Local Infusion of Scopolamine Into Intraparietal Cortex Slows Covert Orienting in Rhesus Monkeys

M. C. Davidson and R. T. Marrocco

Institute of Neuroscience, University of Oregon, Eugene, Oregon 97403-1254


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Davidson, M. C. and R. T. Marrocco. Local Infusion of Scopolamine Into Intraparietal Cortex Slows Covert Orienting in Rhesus Monkeys. J. Neurophysiol. 83: 1536-1549, 2000. There is accumulating evidence to suggest that cholinergic neurotransmission may play an important role in visuospatial attention, but the brain sites at which acetylcholine modulates attention are not well understood. The present work tested the hypothesis that the cholinergic influences within the intraparietal cortex are necessary for normal attentional shifting (covert orienting) in nonhuman primates. Two rhesus monkeys were trained to perform a visual, cued target detection task for liquid reinforcement. The animals pressed a lever to produce a visual display in which a central fixation point was flanked by two circles. Shortly after fixation was established, one of the circles brightened (cue), and a target appeared subsequently within one of the circles. Detection was signaled by a manual response and the reaction time to the appearance of the target was recorded. Four types of trials were presented. For valid cue trials, the cue and target were at the same spatial location; for invalid cues, cue and target were in opposite hemifields; for double cues, both cues were brightened but the target appeared in either the left or right circle; in no-cue trials, the cue was omitted. We localized the intraparietal region by recording attention-related, cellular activity with intracerebral microelectrodes. Among visually responsive cells in this area, valid cues presented to the receptive fields of visual neurons enhanced the responses to target stimuli in about half the cells and inhibited those responses in the remainder. In addition, some cells showed longer response latencies to invalid cues than to valid cues. We then infused scopolamine into attention-related activity sites and assessed its effect on performance. Scopolamine produced a dose-dependent increase in reaction times and decrease in performance accuracy that lasted more than 1 h. Neither vehicle injections in the same locations nor scopolamine outside the physiologically defined area produced any significant change in behavior. Under our conditions of measurement, we conclude that activity mediated by muscarinic cholinergic receptors within the intraparietal cortex is necessary for normal covert orienting.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Attention is a cognitive capacity that has been of interest to cognitive scientists for more than a century (e.g., James 1890). James posited that attention can be captured reflexively by novel stimuli or can be directed voluntarily to external or internal stimuli. Several varieties of visual attention have been identified (e.g., LaBerge 1995), including selective attention (attending to a location in space), divided attention (attending to at least two spatial locations) and sustained attention (attention to a location for long periods). The goal of the attentive process is to reduce the computational demands on an organism by selecting of a small number of items from a larger array for further processing. In the current work, we focus on visual selective attention evoked reflexively by visual stimuli.

Attention usually is linked to eye movements (e.g., Rizzolatti et al. 1987). Objects that appear suddenly in the peripheral visual field may draw both the attention and the eyes to the object location. This linkage, however, is not obligatory and overt orienting of the eyes may be disadvantageous in some circumstances. The shifting of the attention alone is referred to as covert orienting (Posner 1978) because outward signs of the movement are absent.

Because covert attentional shifts are extremely labile, it is critically important to have control over the location of the attentional focus. A paradigm that offers such control for stimulus detection is the covert target detection task (Posner 1980; Posner and Cohen 1984), and scores of studies are in agreement on the dynamics of attentional movements (e.g., Egeth and Yantis 1997; Umiltá 1988). Other paradigms have been used successfully for measuring target discrimination and identification (e.g., Spitzer and Richmond 1991).

In the covert-orienting task, subjects detect targets that are presented to the left or right visual hemifield (Posner and Cohen 1984) and signal detection with a manual response. The dependent variable is the reaction time (RT) to target detection. Before target appearance, a cue is presented that probabilistically forecasts the location of the subsequent target. Cues that reflexively attract attention to the target location (valid cues) produce faster RTs than those that draw attention elsewhere (invalid cues) (e.g., see Witte et al. 1996).

In recent years, the importance of cholinergic neurotransmission in visuospatial attention has become established firmly, in many cases with the help of the covert-orienting task. Both behavioral studies in humans and a large number of studies with animals have demonstrated conclusively that increases of cholinergic neurotransmission plays a critical role in attention (see Levin and Simon 1998; Warburton and Rusted 1993 for reviews). For example, covert orienting by human tobacco smokers is more rapid than that of nonsmokers (Witte and Marrocco 1997). In contrast, patients with Alzheimer's dementia, who appear to have reduced cholinergic innervation to the cerebral cortices, show covert-orienting deficits (Parasuraman et al. 1992). Other tasks yield similar findings. In rats, lesions of the cholinergic basal forebrain nuclei result in attention deficits in a five-choice, serial-reaction-time task that are ameliorated by injections of nicotine (Muir et al. 1994). Performance in sustained attention tasks also is altered by manipulating the brain levels of acetylcholine (Jones et al. 1992; Muir et al. 1994; Parrott and Craig 1992; Warburton and Rusted 1993; see Marrocco and Davidson 1998 for a review). For example, blockade of muscarinic cholinergic receptors with scopolamine impairs sustained and divided attention by slowing RTs, increasing error rates, and increasing distractibility (Callahan et al. 1993; Duka et al. 1995; Jones and Higgins 1995; McGaughy et al. 1994).

Reductions in brain cholinergic neurotransmission in monkeys through lesions of the cholinergic basal forebrain nuclei dramatically impair automatic attentional orienting but have no effect on spatial working memory tasks (Voytko et al. 1994). Two types of deficits are seen in the foregoing report: greatly slowed RTs to invalidly cued targets and moderate slowing of RTs to valid cues. Conversely, activation of cholinergic receptors with the nicotinic agonist nicotine reduces the time necessary to reorient the attention following invalidly, but not validly, cued targets (Witte and Marrocco 1997). In addition, systemic administration of the muscarinic antagonist scopolamine increases RTs for valid and invalid cues but did not change RTs in nonorienting trials (Davidson et al. 1999). Taken together, attentional orienting is facilitated by increased levels of acetylcholine and impaired by reductions in acetylcholine.

Of central interest in the present work is how neural structures control covert orienting and how acetylcholine modulates activity in these structures. The responses of cells in the parietal cortex are modulated by attentional demands (Bushnell et al. 1981; Goldberg et al. 1986; Mountcastle et al. 1981; Robinson et al. 1995; Steinmetz et al. 1994). However, they also are modulated by other factors, including the execution of saccadic eye and limb movements (Batista et al. 1999; Bremmer et al. 1998; Colby et al. 1996; Goldberg et al. 1986), the planning of eye movements (Andersen et al. 1997; Platt and Glimscher 1998), the representation of stimulus salience (Gottlieb et al. 1998), and the representation of multisensory space in relation to spatial object localization (Andersen et al. 1997). Many cells appear to be activated by more than one factor (e.g., Colby et al. 1996), perhaps because each has attentional components. In humans, PET imaging reveals increased glucose utilization in the parietal region during a covert-orienting task (Corbetta 1998; Corbetta et al. 1993).

Currently, little is known of the neurochemical bases of neuronal computations within the parietal cortex. Promising results in rats report attentional impairment after infusions of cholinergic immunotoxins into either cholinergic basal forebrain structures or the posterior parietal cortex (Bucci et al. 1997; Chiba et al. 1999; Risbrough et al. 1997). Partly on the basis of these findings, we hypothesized that the local infusion of the muscarinic cholinergic antagonist scopolamine into the intraparietal cortex in monkeys would selectively slow the orienting of attention. On the basis of our previous work (summarized in Marrocco and Davidson 1998), we also hypothesized that scopolamine would not affect RTs in tasks that do not evoke attention shifts. We found that scopolamine slowed RTs and reduced accuracy in a dose-dependent fashion for trials that required a shift of attention to a peripheral location but not for two types of control trials. A preliminary report of some of these findings has been published (Davidson and Marrocco 1998).


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Subjects

The subjects were two female rhesus macaques (Macaca mulatta) between 12 and 14 yr of age, weighing between 6 and 7 kg during the study. All procedures used with these animals were done in accordance with the Guide for the Use and Care of Laboratory Animals (National Academy of Sciences 1996) and were supervised by the university veterinarian. The animals were trained using water reinforcement. During a 2-wk period before data collection, ad libitum water consumption was measured to determine the animals' daily baseline intake. During data collection, water was removed from the animals' cages the evening before the experiment and the fluid intake was recorded after the next day's experiment. If the animals failed to drink their baseline amount, the difference between intake and baseline was given in the home exercise cage ~2 h after the session. Thus in any 24-h period, the animals consumed their normal volumes of fluid. Body weight, skin turgor, activity levels, and food consumption also were checked on a daily basis to be sure that weight loss or symptoms of other illnesses did not occur. The daily diet of lab chow was supplemented with peanuts, raisins, and fresh fruit.

Surgery

Using isoflurane anesthesia and sterile procedures, a head fixation socket was attached surgically to each animal before the start of training. The socket was anchored to the skull with stainless steel screws coated with dihydroxylapatite, and dental acrylic was applied to cover the base of the socket and anchoring screws. In each monkey, we also placed a stainless steel recording well on the skull of the right hemisphere and centered it at Horseley Clark locations AP -5, ML 10 in the vertical plane. In addition, a scleral search coil (Judge et al. 1980) was fitted around the right eye. Postoperative care over a period of two weeks consisted of prophylactic administration of systemic antibiotics, ophthalmic antibiotic ointment, and pain relieving medication (buprenorphin).

The recording well was designed by Crist Instrument and was used in conjunction with a removable nylon grid that always was placed at exactly the same position in the well on each recording day. In this way, a given hole in the nylon grid was linked to the same stereotaxic location and allowed an electrode to be repositioned in nearly the same brain site on different days (Crist et al. 1988).

Apparatus

The monkey was placed into a primate chair (Crist Instruments, Damascus, MD) at the start of each session. Its head was immobilized by attaching it to the chair, using a bolt designed to fit into the head socket. Two vertical and two horizontal 24-inch-wide magnetic field coils surrounded the animal's head and the upper portion of the primate chair. The monkey, chair, and field coils then were placed into a large Formica chamber with a glass-front window. The stimuli were projected onto a tangent screen outside the chamber window with a computer-driven, active-matrix, LCD projection panel (Panasonic) that was transilluminated with an overhead projector. The screen was 40 cm from the animal's eyes. A video camera allowed the experimenters to monitor the animal's behavior continuously.

A Northgate 386 computer was used to run CORTEX, a program for conducting neurophysiological and behavioral experiments that was provided to us by Robert Desimone of the National Institutes of Health. Graphics were produced with a Pepper SGT-plus graphics card (Number Nine Computer, Lexington, MA), and the measurement of eye position, registration of bar contact closures, and the activation of the reward solenoid were controlled by a A/D board (Computer Boards, Cambridge, MA)

Peripheral cued target detection (CTD) training

The details of the training protocol are similar to those used previously (Witte and Marrocco 1997; Witte et al. 1996). Through computer monitoring of eye position, the animal was trained to maintain fixation within an area of 1.0 deg2 around a small, white spot on the monitor for ~1-2 s. Next, it learned to press a bar that triggered a microswitch and produced the fixation spot. Successful fixations of criterion duration were rewarded, but failure to make or maintain fixation or making temporally inappropriate presses or releases caused the trial to be aborted.

The animal then was trained on the peripheral version of the cued target-detection task. In this task, a bar press produced the central fixation spot (0.5° diam) and two flanking circles (5.0° diam, 0.5° line width, 50 cd/m2 luminance) presented on a background whose luminance was 0.1 cd/m2. The distance from the spot to the geometric center of the circles varied between 5 and 20°, depending on the location of the cell's receptive field (see Fig. 1, top 4 illustrations). The location of the circles was always symmetric about the fixation point. After 500-1,500 ms (determined randomly), one or both of the circles brightened to 75 cd/m2, which served as a cue to attract attention. At 100, 400, or 700 ms after the cue onset (the cue-target interval or CTI), a circular target (0.5° diam) was presented at the center of the circle. The cue and target remained on until the bar was released. The animal was rewarded for releasing the bar between 100 and 850 ms after target appearance. RTs under 100 ms were considered anticipatory and caused the trial to be aborted. Eye movements beyond the 1.0° fixation window or incorrect bar performance also caused the computer to abort the trial. Incorrect trials were not repeated.



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Fig. 1. Cue conditions used in this study. Each row represents the appearance of the display during the trial that uniquely characterizes the trial type. Valid cue: cue (brightened circle) and target (small bright spot) occur in same spatial location. Invalid cue: cue and target are in opposite hemifields. Double cue: both cues are presented, target (white spot) appears on either side. No cue: both cues are omitted, target appears on either side. Fix: only fixation point is presented and is brightened as a cue. Fix + distract: same as Fix, but 2 irrelevant, nonbrightened circles flank the fixation point.

There were four cue conditions in this task (see Fig. 1). In the valid cue condition, the target appeared at the center of the circle (57% of trials). In the invalid-cue condition, the target appeared in the center of the nonbrightened circle in the opposite hemifield (14.3% of trials). The difference in RTs between the validly cued and invalidly cued trials (validity effect, VE) was used as a measure of the effects of directed attention on target detection. The ratio of valid to invalid trials was 4:1.

The cues in the valid trials were spatially informative and provided a general warning that the target would appear within 700 ms. To assess the individual contributions of each type of information, two additional kinds of trials were presented. In the double-cue condition (14.3% of trials), both circles were brightened simultaneously. No spatial information was provided by this cue, but the abrupt onset of the cues provided the subject with the same temporal warning that occurred in valid and invalid trials. In the no-cue condition (14.3% of trials), the cue was omitted altogether so that neither explicit spatial information nor general warning information about the target's subsequent appearance were present. Some implicit temporal information may have been present in the no-cue trials, as the certainty of target appearance increased with time. The difference in RTs between double- and no-cue trials (alerting effect, AE) measured the effects of the warning cue on target detection. The double and no cues are shown in Fig. 1, middle.

Fixation tasks

The monkeys also were trained on a fixation task and a fixation plus distractors task. These tasks assessed whether scopolamine impaired RTs when the animal was attending to a location but did not have to orient its attention to another location for a reward. In the fixation task (Fig. 1, fix), the animal's bar press produced the fixation spot, which remained at a fixed luminance for a variable period (500-1,500 ms, determined randomly). At the end of the random period, one of the three CTIs (100, 400, or 700 ms, determined randomly) preceded the brightening of the fixation point, and the animal's task was to release the bar as soon as it saw the brightening. The initial and brightened luminance values were the same as those used for the cues in the cued target detection task. In the fixation plus distractor task (Fig. 1, fix + distract), the bar press produced both the fixation point and two peripheral circles, the same array used in the cued target detection task. The animal's job was to detect the brightening of the fixation point and ignore the distractors. The intervals before brightening and the luminances of the fixation point plus distractors were the same as those for the fixation and cued target detection tasks. During all session, cued target detection trials and all fixation tasks occurred in different blocks. Within the fixation block, fixation and fixation plus distractor trials were presented in random order. We defined the "distractor effect" (DE) as the mean RTs for the fixation plus distractor task minus those for the fixation task alone.

Drug administration

Scopolamine hydrobromide, a nonspecific muscarinic cholinergic antagonist, was used in this study so that the behavioral effects after local infusion could be compared with those from a previous study using systemic injections (Davidson et al. 1999). Scopolamine (Sigma Chemicals, St. Louis, MO), was dissolved in sterile Ringer solution, and 1.0 ml of the solution was infused into the intraparietal cortex during a session. Across sessions, monkeys received infusions of either vehicle alone, or 4 or 7 mg/ml of the drug, with the order of drug dosages determined randomly across sessions. With these doses, overt signs of sedation or excitement were not observed in either monkey.

Before an infusion was made, the dura at the selected grid was location first anesthetized with a drop of xylocaine (2%). After a few-minute delay to prevent contact of the drug with the cortical surface, the dura was pierced with a sharpened needle. A recording microsyringe (Crist Instrument) containing the scopolamine then was placed into the grid hole. The shaft of the syringe was attached to the microdrive head and lowered to regions of interest.

In each animal, scopolamine was given before 10 data collection sessions, with 5 sessions for each dose. At least 2 days elapsed between drug sessions to minimize carry-over effects. Ringer solution was injected before five data collection sessions in each monkey.

Single and multiunit recording

Electrophysiological recording was done in two parts of this study. In the first part, we attempted to locate neurons in areas LIP and 7a that were attentionally modulated (e.g., Robinson et al. 1995; Steinmetz et al. 1994). Single cells were recorded in the parietal lobe of both monkeys with tungsten microelectrodes (WPI, 2-5 MOmega ). Well isolated action potentials were amplified, converted to standardized pulses, and accumulated into bins for the construction of peristimulus time histograms (PSTHs). In the second part, we used the sites at which attentionally modulated activity was found as targets for scopolamine infusion. Multiunit activity was recorded with the tungsten recording electrode attached to the microsyringe to confirm the intended infusion site. Because we could not retest the same units recorded previously, we relied on the repeatability of electrode placement into the same brain site at different times, the presence of spatially restricted, visually driven multiunit activity at the same receptive field locations as that recorded previously with single-cell electrodes, and multiunit activity that was modulated with saccadic eye movements (see Andersen et al. 1997). None of these criteria is absolutely reliable, but placement error reportedly is not greater than the distance between adjacent electrode guide holes (1 mm) (Crist et al. 1988).

Forty-two penetrations were made across the central area of the well to map the location of the sulcus. Depending on the coordinates, penetrations recorded mixtures of active and passive somatosensory responses, visual and saccade-related responses, and undifferentiated background activity. The dense sampling permitted us to construct a rough map of the intraparietal sulcus, which helped us guide subsequent penetrations. While the animal fixated, stimuli were flashed in various locations to locate the cell's receptive field and its optimal stimulus. The location of the field then was used to precisely position the stimuli used in the cued target detection task.

Behavioral data

During the 2-yr period of this study, RTs were collected first after systemic administration of either saline or scopolamine (saline trials interspersed between drug trials), next during electrophysiological recording (no drugs or vehicle administered), and finally during local scopolamine administration (Ringer trials were included to test for any nonspecific effects of the infusion procedure). The datasets from each of these will be referred to hereafter as systemic, cellular, and local, respectively. To assess the effects of scopolamine, drug trials were compared with saline or Ringer trials within the same dataset. To determine the stationarity of the performance over 2 yr of data collection, the mean RTs (with and without drugs) were compared across datasets. To assess whether the manipulations caused long-term damage, RTs were collected during three sessions (725 trials) at the end of the study for comparison.

In each of the datasets, correct trials were separated from incorrect trials (bar release prior to target onset, loss of fixation, or failure to release bar within 850 ms), and the percent correct was calculated by multiplying the ratio of the correct trials to the total trials by 100. The validity and alerting effects were calculated by subtracting the overall mean valid cue RTs from the mean invalid-cue RTs, and the overall mean double-cue RTs from the mean no-cue RTs, respectively. The distractor effect represented the mean RTs for the fixation + distractor trials minus those for the fixation trials alone. All variances are reported as means ± SE.

Repeated-measures ANOVAs1 were used to test for drug effects. In the first ANOVA, we used a sessions within monkey by drug by cue design. Because the incorrect trials were not replaced during the sessions, there were usually unequal (though large) numbers of trials for different cue types and the Type III sum-of-squares estimates were used to compute F ratios.

A second ANOVA was designed to test for main effects and interactions across the visual fields. Only peripheral target conditions were included in this analysis because cue type and visual field were not completely crossed (the fixation and fixation plus distractor conditions always appeared at the center of the field). A third ANOVA was performed to compare the effects of cue-target interval on performance. We report ANOVA results as F values and significance levels.

Individual planned comparisons were made with multiple regression analyses, which assessed the change in slope of the performance measure with increasing drug dose (Keppel 1982). These analyses involved three partial factorials, each with a single degree of freedom, that contrasted reaction times for the VE, AE, and DE for the low dose versus control and high dose versus control comparisons. The results of the regression analyses are given as probability values only. Accuracy analyses were performed by comparing percent correct against drug dosage and percent error against type of error and drug dosage.

Cellular data analysis

Statistical comparisons of the data from different conditions were made with repeated-measures ANOVAs. For these analyses, the average spike activity within a 500-ms time window (divided into 50-ms bins) was computed for each stimulus event across each cueing condition. The average for each 50-ms bin was based on the number of spikes (for all correct trials within that condition) multiplied by 20 to derive the number of spikes per second. The statistical significance levels were Bonferroni corrected.

Histology

The length of this study made it impossible to reconstruct all of the penetrations made during injection and recording. Therefore we introduced DiI-coated microelectrodes at known locations within the well of monkey A (coordinates -5,10; 3,10; -13,10) 2 days before perfusion. These marks were used to correlate our penetrations with actual brain sites and to assess for tissue shrinkage. Two days later, the monkey was killed with an overdose of pentobarbital (100 mg/kg) and intracardially perfused with normal saline followed by 10% formalin. A slab of tissue containing the penetrations was removed and placed in sucrose for 7 days until it submerged. Fifty-micrometer sections were cut on a freezing microtome, and alternate sections were stained with Luxol fast blue for myelin and cresyl violet for cells. The sections containing the DiI tracks were not stained.

Analysis of the sections showed that the tracks penetrated the region bounded by LIP medially, area 7a laterally, and VIP ventrally. The DiI penetration at the center of the well (-5 medial, 10 lateral) passed through area 7a and ended ~1 mm dorsal to area VIP (see Fig. 2). Five additional penetrations that moved by 1-mm increments laterally (noted by arrows) are also seen in Fig. 2. The vertical dashed lines represent ML coordinate values.



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Fig. 2. Unstained section of right parietal cortex. LIP, lateral intraparietal cortex; VIP, ventral intraparietal cortex. Numbers at bottom of figure represent stereotaxic ML coordinates. DiI penetration was made through the center of the recording well (AP -5, ML 10). Electrode enters the cortex in area 7a and extends downward toward VIP and adjacent white matter. Five electrode penetrations (right-arrow) are seen in this section.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Performance stability

Across the 2-yr period of this study, 36,129 trials were generated by both monkeys. Overall, the animals performed at 71% accuracy. The overall mean accuracy for both monkeys on nondrug trials for the systemic, cellular, and local datasets was 72, 70, and 70% correct, respectively. Although the overall accuracy is a somewhat crude measure, it shows that the RTs differed, on average, by no more than 2% over the 2-yr period.

Within a single nondrug session, RTs ranged from 200 to 800 ms, but the distribution was highly skewed toward shorter RTs. The majority of RTs fell between 300 and 500 ms. There was a tendency for RTs to increase toward the end of the session, perhaps due to fluid satiation or fatigue.

Behavioral RTs

The behavioral RTs for both tasks are shown in Table 1. Significant RT variations were seen within and between datasets for both monkeys. For each dataset, mean valid trials were significantly faster than invalid trials and the majority (5/8) of double trials had faster RTs than no-cue trials [e.g., F(5,5) = 11.1, P < 0.01 for the systemic dataset]. In addition, the overall RTs for the shortest CTI was ~30 ms slower than that for the longest CTI [F(2,2) = 63.8, P < 0.05; data not shown]. Thus the monkeys' baseline behavioral measures were consistent with each other and comparable to previous published work (Bowman et al. 1993). In contrast, mean RTs varied by as much as 70 ms between datasets (e.g., compare systemic values with local and cellular values), and there were slight differences in overall mean RTs between monkeys. In the systemic and cellular datasets, monkey A responded between 29 and 55 ms faster than monkey B but was 21 ms slower in the local dataset. Thus the differences are inconsistent and probably not biologically meaningful.


                              
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Table 1. Comparison of mean RTs for the four, drug-free phases of the experiment

Table 2 shows the mean VE, AE, and DE for each monkey. Monkey A showed larger mean VEs (49 ± 5 ms) and DEs (56 ± 17 ms) than monkey B (29 ± 15 and 32 ± 10 ms, respectively) but consistently smaller AEs (-3 ± 10 vs. 30 ± 18 ms). Within monkeys, the majority of indices between datasets remained within 30 ms of each other for the duration of the study. Taken together, both the overall RTs and the specific indices that gauge attentional orienting, alerting and distraction suggest that the animals' performance was sufficiently stable to permit us to measure scopolamine-induced changes accurately.


                              
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Table 2. Comparisons of computed effects for the four, drug-free phases of the experiment

Cellular recording

A total of 85 cells were recorded in this analysis, and 46 (55%) had well-defined receptive fields located in the upper or lower quadrants of the contralateral visual hemifield. The PSTHs for these cells showed modest activity changes to the onset of the CTD display, moderate increases to the cue onset, and large increases to the presentation of the target. Neuronal responses to cues at the shortest cue-target intervals did not produce significant refractoriness for target responses, perhaps because the cues themselves evoked weak responses from the cells. Of the 46 cells, 21 cells (46%) showed responses to the receptive field target that were significantly (P < 0.05) greater than the spontaneous rate. Of these, 12 (26%) were enhanced and 9 (20%) were suppressed. The remainder (25 cells, 54%) were statistically unaffected by stimuli in this task.

Among the cells with significant excitatory responses, we observed two types of response patterns. In the variable latency type (6/12 cells), the neuron's response latency varied according to cue type. If the cue and the target were in the receptive field, the target response latency was 80-100 ms. However, if the cue and the target were in opposite hemifields, the target response latency was ~280 ms. In the fixed latency type (6/12 cells), the cell's target response latency was 80-100 ms regardless of the cue type.

A representative example of a variable latency cell is shown in Fig. 3. The receptive field was located contralaterally ~10° left and 20° down from fixation (Fig. 3A). The stimulus display was aligned so that the target fell within the receptive field center. One circle was positioned in the receptive field and the other at a diametrically opposed location on the other side of the visual meridian. Valid cues (brightening of the receptive field circle) were followed by brisk target responses that began ~80 ms after target onset and peaked ~100 ms (Fig. 3B). In contrast, the target stimulus that followed an invalid cue (Fig. 3C) evoked a burst of activity beginning ~220 ms and ending ~280 ms (Fig. 3D). Target responses during double- and no-cue trials were smaller than those during trials with valid or invalid cues (data not shown). Latency variations could not be reliably discerned among cells that were suppressed by the targets.



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Fig. 3. Responses of variable latency intraparietal neuron to cued stimuli. Cue-target interval = 400 ms. A and C: locations of cues and targets with respect to the cell's receptive field for the valid and invalid conditions, respectively. Cue is denoted by the thick, darkened circle. Target is shown as small circle inside the receptive field (square). B and D: responses to valid and invalid cues, respectively. B and D, top: response rasters (n = 12) aligned to target onset; bottom: summed peristimulus time histograms (PSTHs) are shown, also aligned on the target onset. Bottom lines show horizontal and vertical eye position during the trial. Latency of the excitatory response is longer for the invalid cue than the valid cue. Bin width = 100 ms. Cell 51897.

An example of a fixed-latency cell is shown in Fig. 4. The responses to the target after both the valid (Fig. 4A) and invalid cues (Fig. 4B) began at ~100 ms and peaked at ~120 ms. No responses to the target were elicited during either double- or no-cue trials.



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Fig. 4. Responses of fixed latency intraparietal neuron to cued stimuli. All conventions are the same as those shown in Fig. 3. Latency of the excitatory response is approximately the same for the valid and invalid cues. Cell 50597.

To assess the pattern of responses to different cues, we averaged target responses for each cue type across cells and the data for the 12 enhanced cells are shown in Fig. 5. The magnitude of the responses depended on the type of cueing. Valid cues (Fig. 5A) produced phasic increases in target response at ~100-200 ms that were significantly larger than those evoked by the invalid, [Fig. 5B; F(1,22) = 5.45; P < 0.05] and no-cue [Fig. 5D; F(1,22) = 8.6; P < 0.05] conditions. No other bins had significantly elevated responses (P > 0.1). It is particularly interesting that the responses during valid cue trials were significantly greater than those during double-cue trials [Fig. 5C; F(1,22) = 7.1; P < 0.05] because, in both cases, a cue preceded the target in the receptive field. However, the valid cue was spatially predictive whereas the double cue was not. This suggests that the response enhancement reflects activity specifically related to spatially cued, attentional orienting. Neuronal activity levels for the enhanced cells in the fixation tasks showed a uniform rate of firing across trials whose magnitude resembled prestimulus levels (data not shown). This result is consistent with the idea that cellular activity is linked to attentional orienting away from the fovea. It also indicates that the receptive fields did not extend into the foveal region. Lastly, the nine cells that showed reduced activity after the target showed no significant firing rate changes (P > 0.2) for any of the cue conditions, possibly because of the low rate of cell activity.



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Fig. 5. Average PSTHs for the sample of 12 cells that showed significant, attention-related changes in neuronal activity. Stimulus configuration relative to receptive field (RF) is shown to the left of each PSTH: FP, fixation point; TAR, receptive field stimulus/target. A: valid-cue trials; B: invalid-cue trials; C: double-cue trials; D: no-cue trials. In the PSTHs of A-C, the cue appeared 400 ms before the target onset. Bin width = 100 ms.

To illustrate the range of attention-related response modulation, we calculated a modulation index by dividing the valid cue responses by the double-cue responses for our 46-cell sample. The distribution of values is shown in Fig. 6. Values greater than unity denote cells that were enhanced and those below unity are those that were suppressed. Significant reductions in activity (square ) and increases in activity (, P < 0.05) are shown. The majority of cells (26/46) were statistically unmodulated by the cues. Of those that were modulated, a greater number of cell responses (12/46) were enhanced than were suppressed (8/46) in this paradigm. The range of values is comparable to that reported in other studies (see Colby et al. 1996), but the present distribution is more bimodal than that reported by Colby et al. (1996).



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Fig. 6. Modulation index for sample of 46 visually responsive neurons. , cells that were significantly suppressed by target in the cued target detection (CTD) condition; black-square, cells that were statistically unaffected by the target; , cells that were significantly enhanced by the target. See text for details.

Local infusions: cued target detection task

The brain locations in and near those that yielded attention-modulated neuronal responses served as the targets for drug infusion. Of the 42 locations recorded in both monkeys, attention-related neural enhancement or inhibition was detected at 12 sites (10 in monkey A and 2 in monkey B). As the cellular experiments and local infusions were done on different days, we verified receptive field position and stimulus-related activity at each site with multiunit recordings before the infusions were made.

Local infusion of scopolamine produced significant behavioral changes in both RT and response accuracy (Fig. 7) that lasted between 60 and 90 min. Injections were made at locations between AP -2 and AP -7 and between ML 7 and ML 11, but not at every possibly location so as to minimize tissue damage. Figure 7, A and B, shows the RT data for monkeys A and B, respectively. Scopolamine slowed RTs in a dose-dependent manner: Ringer infusion yielded RTs between 410 and 450 ms, the low dose of scopolamine increased RTs to between 490 and 540 ms, and the high dose increased RTs to between 565 and 620 ms. The location of the maximum RT slowing in monkey A (AP 9, ML -2) occurred within 1 mm of that in monkey B (AP 10, ML -3). Figure 7, C and D, plot response accuracy for monkeys A and B, respectively. Increasing drug dose progressively decreased accuracy at most sites tested. The locus of maximum accuracy impairment for monkey A (AP 11, ML -4) was ~2 mm from the corresponding site in monkey B (AP 9, ML -6). Each of these effective drug sites also was a site at which attention-related neural modulation was observed. In monkey A, infusions of drug in each of the 10 attention-defined sites produced behavioral impairment. Both of the two attention sites recorded in monkey B were also effective drug sites.



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Fig. 7. Effects of scopolamine infusion on RTs and accuracy in the CTD task for monkey A (A and B) and monkey B (C and D). Each data point is anchored to the stereotaxic location from which it was obtained and its height shows the overall mean behavioral measure for that site. open circle , Ringer solution;  , 4 mg/ml scopolamine; , sites infused with 7 mg/ml scopolamine. A dose-dependent increase in RTs and a decrease in accuracy is observed for each monkey after scopolamine.

To obtain a measure of the dose dependency of the behavioral effects for both animals, we averaged the RTs for each dose for all injection locations and the results may be seen in Fig. 8, left. An ANOVA, which compared the factors of monkey, drug, cue type, and session, showed a main effect of drug [F(1,8) = 96.5, P < 0.001]. Each animal showed the same data trends (Fig. 8, top left), and the main effect of monkey was not significant (P > 0.5). Planned comparisons showed that RTs for monkey A were increased significantly for both the medium and high local scopolamine doses, whereas only the high dose produced significant elevation in monkey B. A comparable pattern was seen for accuracy scores. Local scopolamine produced significant impairments of response accuracy [low dose, t(8) = 2.7; P < 0.05; high dose, t(8) = 3.2; P < 0.02] in both monkeys (monkey A, P < 0.02; monkey B, P < 0.05).



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Fig. 8. Comparison of local (left) and systemic (right) scopolamine on RTs and accuracy in the CTD task for each monkey. Significance asterisks (P < 0.05) have been placed about the data point for monkey A and below the data point for monkey B.

If the results produced by a systemic drug injection are due to action at a single brain site, then the data ought to compare favorably with local drug infusions into that brain site. The extent to which the datasets differ suggests that the systemic drug is having significant effects at other locations. To test this conjecture, the present data were compared with the systemic data collected in the study of Davidson et al. (1999) using the same animals (Fig. 8, right). Qualitatively, both monkeys showed the same dose-dependent increases in RTs and response accuracy for each route of injection. However, the overall mean RTs in the systemic dataset are ~100 ms faster than those in the local infusion dataset (compare top left and right). Note that the RTs for the zero dose (Ringer) of the local infusions also are elevated, suggesting that the increase may be related to the infusion procedure. In addition, a decrease in accuracy is evident as a nonsignificant trend in the systemic dataset but is statistically significant in the local infusion dataset (bottom left and right). However, for these comparisons, the zero dose values are roughly equivalent. In summary, the two routes of injection produce similar but not identical effects on performance. Unresolved is whether there may be a significant impact of the procedure itself.

If each infusion produced nonspecific tissue damage, then RTs should increase and accuracy should decrease with successive infusions. To test these hypotheses, mean RT was plotted against injection number. For monkey A, Ringer injections and both drug doses showed RT increases followed by RT decreases with increasing injection number. For monkey B, there was a slight decrease in mean RTs with increasing injection number for both Ringer and scopolamine. Statistically, there was no significant change for either monkey. Changes in accuracy scores were also not significant. It should be noted that this analysis ignores infusion location, and it has been shown that drug effect varies with stereotaxic location (e.g., Fig. 7). Thus it is not entirely clear whether the RT slowing for monkey A is due to the sequence number or the location. More data are needed to unequivocally eliminate progressive changes, but the present analysis suggests that damage was minor.

We also tested the idea that significant tissue damage during the study should result in elevated RTs collected 1 mo after the end of the study. To answer this, we examined 428 (drug-free) trials for monkey A and 297 trials for monkey B obtained after a 30-day hiatus. The mean RT for monkey A was 368 ± 16 ms, that for monkey B was 441 ± 8 ms, not significantly different from the 394 ± 28 or the 423 ± 20 ms values found for monkeys A and B, respectively, in the systemic dataset (P > 0.2).

Last, if the infusion procedure itself was the cause of the elevated RTs, then the deficits should be seen primarily for cues that appeared in the left visual field because the penetrations were restricted to the right hemisphere. RTs for targets in the left visual field were slowed compared with the right (P < 0.01), but the maximum difference observed was 40 ms, substantially less than the 100 ms difference in overall RT seen in Fig. 8. Taken together, our findings thus suggest that the altered performance measures were of relatively short duration and probably not due to permanent tissue damage.

Because the effects of scopolamine on both monkeys was comparable, we combined the data and illustrate the results in Fig. 9, A and B, leftmost column). The comparison again shows the similarity of the dose dependencies of overall mean RTs for the two routes of injection. In Fig. 9, C and D, we compare mean validity and alerting scores for the systemic and local injections. Significant, dose-dependent decreases are seen for the validity effect, but nonsignificant, dose-independent changes are seen for alerting. These changes in the validity effect from the different routes of injection were produced despite the 100-ms difference in overall RTs shown in Figs. 8 and 9. The same patterns were seen in the individual data (not shown). Thus the mean data show that scopolamine through local infusion or systemic injection produce equivalent effects on validity scores, an index of attentional orienting.



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Fig. 9. Comparison of systemic (top) and local (bottom) administration of overall mean RTs (A and B) and validity/alerting effect size (C and D) for CTD and fixation tasks. Data in A and B reproduced with permission from Springer-Verlag. Bars represent grand means across cue types, monkey, and cue-target interval (CTI). Asterisks represent significant differences (P < 0.05). A, left: mean RTs for CTD task slow with increasing systemic dose; right: mean RTs for fixation tasks unaffected by scopolamine. B, left: RTs increase with increasing local doses of scopolamine; right: RTs for fixation tasks are unaffected by scopolamine. C, left: validity effect decreases with increasing doses of scopolamine; right: alerting effect does not systematically change after scopolamine. D, left: local scopolamine decreases validity effect but has no significant effect on alerting (right).

The validity index can change as a result of alterations in either valid or invalid-cue RTs. Davidson et al. (1999) demonstrated that validity effect reductions after scopolamine were due to a moderate increase in valid cue RTs and a smaller increase in invalid-cue RTs. The valid trials increased on average (both monkeys) by 47 ms, whereas the alerting effect increased by 23 ms from control to high dose. In a comparable fashion, validity effect increases in the current data resulted from a 27-ms average increase in mean valid cue RTs and a 4-ms increase in invalid-cue RTs. Thus the responses to individual cue types were also similar between routes of injection. In summary, the similarities between the two routes of drug administration provide evidence that the bulk of the behavioral changes may be due to the action of scopolamine in the IP cortex.

Local infusions: fixation tasks

RTs for the fixation plus distractor task were significantly slower than for the fixation task alone [F(1,120) = 59.0, P < 0.001] for each animal (24 ms for monkey A, 49 ms for monkey B). This result suggests that the distractors drew attention away from the fixation point and slowed RTs. However, these differences were not statistically affected by the presence of scopolamine. Neither the low or the high dose consistently altered RTs to either stimulus condition.

We also compared the local and systemic datasets for both monkeys in the fixation tasks. (Fig. 9, A and B, right). Although neither the systemic nor the local injections in the combined data changed RTs in the fixation tasks at any dose, the same overall RT difference is seen between routes of injection that was seen for the CTD task. No consistent differences in accuracy were observed across the doses of scopolamine tested in this study (data not shown). Thus scopolamine's effect appears to be limited primarily to attentional orienting.

Visual field effects

We found no main effects or second-order interactions between drug and visual field in the systemic data sets. In the local infusion dataset, however, scopolamine significantly [F(1, 8) = 98, P < 0.001] slowed the RTs in the left visual field in a dose-dependent manner. The slowing was much larger for valid and invalid trials (~40 ms) than for double- and no-cue trials (10 ms). The cellular data set showed the same visual field differences as found in the local infusion dataset.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Our results have demonstrated new findings in three areas. First, we have shown that there appears to be two groups of IP neurons influenced by the CTD task stimuli. Variable latency neurons respond to the targets at shorter latencies after valid than invalid trials and their latencies parallel the behavioral RTs to these same stimuli. The remaining cells show a response latency that was independent of trial type, and these appear to be comparable to those described previously (Robinson et al. 1995). Although novel, these groupings should be considered preliminary because our cell sample is small. In addition, we have demonstrated that the simultaneous presentation of cues to both visual fields causes little or no response in parietal neurons; this is consistent with the absence of attentional orienting to double cues. The lack of a response also suggests that general changes in arousal, if present, do not influence cell activity.

Second, the results confirmed our central hypothesis that cholinergic modulation of attentional function by local application slowed the orienting of attention to peripheral targets. The slowing was seen primarily for invalid cues and secondarily for valid cues, thereby reducing the validity effect. However, there was no slowing of the cognitive operations that mediated either stimulus alerting or attending and reacting to events at a central target.

Third, comparisons of local infusions with systemic injections showed substantial similarities with each other. Overall RTs increased, validity effects decreased, and alerting effects were unchanged with increasing drug dose. One significant difference between routes was the substantially longer overall RTs after local infusion than after systemic injection. These differences could have been a reflection of the immediate or cumulative effects of the infusion procedure. However, analysis of the effects of injection sequence, the permanence of RT changes and visual field effects, suggested that only a small fraction of the RT differences between routes of injection can be accounted for on the basis of the infusion procedure. Other factors, such as fatigue, also may have contributed to some of these differences. We conclude that scopolamine is responsible for the majority of the observed changes in covert orienting.

Response latencies

In our cell sample, response latencies for valid- and invalid-cue conditions were bimodal. For half our cells, valid cue latencies ranged from 80 to 110 ms and much longer latencies (200-280 ms) for invalid trials. The remainder of neurons had similar latencies for the two types of cues. In contrast, previous studies did not find such differences, although some is evident in existing work (see Robinson et al. 1995, Fig. 3). In the Robinson et al. work, the most common cell had equal response latencies but different response magnitudes for valid and invalid trials. The authors hypothesized that the neurons with greater valid cue responses coded attentional location, whereas those with greater invalid-cue responses were hypothesized to signal attentional error. Neither, however, appeared to code for the actual attention shift. In the present sample of cells, those the latencies of which varied with trial type closely resemble the RTs of the animal for those trials. This observation suggests the hypothesis that cellular activity of the variable latency neurons may be linked to the actual attentional movement. In accord with this hypothesis, recent work in our laboratory suggests that the parietal cortex initiates attention shifts (Cutrell 1999).

Cells that failed to respond to double-cue trials

The use of double-cue trials in the present work revealed two interesting findings. First, although double cues carried no spatial information, the abrupt stimulus onset had some alerting value and may have provided explicit timing information as well. That is, the animals learned that once the cue appeared the target would appear within 700 ms. Thus the uncertainty about when the target would appear was reduced. The lack of cellular responses to the double-cue condition suggest that neither of these sources of information were encoded by the parietal neurons sampled. Second, we consider it especially significant that all neurons failed to fire to double cues despite the presence of receptive field stimuli that are identical to those in valid trials. The failure to respond may be due to an inhibitory influence evoked by the cue in the opposite visual hemifield. However, if this was the case, all other trial types should be affected similarly. Another possibility is that response suppression is due to task-related modifications of neural activity. In the presence of two cues, the animal learns to inhibit the orienting reflex, perhaps due to frontal modulation of parietal responses. If this conjecture is true, one ought to be able to observe the learning process by correlating the development of neural responses and behavioral RTs to double cues.

Critical parietal areas for covert orienting

A comparison of Figs. 2 and 7 suggests that scopolamine applied to tissue lateral to and including LIP reduced the animal's ability to covertly orient. An important question is whether scopolamine impairs orienting by causing a nonspecific reduction in firing rates or whether it has more specific effects on cell properties. This would require recording from the same cells after the infusion. This is a technically difficult issue to resolve in alert animals because it is hard to record from the same cell before and after the injection procedure. However, there are many studies that have addressed this issue for the application of acetylcholine to cortical neurons in acute preparations (e.g., Lamour et al. 1988; Metherate et al. 1988), and both nonspecific as well as specific effects on receptive field properties were observed. How these results may be generalized to scopolamine awaits further research.

Previous cellular recording studies (Robinson et al. 1995; Steinmetz et al. 1994) reported that neural activity occurring in conjunction with covert orienting is found in both area 7a and LIP of the intraparietal region. Taken with the present pharmacological results, these studies support the idea that the control of covert attention is intraparietal but not limited to the lateral bank. Carefully controlled, reversible lesions to LIP, VIP, or area 7a would be useful in determining the extent of the critical areas for covert attention.

Comparison with previous work

One major difference between the present study and that of Robinson et al. (1995) is that the latter used only valid- and invalid-cue trials that caused the animal to orient on every trial. The authors concluded that the activity of LIP cells was linked to either attentional orienting or an attentional error signal. However, valid- and invalid-cues also carry temporal information that may affect the responses of parietal neurons. Before it can be concluded that the only relevant cue is spatial, one also must test the alerting effect of cues when orienting does not occur. The present study rules out the latter explanation by demonstrating that response enhancement occurred for valid cues but not for double cues. This result suggests that some intraparietal neurons are active only when a cue causes the animal to orient its attention to the target area. Indeed, because there was no response to invalid cues, the activity was present only when the cue accurately predicts the target location.

Mechanisms underlying the cholinergic control of covert orienting

What is the role of cholinergic neuromodulation of cortical activity in covert orienting? We hypothesize that scopolamine specifically slows the orienting of attention by mitigating the facilitatory effects of cholinergic inputs to intraparietal cortical neurons and reduces the generally facilitatory effects of ACh on parietal and other cortical neurons (McCormick 1993). The specific slowing may be a result of a decreased salience of the cue, a slowing of the actual movement of attention, or impairment of the disengagement of the attentional focus from its current position (Posner et al. 1984). The reduction in general facilitation may be responsible for the overall slowing of RTs within our task. This hypothesis predicts the opposite effects on orienting or attention by cholinergic agonists, and these effects have been confirmed for nicotine in previous work on both humans and monkeys (Witte et al. 1997). It also predicts that enhancement or suppression of the cholinergic basal forebrain nuclei should produce nicotine-like and scopolamine-like alterations in the performance of the covert target detection task.

Functional implications

There is now overwhelming evidence that the posterior parietal cortex plays a major role in the covert orienting of attention. Convergent evidence from human brain imaging (Corbetta 1998) and neurological studies (Posner et al. 1984), single-cell recording (Colby et al. 1996; Robinson et al. 1995; Steinmetz et al. 1994), experimental lesions in primates (Lynch and McLaren 1987), and now pharmacology (present work) is consistent with the idea that this region is necessary for attentional orienting. Moreover, the emergent hypothesis is that cholinergic influences are pivotal to many posterior parietal functions. This may have important implications for attentional dysfunction, at least in the context of the reflexive, cued-target detection task. Work from our laboratory has shown that cholinergic neurotransmission is essential for the orienting component of this task but it is noradrenergic neurotransmission that controls the alerting component (Witte and Marrocco 1997; Witte et al. 1997). Dopamine and serotonin appear to play no role in this task (Ward and Brown 1996; Yamaguchi and Kobayashi 1998). Thus our findings suggest strongly that drug therapy for attention deficits demonstrable with the CTD task may benefit from a combination of agents that affected both the cholinergic and noradrenergic systems.


    ACKNOWLEDGMENTS

The authors thank Drs. Michela Gallagher and Jeff Schall for helpful comments and suggestions.

This research was supported by a grant from the James S. McDonnell Foundation and the Pew Charitable Trusts to the Center for the Cognitive Neuroscience of Attention at the University of Oregon, National Institute of Neurological Disorders and Stroke Grant NS-32973, and Office of Naval Research Grant N00014-96-0273 to M. Posner.

Present address of M. C. Davidson: Behavioral Science, Kennedy Shriver Center, 200 Trapelo Rd., Waltham, MA 02254.


    FOOTNOTES

Address for reprint requests: R. T. Marrocco, Institute of Neuroscience, 1254 University of Oregon, Eugene, OR 97403-1254.

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 The distributions of RTs was skewed as is common in RT experiments. However, violations of the assumptions of normality and equality of variance have little effect on the validity of inferences drawn from the ANOVA provided that the number of observations is very large (Lindeman 1963).

Received 17 June 1999; accepted in final form 2 December 1999.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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