whichever of the LEDs was illuminated and that only one LED would be turned on at any given time. Each testing acquisition consisted of 27 states, each lasting 16 s, starting with a fixation state and ending with an additional 5 volumes. This paradigm was repeated twice utilizing quasi-randomized presentation orders for each participant, resulting in 18 depth fixation periods for each of the three fixation distances. Data analysis Data were treated in the same fashion as in Experiment 1 with respect to software used, movement screening and removal, drift correction and transformation into standard stereotaxic space. Experiment 2, however, did not make use of localization trials as pVIP was no longer a focus of our investigation. Results: Experiment 2 We performed voxelwise contrasts both for individual subjects and for group averaged data in Talairach space. Both approaches show that area dPOS clearly demonstrates an activation gradient based on the oculomotor signals associated with fixating objects at different distances (Near > Medium > Far). In addition, a similar pattern was observed in more diffuse regions of occipital cortex. Voxelwise contrasts in individual subjects To identify areas with a preference for fixating in near-space, our analyses used contrasts within a voxelwise general linear model (GLM) for each participant. Exploratory analyses suggested that it might be important to distinguish between the peak response and sustained plateau. Therefore, in the GLM, we divided each fixation epoch into half, the first half (first 4 volumes, convolved with the HRF) representing the transient response to the vergence eye movement made at the onset of each fixation epoch, and the second half (last 4 volumes, convolved with the HRF) representing the sustained plateau resulting from static vergence eye position. The GLM was composed of predictors for five of the different states (Near-Transient, Medium-Transient, Far-Transient, Near-Sustained, and Far-Sustained), each formed by a square wave function convolved by the standard HRF. As in Experiment 1, we kept one of the states, Medium-Sustained as the baseline (without a predictor) so that the resulting GLM was not over-determined. The GLM contrast of Near fixation vs. Far fixation resulted in a cluster of activation (at least 600 mm3 with thresholds set to at least p < 10− 4) which had average Talairach coordinates in close agreement with the average individual coordinates for dPOS in Experiment 1 [Talairach coordinates: left, X = −8 ± 1, Y= −86 ± 2, Z = 28 ± 4; right, X = 12 ± 1, Y= − 87 ± 2, Z = 33 ± 2] (see Fig. 7—left and Table 1). The individual voxelwise analyses contrasting Near fixation with Far fixation identified a region of interest (see Fig. 7—left)

177

similar to the dPOS region discovered in Experiment 1 (see Fig. 3— left). Within the average BOLD signal time course (Fig. 8A) for area dPOS in Experiment 2, we observed a transient peak at about 4–6 s following the vergence change, followed by a sustained plateau. Most importantly, note that activation during the sustained portion of the time course was highest when subjects fixated at the near distance, moderate when at the medium distance, and lowest when fixating at the far distance. This gradient of sustained activation, based on fixation distance (Near > Medium > Far), likely reflects the ongoing maintenance of the near response. In contrast, the activation peaks, which appeared comparable in size, likely represent the effects of initial near response (transients from the vergence movement, focusing of the accommodation reflex, and/or luminance changes from the adjustment in pupillary diameter). Again, this pattern of activation was consistent across all subjects (see Fig. 7—right). Because the focus of Experiment 2 was to show that dPOS reflects the ongoing signal from the near response and not merely the transients that occur at its onset, we separately evaluated the transient peak and sustained activity. We used the single highest data point value as the transient peak and averaged the last four data points of the epochs to represent the sustained plateau of activity. We performed a 2 × 3 repeated-measures ANOVA using two withinparticipant variables: Phase (Transient versus Sustained) and Distance (Near, Medium and Far). This analysis revealed a significant difference for both Phase (Peak vs. Sustained; F(1,7) = 38.08, p < .001) and Distance (Near vs. Medium vs. Far; F(2,14) = 14.36, p < .001). A significant Phase × Distance interaction was also identified (F(2,14) = 14.21, p < .001). Based on the distance-specific activation we observed for stationary object presentation in Experiment 1, we hypothesized that dPOS activity in Experiment 2 would also display a preference when the near response was maintained. We also wondered whether the dPOS response in the transient component would be distancespecific. Based on these considerations, we performed planned contrasts on Fixation Distance (Near vs. Medium vs. Far) within both the Peak and Sustained Phases. These results are summarized as difference plots in Fig. 8C. Included are 99.17% confidence interval bars, which equates to p = .05 when corrected for multiple comparisons. In summary, there was a significant gradient of activation in the Sustained phase (Near > Medium > Far), but no significant effects of distance on the Peak activation, explaining the significant interaction between Phase × Distance. Even though the activation differences between the peak phase and the sustained phase of Experiment 2 were not the focus of this experiment, we will include the results of post hoc analyses for the sake of completeness. Figs. 8B and D illustrate that peak activation was significantly higher than that of the sustained portion of the activation time course at all three distances. These results are summarized as difference plots in Fig. 8D, including the 98.33% confidence interval bars, which equate to p = .05 when corrected for multiple comparisons. We also found higher activation during the near response throughout diffuse regions of the medial occipital lobe when more liberal thresholds were used (discussed in voxelwise group analysis below). This pattern had also been observed in Experiment 1, but in Experiment 2, the results are much easier to interpret because many of the potential low-level factors (such as differences in the amount of binocular disparity between viewing distances) had been eliminated. When examined in single subjects, the foci for the other occipital activations were less consistent between subjects

178

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

than dPOS and much more variable in their locations with respect to sulcal landmarks. Voxelwise analysis of group data Again, we performed a group voxelwise analysis using separate predictors for each subject in a fixed effects model to determine whether the patterns observed in single subjects were confirmed and whether any additional areas beyond dPOS were reliably activated. As in the individual subject analyses, the key contrast was between the near vs. far distances during the sustained phase of the response, after the near response had stabilized. At high statistical thresholds (t > 8), only a dPOS focus was observed (this focus was also found with a random effects model with t > 4, despite the fact that random effects typically have too little power when n < 10). At lower thresholds, additional foci appeared (e.g., t(3588) = 7.2, p < 10− 12, see Table 1 and Figs. 9A–E) and at even lower but still acceptable thresholds (e.g., t(3588) = 5.2, p < 10− 6), much of the occipital cortex was activated (Fig. 9B). The time courses for the dPOS focus and the greater occipital activation (Figs. 9A and B) were consistent with those observed in single subjects (Fig. 7—left). In addition to dPOS (Talairach coordinates: X = 0, Y= −77, Z = 25), three key foci were observed at thresholds high enough to isolate activation peaks (t(3588) = 7.2, p < 10− 12, Table 1). These foci included symmetric peaks in the superior–posterior occipital lobe (Talairach coordinates: right, X = 12, Y= − 91, Z = 17, Fig. 9C; left, X = −14, Y= −92, Z = 19, Fig. 9D) and a peak at the right inferior occipital lobe (Talairach coordinates: X = 20, Y= − 92, Z = 2, Fig. 9E). Without retinotopic mapping, it is impossible to determine for certain which specific visual areas are implicated. However, we speculate that the symmetric foci may correspond to superior aspect of visual area V3A, while the right inferior activation may correspond to the inferior aspect of V3A (see Figs. 8 and 9 in Tootell et al., 1997) based on an investigation of intersubject variability of visual areas in Talairach space (Hasnain et al., 1998). Eye movement monitoring of vergence stability Ideally, we would have liked to use a binocular eye tracker during the scanning session to ensure that subjects’ vergence eye position was equally stable across viewing distances. This measure would have allowed us to rule out the possibility that eye position stability differences, namely less stability during near fixation than far fixation, were the cause of our near-space preference. Unfortunately, the only MR-compatible long-range eye-tracking system available to us (MEyeTrack LR, SensoMotoric Instruments, Needham, MA) utilizes a near-infrared light source, which extends into the long wavelength (red) end of the visible spectrum. If this infrared light source were present, it would have been visible to the subject and would have illuminated other features in the scanner bore, thus providing binocular disparity cues, which we wanted to

Fig. 7. The left column shows dPOS activation (circled in red) of all subjects. For clarity, the parieto-occipital sulcus has been identified in each subject with a black dashed line. Although all functional data were collected with a surface coil, we performed intersession alignment of subject's current data to high-resolution, full-brain anatomical scans if available (subjects A–D, F, and G). The right column shows the average % BOLD signal time course for dPOS in each individual subject. The dashed yellow line shows the last 4 volumes from the sustained phase of each epoch which were used to calculate average activation levels (Fig. 7).

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

eliminate. Furthermore, our MR-compatible eye-tracking system is strictly monocular and thus cannot differentiate between vergence movements and conjugate eye movements (for example, if the right

179

eye moved to the left, it could be because the subject’s eyes converged or because the subject made a leftward saccade with both eyes). Even though it has been shown that normal subjects can perform vergence eye movements to a distance of 10 cm, even closer with little practice (see von Noorden, 1996 for review), and that ocular stability is exceptionally stable when that head is fixed in position (Steinman et al., 1982), we chose to verify that fixation stability was consistent between fixation distances. We tested three participants from Experiment 2 outside the MR scanner, monitoring eye position with a binocular head-mounted eye-tracking system (SR Research, Eyelink II). This particular eye-tracking system uses two true infrared illuminators that are not visible in darkness, along with two infrared-sensitive cameras that independently track each eye. Unfortunately, this system is not MR-compatible and could not be used during our scanning sessions and was also located off-site (University of Waterloo), so it was not possible to test all subjects from Experiment 2. Outside the scanner, we measured subjects’ eye movements under similar conditions as those used in Experiment 2. Subjects first performed a calibration trial in which they were required to shift their gaze between locations in a 3 × 3 array in the frontoparallel plane in which adjacent points were separated by 5° of visual angle. This trial was then repeated and compared to the first to ensure reliable accuracy. These calibration trials also enabled us to convert the eye movement data of the testing session into degrees of visual angle. In test trials, three LEDs were positioned at the same distances as in the Experiment 2 fMRI session, and again along the natural line of sight, such that vergence changes did not include a vertical component. Participants were instructed to maintain fixation on whichever of the three LEDs was illuminated at any given time. As in the fMRI session, each test trial consisted of 27 states, each lasting 16 s, and was repeated twice for each participant. This resulted in 18 fixation periods for each of the three fixation distances. By keeping the timing as similar as possible between the control and fMRI sessions, we could be certain that the levels of eye muscle fatigue and gaze stability would be comparable. We quantified eye stability using the variance in eye position. Specifically, we expected the standard deviation in eye position would be lower when subjects maintained stable fixation than when they had difficulty maintaining stable fixation, either because of

Fig. 8. (A) Average % BOLD signal time course of area dPOS (calculated from all subjects). The average activation level for the Peak Phase (highest signal reported during fixation) and the Sustained Phase (last 4 volumes of fixation; yellow dashed line) was calculated for statistical analysis. (B) dPOS activation during the Peak and Sustained Phases for each presentation distance (averaged across all subjects). (C) Differences between the three fixation distances for the peak and sustained phase of activation plotted with 99.17% confidence interval bars (which equates to p = .05 when corrected for multiple comparisons). The p values for the post hoc analyses are included above each respective confidence interval bar. Post hoc results for the Peak phase: Near = Medium (t(7) = 1.69, p = .40), Medium = Far (t(7) = 1.23, p = .78) and Near = Far (t(7) = 1.44, p = .58); and for the Sustained phase: Near > Medium (t(7) = 4.71, p < .01), Medium > Far (t(7) = 4.30, p < .05) and Near > Far (t(7) = 5.65, p < .01). (D) Differences between the Peak and Sustained phases for activation in the Near, Medium and Far fixation distances (with 98.33% confidence interval bars, which equates to p = .05 when corrected for multiple comparisons). The p values for the post hoc analyses are included above each respective confidence interval bar. Post hoc results demonstrate Peak > Sustained for Near (t(7) = 3.85, p > .01), Medium (t(7) = 7.81, p > .001) and Far (t(7) = 6.50, p > .001) fixation distances.

180

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

Fig. 9. Results of voxelwise group analysis. (A) dPOS activation cluster resulting from a voxelwise group analysis of Near vs. Far fixation and average % BOLD signal time course at conservative threshold (t > 7.2). (B) Activation resulting from same contrast at a more liberal threshold (t > 5.2) and average time course. (C) Activation and average time course for right superior–posterior occipital lobe foci (t > 7.2). (D) Activation and average time course for left superior–posterior occipital lobe foci (t > 7.2). (E) Activation and average time course for right superior–posterior occipital lobe foci (t > 7.2).

vergence changes or saccades (including microsaccades). We examined the variance only in the horizontal dimension because we were mostly concerned about vergence stability, which would primarily affect horizontal eye position. Horizontal position monitoring could also reflect (micro) saccades, which would affect the horizontal and vertical positions similarly. A data file was produced for each of the 16-second fixation periods performed during the testing session and was comprised of X and Y coordinate measures

for each eye, sampled at a rate of 500 Hz. After blinks and their related artifacts were removed, we computed the variance for each of the fixation periods at each distance and subjected these values to a repeated-measures ANOVA with three levels of fixation distance (Near, Medium and Far). Our analyses found that eye stability was excellent and could not account for the pattern of activation observed in dPOS (see Fig. 10). First, the eyes remained very stable, in all three subjects, with very

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

Fig. 10. Average vergence stability for the near, medium and far fixation distances used in Experiment 2. The solid white line represents the averaged stability (for all subjects) and the gray dashed lines represent the individual subject's eye stability.

low variability (standard deviation < 0.25° of visual angle in conditions within all subjects). Second, distance had no significant effects on eye stability (standard deviation) in either a general ANOVA (F(2,4) = .223, p = .809) or planned comparisons between the distances (Near vs. Medium, t(2) = .64, p = 1.0; Near vs. Far, t(2) = .27, p = 1.0; Medium vs. Far, t(2) = − .75, p = 1.0). Third, although all three subjects showed a clear gradation of activation in dPOS (Near > Medium > Far) in the fMRI session, the individual trends in eye stability could not account for this. For example, Subject N showed the least stability (highest variance) at the far distance but, like all other subjects, had the highest dPOS activity for near vergence (compare activation in Fig. 7—right, with eye stability in Fig. 10). Discussion: Experiment 2 The results of Experiment 2 clearly show that the oculomotor cues of the near response are sufficient to explain the near-space preference in dPOS seen in Experiment 1. Moreover, this increased activation during the near response is sustained over at least 16 s, likely reflecting a tonic distance-specific signal. Specifically, in the stable plateau of the time course, dPOS activation was significantly higher for the near fixation distance than for medium fixation, and likewise, activation in the medium fixation distance condition was significantly higher than for far fixation. This finding clearly demonstrates that dPOS can encode object distance using the near response alone. Given past evidence suggesting that vergence is the most critical component of the near response for depth perception, it is most likely that dPOS reflects vergence eye position rather than accommodative state or pupil size. Pupil size alone does not likely account for our findings because the pupil contracts during near viewing, letting in less light, which, if anything, would be expected to produce less brain activation in visual areas. Two other neuroimaging studies have examined the activation related to changes in the components of the near triad. One study found that vergence tracking of a target oscillating in depth yielded activation across a range of dorsal and ventral stream areas (Hasebe et al., 1999). However, they presented stimuli using a stereoscope, such that vergence and accommodation cues were in conflict. Another study examined the effect of changing accommodation (by insertion of a lens between the eye and target) while vergence remained constant. Compared to target fixation, changing accom-

181

modation produced a large number of activation foci throughout the brain (including visual cortex and dPOS). However, neither of these studies addressed whether there was a preference for near vergence/ accommodation. Furthermore, they each examined one component of the near triad in isolation and it is possible that conflicting cues do not elicit a natural response. It seems quite unlikely that the activation during the near response is due to differences in eye stability that vary with viewing distance rather than fixation distance per se. Our eye movement monitoring showed that vergence eye position was equally stable across viewing distances and is not likely to cause the gradient of activation seen in Experiments 1 and 2. Moreover, if eye position stability varied with viewing distance, the resulting retinal image slip would cause differing activation levels in cortical areas such as MT+. The contrast between near and far conditions, even at relatively liberal thresholds, did not identify any activation at the junction of the inferior temporal and lateral occipital sulci (where the MT+ complex lies, Watson et al., 1993; Dumoulin et al., 2000). Taken together, these results suggest that eye position is equally stable across fixation distances and we are therefore confident that dPOS truly reflects vergence eye position. Another possible explanation for the dPOS activation reported here is that more effort is required to maintain fixation at closer distances; however, we do not believe that activation is due to differences in general attentional demands. The natural resting vergence position of the eyes in the absence of visual input is 120 cm on average (Owens and Leibowitz, 1980; Fisher et al., 1988), and therefore it might be more effortful to verge at nearer distances. However, this resting vergence position varies considerably across studies (e.g., 50 cm in Taptagaporn and Saito, 1993) and between subjects (between 62 cm and 500 cm; Howard, 2002) and can be compressed following prolonged convergence (Maddox, 1893; Rosenfield, 1997). Although we did not measure subjects’ resting vergence positions, the effects of near vs. far vergence in dPOS were remarkably consistent between subjects despite the expected intersubject variability and adaptation to nearer distances (in addition, our subjects came from an academic population where people spend most of the workday doing near work such that near vergence may not have been as effortful as in the general population). Although some studies have investigated the relationship between effort and the near response, “effort” has been quantified in a variety of ways and the results are mixed. For example, some studies have found that visual performance is better for display distances closer to the resting vergence position (Best et al., 1996), while other studies have reported that a mental arithmetic task had no effect on the resting vergence position (Gray et al., 1993). However studies of the network of areas implicated in general attention (Wojciulik and Kanwisher, 1999) have not reported dPOS activation but have reported other activation foci (such as the right intraparietal sulcus) that we did not observe here. The lack of correspondence between our data and the attentional network suggests that, if dPOS activation is a result of “effort”, it appears to be a highly specific type of effort needed to direct eye gaze and not a result of general attention. General discussion Given that one of the clearest brain regions demonstrating a preference for peripersonal space in the macaque monkey is area VIP, we set out to determine whether a proposed human functional equivalent would also display a near-space preference. We iden-

182

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

tified area pVIP in the human intraparietal sulcus based on a response to moving vs. stationary objects near the face. While our pVIP did display clear motion selectivity, it did not, however, display the near-space preference that one would expect of a functional VIP equivalent. Moreover, additional voxelwise approaches did not find any activation along the intraparietal sulcus, including the regions proposed by others as the functional equivalent of pVIP (Bremmer et al., 2001; Sereno and Huang, 2006). As we discussed earlier (Discussion: Experiment 1), the lack of a near-space preference in human pVIP may reflect difficulties in predicting the fMRI population response from single-unit studies, methodological issues or an absence of true functional equivalence between human pVIP and macaque VIP. However, we did identify a clear near-space preference in the occipital lobe, particularly in the dorsal parieto-occipital sulcus. At low, but statistically acceptable thresholds, diffuse activation was observed throughout much of the occipital lobe. When thresholds were raised to identify only the most robust activations, a peak of activity on the posterior bank of the dorsal parieto-occipital sulcus remained in all but one participant in the first experiment. This nearspace preference was not only detectable during real-world viewing conditions where multiple cues to depth were present, but was also quite robust when participants fixated upon points in near vs. far space with no other visual stimulation, such that only oculomotor signals of vergence eye position and ocular accommodation were available. These findings suggest that oculomotor signals modulate large areas of occipital cortex and that this spatial modulation peaks in an area of the dorsal pathway where depth-related information could be used to guide actions such as reaching. Role of human parieto-occipital cortex in reaching and eye movements Although dPOS has not been well-studied in the human, numerous human magnetoencephalography (MEG) and functional neuroimaging studies have reported interesting patterns of activation in the vicinity of our dPOS. These studies have suggested that these functional areas form part of the dorsal stream, are used during the planning and execution of reaching movements, and code for eye position. Because the exact relationship between our dPOS and these areas is uncertain, we will refer to past activations as within the “superior parieto-occipital cortex (sPOC)” which could include the regions on either the posterior or anterior side of the superior parieto-occipital sulcus. Below, we examine the known characteristics of this area in humans. Although it is important for future studies to differentiate between possible subdivisions, at this point the localizations are often only loosely specified in earlier papers so we will discuss activations within the general sPOC location (see Culham et al., in press for a review). Based on human magnetoencephalography (MEG), sPOC likely lies within the dorsal visual stream, which gets input predominantly from the magnocellular pathway (Hari and Salmelin, 1997; Blanke et al., 2003). Consistent with this, the human sPOC responds best to transient luminance changes of coarse images (Portin et al., 1998; Bristow et al., 2005), is activated with a much shorter latency than other extrastriate regions (Vanni et al., 2001), and shows motion selectivity (Stiers et al., 2006; Blanke et al., 2003). New evidence suggests the sPOC plays a critical role in the guidance of arm movements, namely pointing and reaching (Connolly et al., 2003; Astafiev et al., 2003 Prado et al., 2005; Cavina-

Pratesi et al., 2006; de Jong et al., 2001; Pellijeff et al., 2006). This is consistent with the finding that lesions around the sPOC are a common focus for patients with reaching deficits (optic ataxia; Karnath and Perenin, 2005). Although pointing- and reachingrelated activation has typically been stronger and more consistent in a focus anterior to the POS, in several studies activation was also observed posterior to the sulcus (Connolly et al., 2003; Astafiev et al., 2003; Cavina-Pratesi et al., 2006; de Jong et al., 2001), in a similar location to our dPOS focus. These results, when taken in combination, suggest that the sPOC plays a critical role in planning and executing hand actions to the specific spatial location of the target in three-dimensional space. Although the sPOC is responsive to pointing movements, there is also evidence that it can also be driven by eye movements alone. A connectivity study suggested that the human frontal eye fields (FEF) receive neural connections from a region that is very similar in location to our dPOS. These results suggest that dPOS, like FEF, is part of the network of areas involved in eye movements (Paus et al., 1997). Functionally the role of dPOS in eye movements was confirmed by Law et al. (1998), who found activation there when subjects performed saccades, even in the dark. This finding demonstrated that visual stimuli are not required to activate dPOS. Moreover, it suggests that the area may encode eye position signals based on efference copy (information about the motor command to move the eyes) and/or signals from the ocular muscles. Accurate guidance of reaching requires accurate signals about eye positions and eye movements. In the real world, the eyes typically land on the target of an upcoming hand action before the hand action occurs (e.g., Land et al., 1999; Hayhoe et al., 2003). Accordingly, when subjects are looking at the target, they perform the hand action more accurately than when they are looking away (Bock, 1986; Neggers and Bekkering, 1999; Prablanc et al., 1979; Van Donkelaar and Staub, 2000). This increase in accuracy with foveation also occurs for reaches made in complete darkness, suggesting that the improvement is not due entirely from enhanced resolution at the fovea, but relies to some degree on improved spatial coding when eye and hand are directed to the same location (Neggers and Bekkering, 1999). Moreover, patients with reaching deficits (optic ataxia) may demonstrate misreaching only when their gaze is directed away from the target of the reach (Perenin and Vighetto, 1988). Recent fMRI evidence suggests that different reach-related areas may be activated depending on whether or not the reach is preceded by an eye movement to the target. Prado et al. (2005) found that that the sPOC and frontal eye fields (which they called dorsal premotor cortex) were activated during reaching only when the target was presented in peripheral vision (in contrast, an antero-medial intraparietal region was activated by reaching regardless of whether the target was presented at the fovea or in the periphery). These results suggest that the sPOC plays a key role in reaching to targets in the periphery, when the hand movement must be decoupled from the gaze direction. Of course, accurate reaching movements depend not only on conjugate eye movements but also on vergence eye movements. Reaches in the dark are systematically inaccurate when fixation is diverted in depth away from the intended target (Henriques et al., 2003). Prehension movements are also influenced by vergence (Mon-Williams and Dijkerman, 1999). Moreover, as targets get nearer, vergence information plays an increasingly important role in distance perception and presumably also actions (Mon-Williams and Tresilian, 1999). Vergence information is likely processed within the dorsal stream. D.F., a patient with damage to the ventral

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

stream, is nevertheless able to determine the distance of objects in near-space using vergence cues (Mon-Williams et al., 2001). In addition to her prominent ventral stream lesions, D.F. also has a left hemisphere lesion anterior to the POS; however, her dPOS remains intact bilaterally (James et al., 2003). One mechanism by which eye position is likely incorporated into action planning is by way of gain modulation. In gain modulation, the strength of a response to a stimulus or action is affected by another factor that does not change the selectivity of the response (Salinas and Sejnowski, 2001). For example, in single neurons, the response to a visual target may be highest for a particular retinotopic location, but the amplitude of the response can be modulated by whether gaze is directed leftward vs. rightward (see Cohen and Andersen, 2002 for a review). Such gaze modulation is just one example of how “gain fields” can be used to combine information from various sources in the computation of coordinate transformations (Zipser and Andersen, 1988). In the case of gaze modulation, information about lateral eye position, combined with information about retinotopic position, can be used to compute egocentric coordinates for a target’s location in the frontoparallel plane (Andersen et al., 1993). Gaze modulation for lateral (left–right) eye position has been observed in numerous macaque areas (e.g., Andersen et al., 1985; Galletti et al., 1995; Ferraina et al., 1997; and Mushiake et al., 1997) including the parietal reach region (Andersen et al., 1998). Indeed, gaze modulation has also been observed in a reach-related area of the human brain. DeSouza et al. (2000) found that the response to pointing movements was modulated by lateral eye position in the anterior intraparietal cortex. In addition, they also found that visual responses in occipital areas were modulated by lateral eye position as well (DeSouza et al., 2002). These results demonstrate that the human brain, like the macaque brain, uses eye position information in computing body movements. Our results suggest that such eye position-based modulation likely also occurs for the third dimension, with increased signals as gaze becomes closer, and that this modulation is most prominent in dPOS. Gaze modulation usually refers to the modulation of a response to a stimulus (e.g., visual stimulus) or action (e.g., a reaching action) by eye position. Although our first experiment showed modulation of a response to stimulus motion (even though the amplitude of stimulus motion and eye rotation were equivalent at all three distances), in the second experiment, there was no stimulus or action to be modulated. Thus the second experiment suggests that the effects in dPOS reflect true eye position signals, specific to distance, rather than gain modulation per se. Other human fMRI studies have found true eye position signals in visual areas (DeSouza et al., 2002). One possibility is that this tonic gaze coding in dPOS provides input to neurons in other areas that demonstrate gaze modulation in the traditional sense. To summarize, in the context of earlier literature, our findings suggest that near vergence is coded in dPOS, a region within the dorsal pathway that plays a critical role in reaching, particularly when the target is off-fixation. Eye position signals related to the current degree of vergence in dPOS likely supply the dorsal stream with critically important information about object distance with respect to current gaze. Relationship between human and macaque parieto-occipital cortex Based on anatomical location and functional properties, there are a large number of similarities between the human sPOC and the macaque parieto-occipital (PO) area. Macaque PO comprises areas

183

V6 and a part of V6A (Galletti et al., 2005). V6 and V6A lie within the macaque parieto-occipital sulcus, with V6A just anterior and dorsal to V6 (Galletti et al., 1996, 1999a, 1999b). V6A is a visuomotor area which plays a key role in the arm transport component of reaching and grasping movements (Fattori et al., 2001, 2004, 2005). V6A receives strong visual inputs from V6, an extrastriate area which receives magnocellular inputs directly from V1 (Galletti et al., 2001). Both regions are considered part of the dorsal stream and are motion-selective. V6 demonstrates clear retinotopic organization, with little eccentricity scaling, while V6A has little or no retinotopy (Galletti et al., 1996, 1999a, 1999b). Recent neuroimaging evidence has led to the proposal that the putative human functional equivalent of V6 (pV6) lies immediately posterior to the superior end of the POS (Pitzalis et al., 2006a), while the putative human equivalent of V6A (pV6A) is thought to lie just in front of it, anterior to the POS (Pitzalis et al., 2006b). Unlike prior neuroimaging studies which have speculated on the locations of pV6 and pV6A on the basis of the location they occupied in the macaque brain (Dechent and Frahm, 2003), these human neuroimaging studies were clearly motivated by multiple definitive properties of the two areas in the macaque brain. Namely, pV6, like macaque V6, shows a clear retinotopic map when widefield visual stimuli are used and holds a similar relationship with adjacent visual areas (Pitzalis et al., 2006a). pV6A, like macaque V6A, shows a greater response during reaching than saccades (Pitzalis et al., 2006b; Connolly et al., 2003; Astafiev et al., 2003) and contains a similar eccentricity map (Pitzalis et al., 2006b). It is possible that our human dPOS focus corresponds to one of the subregions of the macaque PO complex, V6 and/or V6A. The anatomical locus of dPOS, on the posterior side of the POS, is more consistent with the proposed location of human V6 (Pitzalis et al., 2006a). A preference for near vergence across multiple brain regions Although the signals for near vergence were most robust and reliable in dPOS, we nevertheless observed a general preference for near vergence throughout much of the occipital lobe. Additional activation peaks were observed in areas that are difficult to identify without retinotopy, but which may correspond to visual area V3A. Human V3A has previously found to be activated by visual motion (Tootell et al., 1997) and stereoscopic depth (Brouwer et al., 2005; Tsao et al., 2003; Neri et al., 2004). Our data suggest that, in addition to stereoscopic cues to depth, V3A may also be sensitive to oculomotor cues. These additional occipital areas were activated even though the stimuli were matched for all visual features besides depth and even though our eye movement recordings ruled out differences in fixation stability at different depths. It is quite possible that vergence-related signals are a general property of the visual system. Although such results have not previously been reported with human neuroimaging, numerous macaque neurophysiology studies have found similar near-space preferences in early visual areas. Within macaque V1 (Trotter et al., 1992; Gonzalez and Perez, 1998), as well as V2 and V4 (Dobbins et al., 1998; Rosenbluth and Allman, 2002), modulation by vergence distance has been observed. This result was interpreted as an early representation of oculomotor cues to distance, either vergence eye position signals or ocular accommodative signals (Trotter et al., 1992). This overrepresentation of near-tuned neurons in the macaque also seems to

184

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

be present in humans and could explain the diffuse near response activation seen in our data. Given evidence that dPOS demonstrates functional connectivity with the region around the frontal eye fields (Paus et al., 1997), we propose that dPOS likely forms part of a more general network involved in coding the near response. Our slice parameters did not enable us to scan the frontal eye fields or the cerebellum in all subjects (see Figs. 1 and 6), though the macaque physiological responses (Gamlin and Yoon, 2000; Gamlin and Clarke, 1995) would suggest that a similar near preference should also be observed there. Although our slice parameters did include the putative equivalent of the macaque LIP (e.g., Muri et al., 1996; Sereno et al., 2001), we did not observe any vergence distance modulation outside of the sPOC. The likelihood of observing such a response depends on the proportion of neurons with a preference for near vergence, and that is not well-characterized as yet in macaque LIP. Transcranial magnetic stimulation to the left posterior parietal cortex has been found to slow the latency for convergence; however, the anatomical focus of stimulation was only coarsely specified (Yang and Kapoula, 2004). The majority of vision and action research utilizes two-dimensional, frontoparallel displays presented at a single distance (often 57 cm) to the exclusion of fixation distance as a relevant condition. Our study, along with a wealth of prior physiological research, suggests that viewing distance can have a profound effect on neuronal activity. This has been seen both at the neuronal level in macaque early visual areas, as well as at the population level seen in our diffuse activation throughout such of human primary visual cortex. These findings imply that viewing distance should be an important consideration in the design of neurophysiology experiments. Neurons previously classified as non-visual (at 57 cm) may become active at closer viewing distances. Given the results we see here, we predict that macaque areas V6 and/or V6A should show modulation with viewing distance and a preponderance of near-viewing tuned cells. In addition, as neuroimaging studies begin to explore actions in 3D space (e.g., Culham et al., 2006), it may also be important to keep vergence constant to avoid spurious responses in visual areas. General summary In summary, we have observed that the oculomotor near response produces activation in dPOS, a dorsal stream region that is involved in reaching movements. The responsiveness of dPOS to the near response may play a key role in the utilization of gaze information to guide actions such as reaching and grasping. dPOS appears to be one of numerous regions which are involved in using a signal regarding vergence distance (and possibly accommodation) to transform retinotopic to egocentric coordinates. Acknowledgments This study was funded by an Natural Sciences and Engineering Research Council of Canada operating grant and Ontario Premier’s Research Excellence Award to Jody Culham. We would also like to thank the following: James Danckert for use of his eye-tracking equipment; Haitao Yang for his assistance in collecting eye-tracking data; Dan Pulham and Jim Ladich for their assistance in building our testing equipment; Carol Colby for clarifications regarding her 1993 study; Patrizia Fattori, Claudio Galletti, Denise Henriques and Liana Brown for comments on earlier drafts of the manuscript; our subjects; and lastly, the members of Derek Quinlan’s M.Sc.

advisory committee, Mel Goodale, Tutis Vilis and Ravi Menon, for helpful discussions. References Andersen, R.A., Essick, G.K., Siegel, R.M., 1985. Encoding of spatial location by posterior parietal neurons. Science 25:230 (4724), 456–458. Andersen, R.A., Snyder, L.H., Li, C.S., Stricanne, B., 1993. Coordinate transformations in the representation of spatial information. Curr. Opin. Neurobiol. 3 (2), 171–176. Andersen, R.A., Snyder, L.H., Bradley, D.C., Xing, J., 1997. Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annu. Rev. Neurosci. 20, 303–330. Andersen, R.A., Snyder, L.H., Batista, A.P., Buneo, C.A., Cohen, Y.E., 1998. Posterior parietal areas specialized for eye movements (LIP) and reach (PRR) using a common coordinate frame. Novartis Found. Symp. 218, 109–122. Astafiev, S.V., Shulman, G.L., Stanley, C.M., Snyder, A.Z., Van Essen, D.C., Corbetta, M., 2003. Functional organization of human intraparietal and frontal cortex for attending, looking, and pointing. J. Neurosci. 23 (11), 4689–4699. Belia, S., Fidler, F., Williams, J., Cumming, G., 2005. Researchers misunderstand confidence intervals and standard error bars. Psychol. Methods 10 (4), 389–396. Berti, A., Frassinetti, F., 2000. When far becomes near: remapping of space by tool use. J. Cogn. Neurosci. 12 (3), 415–420. Best, P.S., Littleton, M.H., Gramopadhye, A.K., Tyrell, R.A., 1996. Relations between individual differences in oculomotor resting states and visual inspection performance. Ergonomics 39 (1), 35–40. Bjoertomt, O., Cowey, A., Walsh, V., 2002. Spatial neglect in near and far space investigated by repetitive transcranial magnetic stimulation. Brain: J. Neurol. 125 (9), 2012–2022. Blanke, O., Landis, T., Mermoud, C., Spinelli, L., Safran, A.B., 2003. Direction-selective motion blindness after unilateral posterior brain damage. Eur. J. Neurosci. 18 (3), 709–722. Bock, O., 1986. Contribution of retinal versus extraretinal signals towards visual localization in goal-directed movements. Exp. Brain Res. 64, 476–482. Bremmer, F., Schlack, A., Shah, N.J., Zafiri, O., Kubischik, M., Hoffman, K., Zilles, K., Fink, G.R., 2001. Polymodal motion processing in posterior parietal and premotor cortex: a human fMRI study strongly implies equivalencies between humans and monkeys. Neuron 29 (1), 4–6. Bristow, D., Frith, C., Rees, G., 2005. Two distinct neural effects of blinking on human visual processing. NeuroImage 27 (1), 136–145. Brouwer, G.J., van Ee, R., Schwartzbach, J., 2005. Activation in visual cortex correlates with the awareness of stereoscopic depth. J. Neurosci. 25 (45), 10403–10413. Cavina-Pratesi, C., Fattori, P., Galletti, C., Quinlan, D., Goodale, M., Culham, J., 2006. Event-related fMRI reveals a dissociation in the parietal lobe between transport and grip components in reach-to-grasp movements. Abstract submitted to the Society for Neuroscience. Atlanta, Georgia. Cohen, Y.E., Andersen, R.A., 2002. A common reference frame for movement plans in the posterior parietal cortex. Nat. Rev., Neurosci. 3 (7), 553–562. Colby, C.L., Duhamel, J., Goldberg, M.E., 1993. Ventral intraparietal area of the macaque: anatomic location and visual response properties. J. Neurophysiol. 69 (3), 902–914. Connolly, J.D., Andersen, R.A., Goodale, M.A., 2003. FMRI evidence for a ‘parietal reach region’ in the human brain. Exp. Brain Res. 153 (2), 140–145. Culham, J.C., 2006. Functional neuroimaging: experimental design and analysis. Book chapter, In: Cabeza, R., Kingstone, A. (Eds.), Handbook of Functional Neuroimaging of Cognition, 2nd ed. MIT Press, Cambridge, MA. Culham, J.C., Valyear, K.F., 2006. Human parietal cortex in action. Curr. Opin. Neurobiol. 6, 205–212.

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187 Culham, J.C., Cavina-Pratesi, C., Singhal, A., 2006. The role of parietal cortex in visuomotor control: what have we learned from neuroimaging? Neuropsychologia 44, 2668–2684. Culham, J. C., Gallivan, J., Cavina-Pratesi, C., Quinlan, D. J., in press. fMRI investigations of reaching and ego space in human superior parietooccipital cortex. In: Klatzky, R. L., Behrmann, M., MacWhinney, B. (Eds.), Embodiment, Ego-space and Action. Erlbaum, Madwah NJ. Cumming, G., Finch, S., 2005. Inference by eye: confidence intervals and how to read pictures of data. Am. Psychol. 60 (2), 170–180. Dechent, P., Frahm, J., 2003. Characterization of the human visual V6 complex by functional magnetic resonance imaging. Eur. J. Neurosci. 17 (10), 2201–2211. de Jong, B.M., van der Graaf, F.H.C.E., Paans, A.M.J., 2001. Brain activation related to the representations of external space and body scheme in visuomotor control. NeuroImage 14 (5), 1128–1135. DeSouza, J.F.X., Dukelow, S.P., Gati, J.S., Menon, R.S., Andersen, R.A., Vilis, T., 2000. Eye position signal modulates a human parietal pointing region during memory-guided movements. J. Neurosci. 20 (15), 5835–5840. DeSouza, J.F., Dukelow, S.P., Vilis, T., 2002. Eye position signals modulate early dorsal and ventral visual areas. Cereb. Cortex 12 (9), 991–997. Dobbins, A.C., Jeo, R.M., Fiser, J., Allman, J.M., 1998. Distance modulation of neural activity in the visual cortex. Science 281 (5376), 552–555. Duhamel, J.R., Colby, C.L., Goldberg, M.E., 1998. Ventral intraparietal area of the macaque: congruent visual and somatic response properties. J. Neurophysiol. 79 (1), 126–136. Dumoulin, S.O., Bittar, R.G., Kabani, N.J., Baker, C.L., Le Goualher, G., Bruce Pike, G., et al., 2000. A new anatomical landmark for reliable identification of human area V5/MT: a quantitative analysis of sulcal patterning. Cereb. Cortex 10, 454–463. Fattori, P., Gamberini, M., Kutz, D.F., Galletti, C., 2001. ‘Arm-reaching’ neurons in the parietal area V6A of the macaque monkey. Eur. J. Neurosci. 13, 2309–2313. Fattori, P., Breveglieri, R., Amoroso, K., Galletti, C., 2004. Evidence for both reaching and grasping activity in the medial parieto-occipital cortex of the macaque. Eur. J. Neurosci. 20, 2457–2466. Fattori, P., Kutz, D.F., Breveglieri, R., Marzocchi, N., Galletti, C., 2005. Spatial tuning of reaching activity in the medial parieto-occipital cortex (area V6A) of macaque monkey. Eur. J. Neurosci. 22, 956–972. Ferraina, S., Johnson, P.B., Garasto, M.R., Battaglia-Mayer, A., Ercolani, L., Bianchi, L., Lacquaniti, F., Caminiti, R., 1997. Combination of hand and gaze signals during reaching: activity in parietal area 7 m of the monkey. J. Neurophysiol. 77 (2), 1034–1038. Field, A.S., Yen, Y., Burdette, J.H., Elster, A.D., 2000. False cerebral activation on BOLD functional MR images: study of low-amplitude motion weakly correlated to stimulus. Am. J. Neuroradiol. 21, 1388–1396. Fisher, S.K., Ciuffreda, K.J., Tannen, B., Super, P., 1988. Stability of tonic vergence. Invest. Ophthalmol. Visual Sci. 29, 1577–1581. Foley, J.M., 1980. Binocular distance perception. Psychol. Rev. 87 (5), 411–434. Freire, L., Mangin, J.F., 2001. Motion correction algorithms may create spurious brain activations in the absence of subject motion. NeuroImage 14 (3), 709–722. Galletti, C., Battaglini, P.P., Fattori, P., 1995. Eye position influence on the parieto-occipital area PO (V6) of the macaque monkey. Eur. J. Neurosci. 7, 2486–2501. Galletti, C., Fattori, P., Battaglini, P.P., Shipp, S., Zeki, S., 1996. Functional demarcation of a border between areas V6 and V6A in the superior parietal gyrus of the macaque monkey. Eur. J. Neurosci. 8, 30–52. Galletti, C., Fattori, P., Kutz, D.F., Gamberini, M., 1999a. Brain location and visual topography of cortical area V6A in the macaque monkey. Eur. J. Neurosci. 11, 575–582. Galletti, C., Fattori, P., Gamberini, M., Kutz, D.F., 1999b. The cortical visual area V6: brain location and visual topography. Eur. J. Neurosci. 11, 3922–3936. Galletti, C., Gamberini, M., Kutz, D.F., Fattori, P., Luppino, G., Matelli,

185

M., 2001. The cortical connections of area V6: an occipito-parietal network processing visual information. Eur. J. Neurosci. 13, 1572–1588. Galletti, C., Fattori, P., Gamberini, M., Kutz, D.F., 2005. The cortical visual area V6: brain location and visual typography. Eur. J. Neurosci. 11 (11), 3922–3936. Gamlin, P.D., Clarke, R.J., 1995. Single-unit activity in the primate nucleus reticularis tegmenti pontis related to vergence and ocular accommodation. J. Neurophysiol. 73 (5), 2115–2119. Gamlin, P.D., Yoon, K., 2000. An area for vergence eye movement in primate frontal cortex. Nature 407 (6807), 1003–1007. Goodale, M.A., Milner, A.D., 1992. Separate visual pathways for perception and action. Trends Neurosci. 15(1)[163]:20–25. Goltz, H.C., Dukelow, S.P., DeSouza, J.F.X., Culham, J.C., van den Berg, A.V., Goosens, H.H.L.M., Menon, R.S., Vilis, T., 2001. A putative homologue of monkey area VIP in humans. Poster presented at the annual meeting of the Society for Neuroscience. San Diego, CA. Gonzalez, F., Perez, R., 1998. Modulation of cell responses to horizontal disparities by ocular vergence in the visual cortex of the awake Macaca mulatta monkey. Neurosci. Letters 245 (2), 101–104. Gray, L.S., Winn, B., Gilmartin, B., Eadie, A.S., 1993. Objective concurrent measures of open-loop accommodation and vergence under photopic conditions. Invest. Ophthalmol. Visual Sci. 34 (10), 2996–3003. Grill-Spector, K., Malach, R., 2004. The human visual cortex. Annu. Rev. Neurosci. 27, 649–677. Halligan, P.W., Marshall, J.C., 1991. Left neglect for near but not far space in man. Nature 350 (6318), 498–500. Hasebe, H., Oyamada, H., Kinomura, S., Kawashima, R., Ouchi, Y., Nobezawa, S., Tsukada, H., Yoshikawa, E., Ukai, K., Takada, R., Takagi, M., Abe, H., Fukuda, H., Bando, T., 1999. Human cortical areas activated in relation to vergence eye movements—A PET study. NeuroImage 10 (2), 200–208. Hari, R., Salmelin, R., 1997. Human cortical oscillations: a neuromagnetic view through the skull. Trends Neurosci. 20 (1), 44–49. Hasnain, M.K., Fox, P.T., Woldorff, M.G., 1998. Intersubject variability of functional areas in the human visual cortex. Hum. Brain Mapp. 6 (4), 301–315. Hayhoe, M.M., Shrivastava, A., Mruczek, R., Pelz, J.B., 2003. Visual memory and motor planning in a natural task. J. Vis. 3 (1), 49–63. Heinemann, E.G., Tulving, E., Nachmias, J., 1959. The effect of oculomotor adjustment of the visual stimulus. Am. J. Psychol. 72, 32–45. Henriques, D.Y., Medendorp, W.P., Gielen, C.C., Crawford, J.D., 2003. Geometric computations underlying eye–hand coordination: orientations of the two eyes and the head. Exp. Brain Res. 152 (1), 70–78. Howard, I.P., 2002. Seeing in depth. Basic Mechanisms, vol.1. University Toronto Press, Toronto, ON, Canada. Huk, A.C., Heeger, D.J., 2000. Task-related modulation of visual cortex. J. Neurophysiol. 83 (6), 3525–3536. Iriki, A., Tanaka, M., Iwamura, Y., 1996. Coding of modified body schema during tool use by macaque postcentral neurones. NeuroReport 7 (14), 2325–2330. James, T.W., Culham, J., Humphrey, G.K., Milner, A.D., Goodale, M.A., 2003. Ventral occipital lesions impair object recognition but not objectdirected grasping: an fMRI study. Brain 126 (Pt. 11), 2463–2475. Karnath, H.O., Perenin, M.T., 2005. Cortical control of visually guided reaching: evidence from patients with optic ataxia. Cereb. Cortex 15 (10), 1561–1569. Krekelberg, B., Boynton, G.M., van Wezel, R.J.A., 2006. Adaptation: from single cells to BOLD signals. Trends Neurosci. 29 (5), 250–256. Kunnapas, T., 1968. Distance perception as a function of available visual cues. J. Exp. Psychol. 77 (4), 523–529. Kwong, K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E., Weisskoff, R.M., Poncelet, B.P., Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., Cheng, H-M., Brady, T.J., Rosen, B.R., 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. U. S. A. 89, 5675–5679. Land, M., Mennie, N., Rusted, J., 1999. The roles of vision and eye

186

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187

movements in the control of activities of daily living. Perception 28 (11), 1311–1328. Law, I., Svarer, C., Rostrup, E., Paulson, O.B., 1998. Parieto-occipital cortex activation during self-generated eye movements in the dark. Brain 121 (11), 2189–2200. Loftus, G.R., Masson, M.E., 1994. Using confidence intervals in withinsubject designs. Psychon. Bull. Rev. 1 (4), 476–490. Longo, M.R., Lourenco, S.F., 2006. On the nature of near space: effects of tool use and the transition to far space. Neuropsychologia 44 (6), 977–981. Masson, M.E.J., Loftus, G.R., 2003. Using confidence intervals for graphically based data interpretation. Can. J. Exp. Psychol. 57 (3), 203–220. Marshall, J.C., Fink, G.R., 2001. Spatial cognition: where we were and where we are. NeuroImage 14, 2–147. Maddox, E.E., 1893. The Clinical Use of Prisms and the Decentering of Lenses. John Wright, Bristol England. Makin, T.R., Holmes, N.P., Zohary, E., 2007. Is that near my hand? Multisensory representation of peripersonal space in human intraparietal sulcus. J. Neurosci. 27, 731–740. Milner, A.D., Goodale, M.A., 1995. The Visual Brain in Action. Oxford University Press, New York, NY: US. Mon-Williams, M., Dijkerman, H.C., 1999. The use of vergence information in the programming of prehension. Exp. Brain Res. 128 (4), 578–582. Mon-Williams, M., Tresilian, J.R., 1999. Some recent studies on the extraretinal contribution to distance perception. Perception 28 (2), 167–181. Mon-Williams, M., Tresilian, J.R., McIntosh, R.D., Milner, A.D., 2001. Monocular and binocular distance cues: insights from visual form agnosia I (of III). Exp. Brain Res. 139 (2), 127–136. Morrison, J.D., Whiteside, T.C., 1984. Binocular cues in the perception of distance of a point source of light. Perception 13 (5), 555–566. Muri, R.M., Iba-Zizen, M.T., Derosier, C., Cabanis, E.A., PierrotDeseilligny, C., 1996. Location of human posterior eye field with functional magnetic resonance imaging. J. Neurol. Neurosurg. Psychiatry 60, 445–448. Mushiake, H., Tanatsugu, Y., Tanji, J., 1997. Neuronal activity in the ventral part of the premotor cortex during target-reach movement is modulated by direction of gaze. J. Neurophysiol. 78 (1), 567–571. Neggers, S.F., Bekkering, H., 1999. Integration of visual and somatosensory target information in goal-directed eye and arm movements. Exp. Brain Res. 125 (1), 97–107. Neri, P., Bridge, H., Heeger, D.J., 2004. Stereoscopic processing of absolute and relative disparity in human visual cortex. J. Neurophysiol. 92 (3), 1880–1891. O’Craven, K.M., 1997. Voluntary attention can modulate fMRI activation in human MT/MST. Dissertation Abstracts International: Section B: The Sciences and Engineering, 57 (9-B): 5519. Ogawa, S., Tank, D.W., Menon, R., Ellermann, J.M., Kim, S.-G., 1992. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. U. S. A. 89, 5951–5955. Owens, D.A., Leibowitz, H.W., 1980. Accommodation convergence and distance perception in low illumination. Am. J. Optom. Physiol. Opt. 57, 540–550. Paus, T., Jech, R., Thompson, C.J., Comeau, R., Peters, T., Evans, A.C., 1997. Transcranial magnetic stimulation during positron emission tomography: a new method for studying connectivity of the human cerebral cortex. J. Neurosci. 17 (9), 3178–3184. Pellijeff, A., Bonilha, L., Morgan, P.S., McKenzie, K., Jackson, S.R., 2006. Parietal updating of limb posture: an event-related fMRI study. Neuropsychologia 44 (13), 2685–2690. Perenin, M.T., Vighetto, A., 1988. Optic ataxia: a specific disruption in visuomotor mechanisms: I. Different aspects of the deficit in reaching for objects. Brain 111 (Pt. 3), 643–674. Pitzalis, S., Galletti, C., Huang, R.-S., Patria, F., Committeri, G., Galati, G., Fattori, P., Sereno, M.I., 2006a. Wide-field retinotopy defines human cortical visual area V6. J. Neurosci. 6, 7962–7973.

Pitzalis, S., Galletti, C., Huang, R.-S., Patria, F., Committeri, G., Galati, G., Fattori, P., Sereno, M.I., 2006b. Visuotopic Properties of the Putative Human Homologue of the Macaque V6A. Poster Presented at the 12th Annual Meeting of the Organization for Human Brain Mapping in Florence, Italy. Portin, K., Salenius, S., Salmelin, R., Hari, R., 1998. Activation of the human occipital and parietal cortex by pattern and luminance stimuli: neuromagnetic measurements. Cereb. Cortex 8 (3), 253–260. Prablanc, C., Echallier, J.F., Komilis, E., Jeannerod, M., 1979. Optimal response of eye and hand motor systems in pointing at a visual target: I. Spatio-temporal characteristics of eye and hand movements and their relationships when varying the amount of visual information. Biol. Cybernet. 35, 113–124. Prado, J., Clavagnier, S., Otzenberger, H., Scheiber, C., Kennedy, H., Perenin, M.-T., 2005. Two cortical systems for reaching in central and peripheral vision. Neuron 48, 849–858. Previc, F.H., 1990. Functional specialization in the lower and upper visual fields in humans: its ecological origins and neurophysiological implications. Behav. Brain Sci. 13 (3), 519–575. Previc, F.H., 1998. The neuropsychology of 3-D space. Psychol. Bull. 124 (2), 123–164. Rosenfield, M., 1997. Tonic vergence and vergence adaptation. Opt. Vis. Sci. 74, 303–328. Rosenbluth, D., Allman, J.M., 2002. The effect of gaze angle and fixation distance on the responses of neurons in V1, V2, and V4. Neuron 33 (1), 143–149. Salinas, E., Sejnowski, T.J., 2001. Gain modulation in the central nervous system: where behavior, neurophysiology, and computation meet. Neuroscientist 7 (5), 430–440. Scannell, J.W., Young, M.P., 1999. Neuronal population activity and functional imaging. Proc. R. Soc. B, Biol. Sci. 266 (1422), 875–881. Sereno, M.I., Huang, R.S., 2006. A human parietal face area contains aligned head-centered visual and tactile maps. Nat. Neurosci. 9, 1337–1343. Sereno, M.I., Pitzalis, S., Martinez, A., 2001. Mapping of contralateral space in retinotopic coordinates by a parietal cortical area in humans. Science 294 (5545), 1350–1354. Skrandies, W., 1987. Visual persistence of stereoscopic stimuli: electric brain activity without perceptual correlate. Vision Res. 27 (12), 2109–2118. Steinman, R.M., Cushman, W.B., Martins, A.J., 1982. The precision of gaze. Hum. Neurobiol. 1, 97–109. Stiers, P., Peeters, R., Lagae, L., Van Hecke, P., Sunaert, S., 2006. Mapping multiple visual areas in the human brain with a short fMRI sequence. NeuroImage 29 (1), 74–89. Swenson, H.A., 1932. The relative influence of accommodation and convergence in the judgment of distance. J. Gen. Psychol. 7, 360–380. Talairach, J., Tournoux, P., 1988. Co-planar Stereotaxic Atlas of the Human Brain. Thieme, New York. Taptagaporn, S., Saito, S., 1993. Visual comfort in VDT operation: physiological resting states of the eyes. Ind. Health 31 (1), 13–28. Tootell, R.B., Mendola, J.D., Hadjikhani, N.K., Ledden, P.J., Liu, A.K., Reppas, J.B., Sereno, M.I., Dale, A.M., 1997. Functional analysis of V3A and related areas in human visual cortex. J. Neurosci. 17 (18), 7060–7078. Trotter, Y., Celebrini, S., Stricanne, B., Thorpe, S., Imbert, M., 1992. Modulation of neural stereoscopic processing in primate area V1 by the viewing distance. Science 257 (5074), 1279–1281. Tsao, D.Y., Vanduffel, W., Sasaki, Y., Fize, D., Knutsen, T.A., Mandeville, J.B., Wald, L.L., Dale, A.M., Rosen, B.R., Van Essen, D.C., Livingstone, M.S., Orban, G.A., Tootell, R.B., 2003. Stereopsis activates V3A and caudal intraparietal areas in macaques and humans. Neuron 39 (3), 555–568. van Donkelaar, P., Staub, J., 2000. Eye–hand coordination to visual versus remembered targets. Exp. Brain Res. 133, 414–418. Vanni, S., Tankskanen, T., Seppa, M., Uutela, K., Hari, R., 2001. Coinciding early activation of the human primary visual cortex and anteromedial cuneus. Proc. Natl. Acad. Sci. U. S. A. 98 (5), 2776–2780.

D.J. Quinlan, J.C. Culham / NeuroImage 36 (2007) 167–187 von Noorden, G.K., 1996. Binocular Vision and Ocular Motility: Theory and Management of Strabismus: Theory and Management of Strabismus. St. Louis, MO: Mosby. Vuilleumier, P., Valenza, N., Mayer, E., Reverdin, A., Landis, T., 1998. Near and far visual space in unilateral neglect. Ann. Neurol. 43 (3), 406–410. Watson, J.D.G., Myers, R., Frackowiak, R.S.J., Hajnal, J.V., Woods, R.P., Mazziotta, J.C., et al., 1993. Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cereb. Cortex 3, 79–94. Weiss, P.H., Marshall, J.C., Wunderlich, G., Tellmann, L., Halligan, P.W.,

187

Freund, H.J., Zilles, K., Fink, G.R., 2000. Neural consequences of acting in near versus far space: a physiological basis for clinical dissociations. Brain 123 (Pt. 12), 2531–2541. Wojciulik, E., Kanwisher, N., 1999. The generality of parietal involvement in visual attention. Neuron 23, 747–764. Yang, Q., Kapoula, Z., 2004. TMS over the left posterior parietal cortex prolongs latency of contralateral saccades and convergence. Invest. Ophthalmol. Visual Sci. 45 (7), 2231–2239. Zipser, D., Andersen, R.A., 1988. A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature 25;331 (6158), 679–684.