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Department of Psychology and Center for Neural Science, New York University, New York, New York
Submitted 10 August 2007; accepted in final form 15 November 2007
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ABSTRACT |
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INTRODUCTION |
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; Curtis et al. 2004; Funahashi et al. 1989; Gnadt and Andersen 1988; Hikosaka and Wurtz 1983; Lawrence et al. 2005; Schluppeck et al. 2006; Srimal and Curtis 2008). In this task, a sensory cue is briefly flashed in the periphery and the primate is required to "hold in mind" the location of the cue, after which an eye movement is generated to the remembered location. This paradigm is designed to separate in time the physiological responses to the sensory, delay, and motor components, so that they can be analyzed independently, uncontaminated by each of the other components. For example, persistent neural activity during the memory delay, after the visual cue has extinguished but before the memory-guided saccade has been generated, may represent the mnemonic mechanism that bridges in time the past sensory event and the future contingent response (Fuster 2001).
However, one critical problem arises in attempts to infer the nature of persistent activity during the memory interval—that is, does the delay activity represent a retrospective code of the sensory cue's location or does it instead represent a prospective code of the forthcoming movement (Calton et al. 2002; Curtis and D'Esposito 2006; Curtis et al. 2004; D'Esposito et al. 2000; Dickinson et al. 2003; Funahashi et al. 1993; Snyder et al. 1997; Srimal and Curtis 2008; Takeda and Funahashi 2004)? The type of motor effector the primate plans to use can modulate persistent activity and thus the activity may represent the "intention" to make a specific motor response (for review, see Snyder et al. 2000). Another question is whether neural activity persists only when the planned response is guided by memory. In other words, would the observed monkey single unit (Snyder et al. 2000) and functional magnetic resonance imaging (fMRI) signals (Curtis 2006) persist even when there is no memory component and the movement is externally guided? To address this question, we used an oculomotor deferred-saccade task (DST), in which the sensory cue is continuously present throughout the delay interval, rather than merely being transiently flashed prior to the delay interval. This paradigm eliminates the memory component (i.e., the need to remember the location of the saccade target) and places the emphasis squarely on advance motor preparation (Evarts et al. 1984; Requin et al. 1990; Riehle and Requin 1993). Consequently, in the present study, fMRI blood oxygen level–dependent (BOLD) activity increases can be argued to represent a prospective motor plan and/or a "preparatory set."
A second and related question is whether such signals would persist even when a future saccadic eye movement is planned to an internal rather than to an external representation of the target. This issue was addressed by requiring subjects to plan either a prosaccade (i.e., look toward the target) or an antisaccade (Hallett 1978; Munoz and Everling 2004). In the antisaccade task, subjects prepared saccades to an internal representation of the mirrored location of the target. Third, we tested whether delay signals represent a general form of preparatory set (i.e., a readiness to act on the visual target) versus the preparation of the specific metrics of the planned saccade. To do so, subjects performed trials in which the target was present (known DST) or not present until after (unknown DST) the preparatory delay interval. Fourth, we tested whether brain areas with preparatory activity have spatial selectivity by comparing activity during the preparation of contraversive and ipsiversive movements. Electrophysiologists have repeatedly demonstrated a bias in which neuronal activity represents contraversive saccade plans (Bruce et al. 2004; Platt and Glimcher 1997; Schall 1991).
Therefore we stipulated several a priori criteria that must be met before we can strongly conclude that an area codes for the planning of a specific saccade. Importantly, it must exhibit activity increases during the preparatory delay interval that are greater when the precise saccade metrics are known to the subject and greater prior to contraversive movements.
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METHODS |
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Twelve neurologically healthy participants (four females, eight males, between ages 21 and 35 yr, ten right-handed) were recruited for participation and were paid for their time. All subjects had normal or corrected-to-normal vision. Subjects gave written informed consent and all procedures were in compliance with the safety guidelines for fMRI research and approved by the human subjects Institutional Review Board at New York University.
Behavioral procedures
The experimental stimuli were controlled by E-Prime (Psychology Software Tools, Pittsburgh, PA) and projected (Eiki LC-XG100) into the bore of the scanner on a screen that was viewed by the subjects through an angled mirror.
Each subject performed eight runs of the deferred-saccade task (DST) yielding 144 total trials. A schematic of the experiment is illustrated in Fig. 1 A. In the known DST, a trial began when the gray fixation dot turned white to alert the subject that a new trial was beginning. Then, the fixation dot changed color to either green (prosaccade) or red (antisaccade) to instruct subjects as to whether they were to look toward (prosaccade) or away from (antisaccade) the target. Simultaneously, a target (1° cyan square) appeared anywhere between 5 and 15° left or right and 4 to 5° above or below the central fixation point (white dot). Subjects continued to maintain fixation during a long, variable, and unpredictable delay (7.5, 9, 10.5, 12, or 13.5 s) during which they were instructed to plan the eye movement and remain in a ready state to execute the eye movement as soon as the fixation point disappeared. Trials were separated by an intertrial interval (ITI) between 12 and 15 s to allow the hemodynamic response to return to baseline.
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Oculomotor methods
Eye position was monitored in the scanner at 60 Hz with an infrared videographic camera equipped with a telephoto lens (ASL 504LRO; Applied Sciences Laboratories, Bedford, MA; modified with a Sony HAD charge-coupled detector device) that focused on the right eye viewed from a flat-surface mirror mounted inside the radio frequency (RF) coil. Nine-point calibrations were performed at the beginning of the session and between runs when necessary. Eye-movement data were calibrated then transformed to degrees of visual angle using a third-order polynomial algorithm that fit eye positions to known spatial positions and scored off-line with in-house function-graphing software (GRAPES). Saccadic reaction times were estimated with semiautomatic routines that relied on the velocity of the eye reaching about 30°/s to determine the onset of saccades. We visually inspected each trial to validate the saccade onset time determined by the routine.
We excluded from analysis a total of 126 trials (an average of 4.5% of trials) in which subjects did not strictly comply with task instructions. The vast majority of these were breaks in fixation during the delay periods. We also excluded from analysis trials in which subjects made saccade errors. With regard to errors (i.e., prosaccades on antisaccade trials or vice versa), subjects made errors on 4.3% of known antisaccade trials, 12.3% of unknown antisaccade trials, 2.0% of known prosaccades, and 1.1% of unknown prosaccade trials. These trials were separately modeled in the general linear model (GLM) to remove any variance in BOLD signal associated with these epochs, but because they were so infrequent they were not further analyzed.
MRI procedures
MRI data were collected using a 3-T head-only scanner (Allegra; Siemens, Erlangen, Germany) at the Center for Brain Imaging at New York University. Images were acquired using custom RF coils (NM-011 transmit head-coil and NMSC-021 four-channel phased array receive coil; NOVA Medical, Wakefield, MA) placed over lateral frontal and parietal cortices. During each fMRI scan, a series of volumes were acquired using a T2*-sensitive echo planar imaging pulse sequence (repetition time, 1,500 ms; echo time, 30 ms; flip angle 75°; 24 slices; 3 x 3 x 3-mm3 voxels; 192 x 192 field of view). High-resolution (1-mm isotropic voxels) magnetization-prepared–rapid gradient echo three-dimensional T1-weighted scans were acquired for anatomical registration, segmentation, and display. To minimize head motion, subjects were stabilized with foam padding around the head.
fMRI data preprocessing and surface-based statistical analysis
Post hoc image registration was used to correct for residual head motion [MCFLIRT (motion correction using the Linear Image Registration Tool from Oxford University's Center for Functional MRI of the Brain)]. Additional preprocessing of the fMRI data was as follows. We band-pass filtered the time series of each voxel (0.01 to 0.33 Hz) to compensate for the slow drift typical in fMRI measurements (Biswal and Hyde 1997; Zarahn et al. 1997), divided the time series of each voxel by its mean intensity to convert to percentage signal modulation and to compensate for the decrease in mean image intensity with distance from the receive coil, and spatially smoothed the data to arrive at smoothness of 6 mm at full width at half-maximum.
We modeled each within-trial event (i.e., instruction + target, delay, and response) for each trial type (i.e., known/unknown x pro-/antisaccade) separately. Since the instruction and target were presented simultaneously in the known condition, these were modeled as a single event. The encoding of the instruction + target and the generation of the motor response were short transient events and were thus modeled with an impulse time-locked to the event convolved with a canonical hemodynamic response function (HRF) (Polonsky et al. 2000). The preparatory delay spanned 7.5–13.5 s and was modeled very well by the linear combination of a zero-order polynomial (i.e., boxcar) and a first-order polynomial (i.e., linear ramp). Both delay regressors spanned the delay period and were time-shifted by 4,000 ms to account for the hemodynamic lag. This time shift resulted in the least correlation among regressors. The parameter estimates from the first-order polynomial were used to estimate delay-period activity at the group level because at the individual subject level they predicted significant delay-period activity, which was confirmed by plotting the time series of the voxels identified by this parameter. Each of the independent variable regressors was entered into a modified GLM (Worsley and Friston 1995) for statistical analysis using VoxBo (http://www.voxbo.org).
For each subject, we used Caret (http://brainmap.wustl.edu/caret) for anatomical segmentation, gray/white matter surface generation, flattening, and multifiducial deformation mapping to the population-average, landmark- and surface-based (PALS) atlas (Van Essen 2005). Registering subjects in a surface space using precise anatomical landmark constraints (e.g., central sulcus, sylvian and calcarine fissures, etc.) results in greater spatial precision of the alignment compared with standard volumetric normalization methods (Van Essen 2005). Further, statistical maps for contrasts of interest were created using the beta weights estimated from each subject's GLM. These maps were then deformed into the same atlas space and t-statistics were computed for each contrast. We used a nonparametric statistical approach based on permutation tests to help address the problem of multiple statistical comparisons (Holmes 1996; Nichols and Holmes 2002). First, we constructed a permuted distribution of clusters of neighboring surface nodes with t-values >3.0. We chose a primary t-statistic cutoff of 3.0 because it is strict enough that intense focal clusters of activity would pass but not so strict that diffuse large clusters of activity are lost. In the case of a one-sample comparison, where measured values are compared with the test value of 0, the signs of the beta values for each node were randomly permuted for each subject's surface, prior to computing the statistic. In all, 1,000 iterations (N) of this procedure were performed to compute a permutation distribution for each statistical test performed. Then, we ranked the resulting suprathreshold clusters by their area. Finally, corrected P values at 
= 0.05 for each suprathreshold cluster were obtained by comparing their area to the area of the top 5% of the clusters in the permuted distribution, where the critical suprathreshold cluster size (C) at a t-score threshold of t >3.0 was C = N + 1. The permutation tests controlled for type I error by allowing us to formally compute the probability that activation of a given magnitude could cluster together by chance.
Region-of-interest (ROI) time series procedures
We used ROI-based analyses of the time courses of BOLD signal change. First, on each subject's high-resolution anatomical scans, we traced around gray matter of several ROIs including the superior precentral sulcus (sPCS), paracentral sulcus (paraCS), intraparietal sulcus (IPS), and transverse parietal sulcus (tPS). These ROIs were selected because of consistent activations from past studies and the preliminary investigations of the current single-subject data. The sPCS was defined as the dorsal segment of the precentral sulcus at the junction of the superior frontal sulcus. The paraCS was defined as the descending sulcus along the dorsal medial wall just anterior to the central lobule. The IPS was defined as the sulcus that divides the superior and inferior parietal lobules. The tPS was defined as the descending sulcus on the medial wall of the parietal lobe posterior to the cingulate and anterior to the posterior occipital sulcus. Next, within each ROI, we selected the 20 voxels (540 mm3) with the strongest main effect of the linear combination of all the task covariates. These voxels showed some consistent deviation from baseline during the task without being biased by any task component. Using a combined structural–functional criteria to select voxels for study is similar to the way electrophysiologists first identify neurons that respond to the task and then subject those neurons to further study.
We plotted the time series of BOLD responses, averaged across voxels within an ROI and averaged across subjects from analogous ROIs, time-locked to the presentation of the instruction cue or the signal to generate the saccade. In both cue-locked and response-locked plots, the average signal was baselined against the average response of the last two repetition times (TRs) before the trial began. For cue-locked plots, the average plot data included the TRs only up to the end of the delay so as not to contaminate the estimation with activity evoked by the motor response after the delay. This allowed us to combine data from the different delay lengths. The error bands were computed by taking the average of each individual's SE, which appropriately estimates the mean of the within-subject variance.
Contraversive effect
To test hypotheses about lateralized activity, we combined activity from both hemispheres of each ROI in the following way. Contraversive activity was defined as left ROI activity when the planned saccade was directed into the right visual field plus right ROI activity when the saccade was directed toward the left visual field. Similarly, ipsiversive activity was defined as left ROI/left saccade direction plus right ROI/right saccade direction. Although we find the same results when we analyze each hemisphere ROI separately, this procedure allowed us to pool data across hemispheres to increase our statistical power.
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RESULTS |
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By concurrently measuring eye movements we were able to characterize the distribution of saccadic response times (SRTs). About 90% SRTs fell in the normal range of visually guided saccades, i.e., 200–500 ms (Fig. 1B). A repeated-measures ANOVA (condition [Known AS, Known PS, Unknown AS, Unknown PS] x delay length [7.5, 9, 10.5, 12, 13.5 s]) showed that the conditions differed significantly by SRT [F(3,9) = 21.4, P < 0.0001]. Importantly, the SRTs did not differ as a function of the delay length [F(4,8) = 0.9, P > 0.5] and repeated-measures ANOVAs run separately for Known and Unknown trials confirmed that SRTs were not affected by the interaction between delay length and the type of saccade (i.e., AS, PS): Known: F(4,8) = 1.7, P > 0.22; Unknown: F(4,8) = 1.2, P > 0.36. Since SRTs were comparable across the delays used in the study, we collapsed across the delays in subsequent analyses. All follow-up pairwise comparisons were significantly different from one another at the P < 0.05 level (mean ± SE for Known AS = 374 ± 19 ms; Known PS = 349 ± 18 ms; Unknown AS = 434 ± 19 ms; Unknown PS = 321 ± 17 ms) except Known and Unknown PS trials (P = 0.3). The longer latencies for antisaccade compared with prosaccades presumably reflect the extra processes required for this type of movement (e.g., inversion of visually guided saccade vector). The difference in SRTs between anti- and prosaccades was significantly diminished when the target metrics were known in advance (
25 vs. 112 ms), suggesting that much of the computational differences happen during planning rather than execution of the saccade. Overall, the SRT data support our assumption that subjects did indeed prepare saccades in advance during the delay when the location of the target was known.
Surface-based statistical tests
Group statistical maps of activity specific to processing the instruction cue (i.e., pro-/antisaccade) and the visual target in the known DST are shown in Fig. 2 and listed in Table 1. We observed BOLD activation during the cue epoch in the known condition in the sPCS, iPCS, and paraCS in the frontal cortex and along the posterior and lateral segments of the IPS in the parietal cortex bilaterally (Fig. 2A, left). We observed less activation when the visual target was not shown until after the delay in the unknown trials (Fig. 2A, middle and right). The increased activation in these frontal and parietal areas could be due to several additional factors occurring during the early part of the trial in the known condition—that is, visual stimulation evoked by the target cue's appearance, visuomotor selection, inhibition of the tendency to saccade to the abruptly onset visual target, and the planning of the instructed saccade. At the cue epoch, the sPCSs bilaterally were the only areas that were significantly more active during antisaccade trials compared with prosaccade trials (Fig. 2B). Even in the unknown trials, when the specific metrics of the saccade could not be planned, the sPCS was significantly more active during antisaccades than during prosaccades (Fig. 2B, right). No areas were more active for prosaccades that antisaccades at this trial epoch.
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Finally we focused on the preparatory delay period of the known trials so we could test whether the side of the visual stimulation or the direction of the forthcoming planned saccade biased the activations we observed. During the planning of prosaccades, the side of visual stimulation is the same as the direction of the saccade (e.g., visual target on left, prepare saccade to left). Indeed, there was greater activation in the right hemisphere when the visual target and motor plan were to the left of fixation (Fig. 4 A, left column). During the planning of antisaccades, the side of visual stimulation is opposite the direction of the saccade (e.g., visual target on right and saccade to the left). The laterality bias can actually be addressed by comparing the hemisphere x side interaction. At least in the frontal cortex along the PCS, there was greater activation in the hemisphere opposite the direction of the motor plan (i.e., in the contraversive hemisphere) compared with the side of visual stimulation. At the whole brain level, there was not enough power to test these differences directly (e.g., contraversive AS > ipsiversive AS) since the number of trials is too small [i.e., (144 total trials)/(2 conditions: known/unknown)/(2 saccades: PS/AS)/(2 directions: contra-/ipsiversive)/(2 hemispheres) = 9 trials]. As we will see in the following text and plotted in Fig. 4B, we were able to collapse across hemispheres in the time-series analyses to boost our power and we find clear support for the trends in a contraversive bias we see in the data at the whole brain level shown in Fig. 4A.
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Next, we plotted the time series of BOLD responses from frontal and parietal ROIs to test several hypotheses. First, a region that plays a critical role in saccade preparation should show activity above baseline throughout the preparatory delay irrespective of the length of the delay. For each ROI, we plotted the BOLD time-course data for each of the five different delay lengths of the known DST, time-locked to the presentation of the instructional cue (Fig. 5). Initially, a transient response can be seen time-locked to the instruction cue and visual target. Then, the sPCS and IPS ROIs show activity above baseline that spans the entire delay, even when the subjects were in an active state of motor preparedness for nearly 14 s. Notice that in the late phase of the delay period, activity begins to increase or ramp up well before the end of the delay and well before the saccade was generated. The presaccadic ramping is even more pronounced if one figures in the hemodynamic delay of about 2 s (i.e., shifting the BOLD time course to the left). Finally, one can see another later robust transient response time-locked to the instructed saccade that is staggered in time in the order of the delay length. Moreover, the magnitudes of the saccade-related responses are also staggered by delay length, with larger responses following longer delays. To quantify this effect, for each subject we computed the slope of a line fit through the peak responses at each delay. The slopes were significantly greater than zero for the sPCS (P < 0.00001, slope = 0.045), IPS (P < 0.001, slope = 0.059), and paraCS (P < 0.02, slope = 0.020), but not tPS (P = 0.2, slope = 0.012) ROIs. For example, the sPCS response after the delay tended to increase in magnitude by 0.045% with every 1.5-s increase in the delay period. This can be accounted for by the integration of the ramping activity during the delay with a fixed magnitude response at the saccade period. The signal in tPS did not sustain throughout the delay period and activity in the paraCS slowly climbed above baseline during the latter half of the delay period. Together, the time-course data reveal robust delay-period activity in the sPCS and IPS when subjects are preparing a saccadic response. The activity during the preparatory delay cannot be attributed to processing the instruction or generating the saccade.
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= 0.05, one-tailed), comparing the magnitude of BOLD time-course data during the delay period, using as the metric of analysis the average of the time points beginning at 6 s to the end of the delay. During the preparatory delay period, BOLD activity in the sPCS and IPS was greater throughout the delay in the known compared with unknown condition for both regions (all values of P < 0.05). No significant differences were found in the paraCS and tPS.
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; Srimal and Curtis 2008) and during the maintenance of spatially directed attention (Ikkai and Curtis 2007). As in the current study, such spatial biases were lost when the memory-guided saccade was generated. This may be due to cross-hemisphere interactions between homotopic brain areas that nullify the differences that can be observed before the saccade is generated. Indeed, frontal eye field (FEF) neurons that code for conflicting saccade vectors are thought to inhibit one another around the time of saccade execution (Schlag et al. 1998; Seidemann et al. 2002), which would evoke BOLD responses that were together nonspatially selective for saccade direction. One implication is that a "push–pull" mechanism may be active only around the time of the saccade and not during saccade preparation. The other ROIs, including the IPS (Fig. 4B, right), did not show significant visual field (e.g., contralateralized) or motor response (e.g., contraversive) biases. |
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DISCUSSION |
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Ramping activity in the human frontal and parietal cortex
Single-unit recordings from monkeys have demonstrated that spike rates increase in saccade neurons in the frontal eye field (FEF) and lateral intraparietal (LIP) area prior to saccades and build up to a maximum when the saccade is finally triggered (Andersen et al. 1992; Bruce and Goldberg 1985). It is thought that these cortical neurons are important for specifying the metrics or spatial goals of saccades. To identify homologous activity in humans using a noninvasive technique, we used fMRI to measure cortical activity while subjects prepared to make saccades. Importantly, with our design we were able to separately measure activity during preparation and execution. Based on the surface-based statistical maps (Fig. 3A) of activity during the preparatory epoch, we found robust activations in the sPCS and IPS bilaterally. These same areas were also active when the saccade was finally executed. We confirmed the effects in these statistical maps by plotting the trial-averaged time series data from several individually defined frontal and parietal ROIs. Consistent with the preparatory delay period surface maps, we found that BOLD activity in the sPCS and IPS persisted above baseline until the saccade was generated (Fig. 4B). Activity in the sPCS and IPS began to increase, or ramp up, during the preparatory delay prior to the instruction to generate the saccade even when the amount of preparation time was randomly varied in duration (Fig. 5). Therefore the activity we measured is related to saccade preparation and not to the processing of the instruction or to generation of the saccade itself. We conclude that the delay-period BOLD signals we measured reflect neural activity of the human homologs of the monkey FEF and lateral intraparietal area (LIP).
Prospective coding of saccade metrics
If these delay-period BOLD signals do indeed reflect the activity of neurons involved in saccade planning, then we can make several predictions. First, we expected that activity would be greater when the precise metrics of the upcoming saccade were known. Indeed, the sPCS and IPS were more active when subjects could prepare a specific saccade compared with when they could not (Figs. 3A and 6), consistent with the hypothesis that the BOLD signals during the preparatory period reflect the activity of saccade-specific neurons in the human frontal and parietal cortex. Although much smaller in magnitude, we did observe significant activity during the trials in which subjects did not know the spatial goal of the upcoming saccade in both sPCS and to a much smaller extent in posterior IPS. Such activity likely reflects general, nonspatial anticipation of a future action akin to a "preparatory set" (Evarts et al. 1984). In previous fMRI studies, Connolly, Goodale, Munoz and colleagues (Connolly et al. 2002, 2005, 2007) have used a gap saccade task to show that activity in the human PCS, but not IPS, ramps up in general anticipation that a saccade will be required shortly. In this task, the fixation point disappears for a variable duration (0, 2, or 4 s) before the visual saccade target is presented and BOLD activity builds as the gap duration lengthens. The current study's unknown condition is most similar to the gap saccade paradigm and we also show strong evidence of a preparatory delay-period activity in PCS even when no spatial information about the saccade is available. In contrast to the gap saccade studies, we also find that the IPS is active during the preparatory delay, even when the spatial direction of the saccade is unknown. The magnitude of the effect is very small relative to when the saccade's direction is known, but it is reliable and consistent with other reports of nonspatially specific saccade preparation (Astafiev et al. 2003; Brown et al. 2007; Curtis and D'Esposito 2003b; Curtis et al. 2005; DeSouza et al. 2003). Moreover, these results are consistent with monkey electrophysiological work showing that parietal area LIP neurons increase in spike rate in anticipation of making a saccade even in the absence of the spatial direction of the saccade (Dickinson et al. 2003; Stoet and Snyder 2004). Similarly, our results in the PCS, the putative human FEF, are consistent with nonspatial preparatory signals recorded in monkey FEF neurons (Lawrence and Snyder 2006).
Although we did not test to see whether these preparatory signals were effector specific, other studies have done so and are relevant here. Within the FEF, surprisingly, there is no convincing evidence for effector specificity for eye compared with forelimb movements during preparatory delays in humans (Astafiev et al. 2003; Connolly et al. 2000, 2007; Levy et al. 2007) or monkeys (Lawrence and Snyder 2006); neural activity precedes planned saccades and forelimb movements. Within the monkey parietal cortex, area LIP, like the FEF, has traditionally been thought of as an oculomotor region (Andersen et al. 1992), but it also shows activity preceding planned eye and forelimb movements (Dickinson et al. 2003). Importantly, these signals, which may be related to a preparatory set, can often be observed when the specific movement cannot be prepared because its spatial goal is not available. The current study provides additional evidence that the putative human FEF and LIP are involved in such a simple preparatory set. In both monkey FEF and LIP, however, signals are often greater when the spatial goal of the movement is known and the metrics of the movement can be prepared. It is thought that nonspatially specific and spatially specific information combines linearly in these regions (Dickinson et al. 2003; Lawrence and Snyder 2006). Our data contribute to this literature by showing that indeed activity in the putative human FEF and LIP is greater when spatial information is available for motor planning; activity in sPCS and IPS was greater during the preparatory delay during the known compared with unknown trials (Fig. 6).
Second, we expected that the field of the visual cue or the direction of the planned saccade would spatially bias neural activity. Specifically, we tested the hypothesis that activity would be greater in the hemisphere opposite the hemifield of the visual target guiding the saccade, i.e., a visual contralateralized bias. We tested the alternative hypothesis that activity would be biased by the direction of the planned saccade, i.e., a motor contraversive bias. The maintenance of a planned prosaccade evoked delay-period activity that was greater in the hemisphere contralateral to the visual cue in the sPCS, a finding consistent with both the visual and the motor bias hypotheses. To differentiate between these two hypotheses, we relied on signal evoked during the planning of antisaccades, where the visual stimulus and saccade plan were in opposite hemifields. We found that differential activity was consistent with the contraversive hypothesis—that is, that activity was greater in the hemisphere opposite the direction of the saccade, but not during the visual cue guiding the saccade (Fig. 3). Similar conclusions have been made by Medendorp and colleagues (2005, 2006) regarding a region of the IPS that they refer to as "retIPS," for retinotopic IPS, which had contralateralized memory-guided saccade activity. In these studies, the authors demonstrated that retIPS primarily stores saccade goals as opposed to the visual cues specifying the saccade directions (also see Gottlieb and Goldberg 1999; Platt and Glimcher 1997). Our data support their claims and further extend them to the putative human FEF, which has not been demonstrated previously. Indeed, the response field of monkey FEF neurons is often located in the contralateral visual field (Bruce and Goldberg 1985; Marrocco 1978; Schall 1991; Tehovnik and Sommer 1997). Moreover, electrical stimulation of the human FEF induces saccades to the contralateral visual field (Blanke et al. 1999) and lesions disrupt contraversive saccades (Gaymard et al. 1999; Rivaud et al. 1994). Therefore our data provide new convergence with existing monkey and human studies.
Implications for the nature of persistent activation during spatial working memory delays
Persistent neural activity during the retention interval of memory-guided saccade tasks has been most consistently found in the sPCS and IPS, presumably human homologues of monkey areas FEF and LIP (for reviews see Curtis 2006; Postle 2006). Since the precise metrics of the forthcoming saccade are known during these memory delays, we have argued that a component of these signals may originate from neurons whose activity codes for the spatial goal of planned saccades (Curtis 2006; Curtis and D'Esposito 2006; Curtis et al. 2004). Such a prospective code is different from a retrospective code (e.g., the color, shape, or position of a past stimulus) that is most usually associated with theories of working memory (Curtis and D'Esposito 2003a; Fuster 2001; Srimal and Curtis 2008). Here, we show that BOLD signals persist in the frontal and parietal cortex throughout the delay interval even when working memory is not required since the target is continuously visible (i.e., there is no need to remember the location of the saccade target). Therefore the delay-period activity that is often interpreted as the signature of working memory maintenance may in fact rely on mechanisms related to prospective motor planning or spatial attention to bridge the temporal gap between a stimulus and its contingent response.
Patterns of activity in frontal and parietal cortex are often strikingly similar during visuospatial working memory, but the two cortices may make distinct contributions. One hypothesis suggests that the frontal cortex represents prospective information, whereas the parietal cortex represents retrospective information (Fuster 2001). Our recent work has generally supported this distinction (Connolly et al. 2002, 2005, 2007; Curtis 2006; Curtis and D'Esposito 2006; Curtis et al. 2004), but with the caveat that the distinction is relative. For instance, we show here that both sPCS and IPS are active when maintaining a saccade plan, just as they are when subjects maintain spatial information in working memory (Curtis and D'Esposito 2006; Curtis et al. 2004; Srimal and Curtis 2008) and just as they are when subjects maintain covert attention (Ikkai and Curtis 2007). If we assume attention is directed to the location of an external target (e.g., a visible cue) or an internal target (e.g., a memorized cue or transformed anticue), then attentional factors may be the commonality between spatial working memory and saccade planning (Awh et al. 2006; Corbetta et al. 2002; Postle et al. 2006; Srimal and Curtis 2008; Theeuwes et al. 2005). Similarly, all of the effects that we have called saccade planning could be mediated through attention mechanisms. For instance, attention could be fixed at the location of the planned saccade. Areas FEF and LIP may contain populations of neurons that form topographic maps of prioritized space (Goldberg et al. 2002; Serences and Yantis 2006; Thompson et al. 2005). A read-out of such a map of prioritized locations could be used not only to guide eye movements, whether they are visually or memory guided, but also to guide our attention.
Prosaccades versus antisaccades
We generally found greater BOLD responses to anti- compared with prosaccade trials, in agreement with other fMRI studies (Brown et al. 2006, 2007; Connolly et al. 2002; Cornelissen et al. 2002; Curtis and D'Esposito 2003b; DeSouza et al. 2003; Ford et al. 2005). As a side note, spike rate prior to antisaccades compared with prosaccades is lower in FEF neurons (Everling and Munoz 2000). The most likely reason for the discrepancy is methodological; BOLD signal is sensitive not only to neural spiking but also to synaptic activity (Logothetis and Wandell 2004), making it potentially sensitive to postsynaptic potentials related to inhibition. Nevertheless, several differences in task requirements are likely responsible for increased activity on antisaccade trials. One important difference is response-selection demands. On antisaccade trials, the saccade vector must be flipped 180° from the visually cued location. The difference in saccadic response times between pro- and antisaccades was much greater in the unknown than that in the known condition (25 vs. 112 ms), indicating that response selection took place as soon as the visual cue was given, before the preparatory delay in the known condition, but after it in the unknown condition. Consistent with this hypothesis, we found greater BOLD differences between anti- and prosaccades early in the trial (i.e., cue period) in the known condition and later in the trial at the response epoch in the unknown condition. These two contrasts resulted in greater activity in the sPCS and IPS for antisaccades (Figs. 2B and 3B). An alternative, but not mutually exclusive, hypothesis is that the differences emerge from implementing a "preparatory set" or the task rule (Evarts et al. 1984; Miller and Cohen 2001). We find support for this hypothesis as well. In the unknown condition, when the specific response cannot be selected, small increases in the BOLD signal were found in the sPCS and IPS as reported elsewhere (Astafiev et al. 2003; Connolly et al. 2002, 2007; Medendorp et al. 2005, 2006). Therefore both rule implementation and response-selection effects are reflected in the frontal and parietal cortices. These differences cannot be due to differences in inhibition, as in the known condition, because saccades to the abrupt onset of the visual cue must be inhibited on both pro- and antisaccade trials. Indeed, when subjects were instructed to select the transformed response in the known condition, antisaccades were only slightly delayed compared with prosaccades. At the response epoch of the unknown condition, antisaccade trials required inhibition of the "reflex-like" saccade to the cue, but prosaccade trials did not. The dorsal anterior cingulate and the dorsolateral prefrontal cortex were active during this BOLD contrast, suggesting their involvement in saccade inhibition (Brown et al. 2006; Curtis et al. 2005). However, other factors such as performance or conflict monitoring also could have contributed to the difference.
Summary
In the current set of experiments, we demonstrated that neural activity in the frontal and parietal cortices persisted while subjects were in an active state of maintaining a saccade plan. This activity predicted whether the subject knew the specific metrics of the saccade, whether the saccade was directed toward an internal representation (i.e., anticue location), and the direction of the planned movement. These findings are in accord with observations in homologous brain regions in the monkey and together support the hypothesis that both the human frontal and parietal cortices are involved in the spatial selection and preparation of saccades.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: C. E. Curtis, Department of Psychology and Center for Neural Science, 6 Washington Place, New York, NY 10003 (E-mail: clayton.curtis{at}nyu.edu)
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