Abstract

Rats with a deficit in selective attention accompanied by impulsivity can be identified using a five-choice serial reaction time task (5-CSRT) and have been proposed to represent a rodent model of attention-deficit hyperactivity disorder (ADHD). The aim of the present study was to investigate which brain areas are important for visuospatial attention and to test the specific hypothesis that dysfunction of the frontal cortex is related to the behavioral deficits observed in poorly performing rats. Therefore, [14C]deoxyglucose (DG) uptake, an index of brain metabolic activity, was measured during the performance of a 5-CSRT task in two populations of rats (poorly and well-performing rats) to study the relationships between the regional brain activity and behavioral output. While performing a 5-CSRT task, poorly performing rats exhibited lower DG uptake in the cingulate and ventrolateral orbital cortices than did well-performing rats,. Moreover, there was a positive correlation between choice accuracy and DG uptake in several areas, especially in the frontal and parietal regions, whereas there was an inverse correlation between the percentage of premature responses and DG uptake in the ventrolateral orbital and cingulate cortices.These results, which demonstrated that the poorly performing rats exhibited metabolic dysfunction in the cingulate and prefrontal cortices, provide a basis for the face validity of the rodent model of ADHD. Moreover, they suggest that the neural network of attention in rats is remarkably analogous to that described in primates.

Introduction

Attention-deficit hyperactivity disorder (ADHD) is a common behavioral disorder among school-aged children (American Psychiatric Association, 1994). Patients with ADHD are restless, easily distracted by extraneous stimuli and have difficulty in sustaining attention in tasks. They also have difficulty in waiting and delaying gratification (American Psychiatric Association, 1994), and are consistently impaired on tests of frontal lobe function, such as the Wisconsin Card Sort Test (Barkley et al., 1992) or the Continuous Performance Task, which assess sustained attention (Losier et al., 1996; Barkley, 1997).

The etiology of the syndrome is not well understood, but there is converging evidence to indicate that ADHD is a genetically programmed disorder of brain development, involving the frontal-striatal-pallidal-thalamo-cortical loops subserving the regulation of cognitive processes, attention and motor output behaviors (Voeller, 1994; Castellanos et al., 1996). Structural neuroimaging studies have revealed volumetric abnormalities of the frontal and parietal lobes and the basal ganglia (Aylward et al., 1996; Filipek et al., 1997). Positron emission tomography studies on ADHD patients (Zametkin et al.., 1990; Amen and Carmichael, 1997) have demonstrated reductions of cerebral glucose metabolism in the frontal cortex during the performance of an auditory attention task.

A number of animal models for ADHD have been proposed (Archer et al.., 1988; Elsner, 1991; Sagvolden et al.., 1992; Diaz-Granados et al.., 1994; Kostrzewa et al.., 1994). In most of those models, however, hyperactivity is not naturally occurring, but is induced by pharmacological manipulations or brain lesions, which are different from disturbances occurring during normal developmental processes that ultimately lead to ADHD. An additional factor confounding the validity of these models is that the attentional performance of these animals is rarely studied. An animal model of ADHD without exogenous manipulation was proposed by Puumala et al. (Puumala et al., 1996; 1997). Using the five-choice serial reaction time (5-CSRT) task, initially described to assess attention (Carli et al., 1983), animals with a deficit in their attentional processes accompanied by simultaneous hyperactivity were selected from normally behaving animals: poorly performing rats make more incorrect choices and premature anticipatory responses, reflecting poor sustained attention and impulsivity, respectively (Puumala et al., 1996; 1997). Moreover, Puumala et al. (Puumala et al., 1996) demonstrated that the visual sensory deficit might not be the cause of the poor choice accuracy in the 5-CSRT task, and that methylphenidate could slightly improve the attention of these poorly performing rats.

The aim of the present study was to investigate which brain areas are important for visuospatial attention and to test the specific hypothesis that dysfunction of the frontal cortex, a region vitally important to the efficient functioning of the fronto-striatal attentional networks and presenting monoamine abnormalities in the present rodent model of ADHD (Puumala and Sirviö, 1998), is related to the behavioral deficits observed in the poorly performing rats. Therefore, [14C]deoxyglucose (DG) uptake, an index of neuronal activity, was measured during the performance of a 5-CSRT task in two populations of rats (poorly and well-performing rats) to study the relationships between the regional brain activity and behavioral output in order to establish the face validity of the present animal model of ADHD.

Materials and Methods

Animals

Thirty-five male Han:Wistar rats (National Animal Center, Kuopio, Finland) were used in the experiment. The rats were 2 months old at the beginning of behavioral training and were housed singly in stainless steel shoe-box cages with elevated covers. The cages were placed in a environment controlled for temperature (20 ± 1°C), humidity (55 ± 10%) and light period (lights on 07:00–19:00 h). During training and testing, the rats were food deprived for 16–17 h before each daily session. After daily behavioral training, the rats received 15–18 g of food pellets (SDS, Special Diets Service, Stockholm, Sweden) so that they were maintained at ~80–85% of their free-feeding weight. Water was available ad libitum except in the test apparatus.

Five-choice Serial Reaction Time task: Behavioral Training and Testing

In the 5-CSRT task, rats are required to spatially discriminate a short visual stimulus occurring randomly in one of five locations. During the testing period, the rat is required to sustain its attention to discriminate the brief stimulus while maintaining a sufficient activity level so that it can respond appropriately (Carli et al., 1983).

Apparatus

The apparatus (Carli et al., 1983), which was made in the Technical Center (University of Kuopio, Finland), consisted of a 25 × 25 cm aluminum chamber with a curved rear wall. Set in the curved wall were nine 2.5 cm2 holes, 4 cm deep and 2.5 cm above the floor. Each hole had an infrared photocell beam crossing the entrance vertically and illuminating a photoelectric cell. A standard 2 W bulb at the rear of the hole provided illumination of that hole. The entrance to holes 2, 4, 6 and 8 were blocked with a metal cap. Food pellets (45 mg, dustless, Bioserv Inc., Holton Instrument Co., Frenchtown, NJ) were delivered automatically into a magazine at the front of the chamber. Access was gained to the magazine through a perspex door (= panel). The distance from the panel to all of the illuminated holes at the rear of the box was 25 cm. The chamber was illuminated with a house-lamp (2 W) mounted in the roof. The animals were placed in the chamber through a perspex door in the upper half of the front wall. The apparatus was housed in a dark, soundproof compartment. Online control of the apparatus and data collection were performed using microprocessors, which were programmed using Spider (Paul Fray Ltd, Cambridge, UK).

Training

Rats were trained in the following manner to spatially discriminate a brief visual stimulus, presented randomly by the computer in one of the five holes (from the left, holes 1, 3, 5, 7 and 9). On the first 2 days of behavioral training, rats were magazine-trained by being placed in the chamber for 15 min with the house-light off and the magazine containing 20–30 food pellets. The next day, rats were placed in the chamber for 15 min and a food pellet was delivered every 10 s into the magazine. The house-light was on during this phase. In the third phase, one of the holes was illuminated all the time during the 15 min training period and every time a rat made a response (nose-poke) towards the illuminated hole, it was rewarded by a food pellet into the magazine. It took ~3–7 training days for each rat to learn this phase.

After learning this procedure, rats entered the next phase, which began by the delivery of a single food pellet. The first trial was started when the panel was opened to collect the food. After a fixed delay (intertrial interval, ITI), the light at the rear of one of the holes was illuminated for a short period (stimulus duration). The light stimulus was presented in each of the holes an equal number of times during each complete session, and the order of presentations was randomized by the computer. Responses (nose-pokes) by the rat into the illuminated hole or responses in that particular hole for a short period of time following illumination (limited hole period) were rewarded with the delivery of a food pellet and a correct response was recorded. The next trial was initiated when the rat opened the panel to collect the food pellet. A response in any other hole (incorrect response) or a failure to respond at all during the limited hold period (omission) resulted in a punishment period of darkness (time-out). Therefore, if a rat were facing in the wrong direction when the visual stimulus was presented in the hole, it would not be able to detect it and that trial would result in an omission and period of time-out. Any response made during the time-out period restarted the time-out. Responses made in the holes during the ITI period were recorded as premature responses, and these responses resulted in a period of time-out. Responses made towards the magazine during the ITI were also recorded, but did not result in a period of time-out. After a time-out period, the next trial was initiated when the rat opened the panel (the magazine is empty). The latency between the onset of the stimulus and the response (whether correct or incorrect) was measured, as well as the latency to collect the earned food pellet after a correct response. Each daily training session (five sessions per week) lasted 15 min at the beginning. During the progression of training, the duration of a testing session was increased to 30 min and finally to 40 min. During the first training session, the stimulus duration and the limited hold period were set at 4.0 and 0.5 s, respectively. The stimulus duration and the limited hold period were then progressively changed to 0.25 and 3.75 s, respectively, during the training. The ITI and time-out were set at 5.0 and 4.0 s, respectively. Each rat was trained on this schedule depending on its own performance until a stable level of performance had been reached. The criteria for advancing in the training program were that the proportion of correct responses was >50%, the proportion of omissions was <15% and the rat completed >40 trials per 30 min training period. It took ~60–70 training sessions to reach a stable level when no improvement in performance could be observed.

The following behavioral parameters were analyzed in each session: the total number of trials started (correct + incorrect responses + omissions + premature hole responses), the total number of trials completed (correct + incorrect responses), the percentage omission (omissions/trials started that were not terminated by a premature response; %OMI), the percentage of correct responses (correct responses/trials completed; %Correct), the percentage of ITI hole responses (ITI hole responses/trials started; %ITI), the response latencies for correct responses (CLATE), and the incorrect responses as well as food collection (MAGALAT).

Experimental Groups

After completion of the training, rats were assigned into two groups depending on their level of percent correct responses and percent premature hole responses. Five rats had the criteria of poorly performing rats [percent correct less than the mean of the 35 rats (57%) and percent premature hole response greater than the mean of the 35 rats (18%)]. Five rats were randomly selected among the population defined by percent correct greater than the mean of the 35 rats and percent premature hole response less than the mean of the 35 rats.

For the DG experiments, the well- and poorly performing rats (n = 5 in each group) were tested with a stimulus duration of 0.25 s and a limited hold period of 3.75 s.

Cerebral Uptake of [14C]DG

Immediately after receiving an i.p. injection of [14C]DG (McIntosh and Gonzalez-Lima, 1993) (150 mCi/kg in 0.8 ml saline; NEN), animals were placed in the chamber and stimulus presentation began (stimulus duration 0.25 s). Upon completion of the [14C]DG uptake period (40 min), the animals were removed from the chamber and quickly decapitated. The brains were removed rapidly, frozen in isopentane (–45°C) and stored at –80°C. Serial coronal brain slices (20 μm) were obtained with a cryomicrotome (–20°C) in the following sequence: one section for histology, three for autoradiography and 10 discarded. Sections for autoradiography analysis were mounted on coverslips and rapidly dried on a hot plate (60°C). Sections and calibrated 14C standards (Amersham) were exposed to X-ray films (Biomax MR, Kodak). Sections for histology were airdried and cresyl violet-stained for identification of brain structures on the autoradiograms.

Densitometric Analysis of Autoradiograms

Autoradiograms were analyzed with semi-quantitative densitometry with a computerized image analysis system (MCID, Imaging Research Inc.). Thirty-five brain regions, anatomically defined according to Paxinos and Watson (Paxinos and Watson, 1986) on Nissl-stained sections, were selected for analysis. For each region of interest, the means of optical density were measured bilaterally over two consecutive sections and averaged independently for left and right sides. The value of each structure was then expressed as a ratio of the optical density of the particular structure and the mean optical density obtained in the corpus callosum within the same brain used as a reference. The relative metabolic activity method, i.e. the method used in this study, is not equivalent to the measurement of local cerebral glucose use as defined by Sokoloff et al.  (Sokoloff et al., 1977) but provides useful information on functional metabolic changes in protocols involving freely moving animals engaged in a cognitive task, which are not compatible with all the requirements of the local cerebral glucose use measurement (assessment of arterial plasma concentrations of [14C]DG and glucose) (Destrade et al., 1992). The ratios, which are a linear function of local cerebral glucose use (Sharp et al., 1983), allow comparisons between animals in similar physiologic conditions and also tend to attenuate experimental errors due to differences in the amount of injected isotope, section thickness, film exposure and development time.

Statistics

Comparisons between well- and poorly performing rats were assessed through the use of a repeated measures ANOVA, with ‘group’ as the between-subject factor and ‘structure’ as the within-subject factor. In the case of statistical interactions, this was followed by a one-way ANOVA.

In order to test if differences between the well- and poorly performing rats were especially related to the attentional deficit or impulsivity, the relationships between behavioral parameters recorded during the attentional task and regional DG ratios were analyzed by calculating the correlation coefficient (Pearson's) and testing the significance (two- tailed) of the null hypothesis of no association. The criterion for statistical significance was a P value of ≤0.05.

Results

Behavioral Performances

The behavioral profile of the rats performing a 5-CSRT task during the session of DG uptake measurement is presented in Table 1. By definition, the poorly performing rats made significantly fewer correct responses and more premature responses than the well-performing ones. The number of trials completed, percentage of omissions, latency to correct response and latency to collect food after a correct response, however, were not different between these two groups (Table 1).

[14C]DG Uptake

The analysis of variance (ANOVA) performed on the overall results obtained in the well- and poorly performing rats revealed significant differences between the brain regions (P < 0.001), but no differences between the groups. Since the structure × group interaction was significant (P < 0.001), one-way ANOVAs were performed to test the differences between groups in each brain area separately. The analysis indicated that DG uptake of the poorly performing rats, when compared with their well- performing counterparts, was significantly lower in both the right and left cingulate cortices, as well as in the left ventrolateral orbital cortex (Table 2, Fig. 1).

The Relationship between Regional [14C]DG Uptake and Behavioral Performance

Pearson correlation analyses between DG uptake ratios and the performance accuracy (percentage of correct response) indicated a significant positive correlation in several areas, especially in the prefrontal regions (ventrolateral orbital, medial prefrontal, lateral prefrontal, cingulate cortices) (Table 3, Figs 2–4). There was also a significant correlation in the frontal, parietal, temporal, occipital and entorhinal cortices, as well as in some subcortical areas (hippocampus, substantia innominata, substantia nigra; Tables 3 and 4). In some of these areas, the correlation was significant only in the left hemisphere (e.g. ventrolateral orbital, frontal) or in the right hemisphere (e.g. entorhinal and lateral prefrontal cortices, and substantia innominata and substantia nigra). There were correlations approaching significance between DG uptake and percentage of correct response in various cortical regions (Table 3), but there was no association between those parameters in the other subcortical structures, such as the basal ganglia and hypothalamus (Table 4).

There were significant inverse correlations between the percentage of premature responses and DG uptake in three cortical areas: the right and left cingulate cortices, and the left ventrolateral orbital cortex (Table 3, Figs 3 and 4).

There was a significant inverse correlation between the latency to correct response and DG uptake in the right nucleus accumbens (both in the core and shell; Table 3).

There was no correlation between DG uptake and the number of trials completed except in the hindlimb and forelimb areas (Tables 3 and 4).

There was no correlation between DG uptake and the latency to incorrect response, or latency to collect food in any of the areas studied (Tables 3 and 4).

Discussion

Methodical Considerations

The DG uptake method was used to map the overall activity of the brain during the 40 min of the testing session. Contrary to some human studies using PET or fMRI, it is not possible to see the activity online (Carter et al., 1998), and thus to assign the modification of the activity to one particular event. Correlation analyses between regional DG uptake and behavioral parameters were thus performed to assess the relationships between performances and regional brain activity. Since correlation analyses between several regional DG uptake and behavioral parameters were performed, it is possible that some of the calculated correlations are statistically significant by chance. All of the significant correlations are, however, in the same direction, which is not consistent with the assumption of a random cause for the correlations. Moreover, anatomical, functional and clinical data (see below) support the claim that many of these significant correlations are meaningful.

The neuronal activity between the well- and poorly performing rats was compared using a one-way analysis of variance. Since this analysis was performed among the high number (34) of regions of interest, no conservative correction for multiple comparisons (e.g. Bonferroni) was done because this would be prone to type II error. Even though the possibility of type I error should not be ignored, the results on premature responding obtained in the present study are remarkably consistent with other sources of evidence (see below), which renders the possibility of a type I error to be much less likely in this case. Therefore, it is relevant to discuss the physiological significance of those correlations/changes.

Distributed Network for Sustained Visuospatial Attention in Rats

The positive correlations between the percentage of correct responses and DG ratios in many cortical areas and also in some subcortical areas (see Figure 5) suggest that these regions are engaged during the 5-CSRT task.

The present findings add additional support to the hypothesis that the medial prefrontal cortex is involved in attentional processes in the rat. Indeed, Muir et al. (Muir et al., 1996) described lesions of the medial prefrontal cortex reducing choice accuracy, and Sarter et al. (Sarter et al., 1996) reported that neurons in the medial prefrontal cortex are activated during the performance of a 2-CSRT task.

The present neuroimaging study suggests that the parietal cortex and also the occipital cortex, a region considered in the rat to be analogous to the primate posterior parietal cortex (Reep et al., 1994), are engaged during attentional processes. Lesions of the posterior parietal cortex impair visuospatial abilities of rats, as well as primates, implying that the visuospatial route via the posterior parietal cortex may not be unique to primates (Kolb, 1990; King and Corwin, 1993). However, some studies have indicated a lack of involvement of the posterior parietal cortex in visuospatial orienting in rodents (Rosner and Mittleman, 1996). Furthermore, Muir et al. (Muir et al., 1996) demonstrated that lesions of the parietal cortex [part of the parietal cortex more anterior to the rat area analogous to the primate posterior parietal cortex (Reep et al., 1994)] did not disrupt the choice accuracy of rats in the 5-CSRT task. One interpretation of the discrepancies between the present study and that of (Muir et al., 1996) is that this part of the parietal cortex is engaged during the task but is not essential for task accomplishment.

The present study also indicated that the substantia innominata (the rodent equivalent of the nucleus basalis of Meynert) is engaged during the 5-CSRT task. The implication of the substantia innominata in the modulation of visual attention has been described in rodents (Muir et al., 1992; 1994), but also in humans (Lawrence and Sahakian, 1995).

The role of the hippocampus in attentional processes in rats is not clear. Rats with hippocampal lesions were impaired in the acquisition of the 5-CSRT task (Bratt et al., 1995), suggesting the presence of attentional deficits in hippocampal-lesion rats, but bilateral knife cut lesions to the perforant path did not impair the performance of 5-CSRT task acquired prior to lesioning (Kirkby and Higgins, 1998). Olton et al. (Olton et al., 1988) demonstrated that rats with fimbria fornix or medial septum lesions were impaired in their estimation of the duration of a stimulus. The involvement of the hippocampus in such timing processes may explain why this structure is engaged during the performance of a 5-CSRT task, because the visual stimulus is delivered after a fixed intertrial interval.

Taken together, these results indicate that there is a widely distributed network for sustained visuospatial attention in rats which is remarkably analogous to that described for human and non-human primates (Posner et al., 1984; Mesulam, 1990; Posner and Petersen, 1990; Pardo et al., 1991; Coull et al., 1996; Coull and Frith, 1998).

Decreased Neuronal Activity in the Cingulate and Ventrolateral Orbital Cortex and the Inattention and Impulsivity of the Poorly Performing Rats

Functional neuroimaging findings in normal volunteers (Pardo et  al., 1990; Badgaiyan and Posner, 1998; Bush et al., 1998) have led to the suggestion that the cingulate cortex is important for response selection, such as the inhibition of prepotent response tendencies. Furthermore, Muir et al. (Muir et al., 1996) demonstrated that lesions of the anterior cingulate cortex do not affect choice accuracy in the 5-CSRT, but do increase premature responses of rats. The present results demonstrating that metabolic activity in the cingulate cortex is inversely correlated with the percentage of premature responses strongly support the hypothesis that the cingulate cortex is an important brain area in the network underlying the inhibition of irrelevant responses. Interestingly enough, ADHD subjects displayed decreased function in the anterior cingulate cortex, suggesting that a dysfunction in the cingulate cortex might lead to inattention/ impulsivity and therefore contribute to the pathophysiology of ADHD (Rubia et al., 1999).

The rat ventrolateral orbital cortex, which also showed a lower DG activity in the poor performers, belongs to a cortical network connecting the frontal, posterior parietal and ventrolateral orbital cortices with each other (Reep et al., 1996). Lesions of this region produced severe multimodal neglect (King et al., 1989) and allocentric spatial deficits in rats (Corwin et al., 1994). In addition, imaging studies in humans (Paus et al., 1993; Jonides et al., 1998) and a study of human patients who suffer from reduced inhibitory control as well as electrophysiological recordings in animals indicated that the lateral prefrontal cortex is critically involved in inhibitory functions (Watanabe, 1986; Chao and Knight, 1995). Reduced glucose utilization was reported in the orbital frontal cortex of ADHD children during an auditory continuous performance task (Ernst et al., 1994). Moreover, Barkley's (Barkley, 1997) inhibitory dysfunction model ascribes the inhibitory functions of ADHD patients to the orbitofrontal regions of the prefrontal cortex.

Conclusions

The present study, which maps for the first time brain activity in the rat during an attentional task, suggests that the neural networks of attention of rats are quite similar to those described for humans (Mesulam, 1990; Posner and Petersen, 1990). In addition, these results demonstrate that poorly performing rats exhibited decreased metabolic activity in the cingulate and ventrolateral orbital cortices, changes which might be related to their impulsivity (failure to inhibit response) and impaired attention, as described in ADHD patients. Therefore, this rodent model of ADHD possesses considerable face validity and might be used in the lead optimization phase during the preclinical development of more effective and safe treatments for ADHD, since the present therapy is by no means optimal. In the future, the assessment of DG uptake during drug treatment might provide important insight into the site of action of putative drug candidates.

Table 1

Performance of the rats in the five-choice serial reaction time task

 %Correct %ITI Trials %OMI CLATE MAGALAT 
Results are expressed as means ± SEM. %Correct, percentage of correct responses; %ITI, percentage of premature responses; Trials, number of trials completed; %OMI, percentage of omissions; CLATE, latency to correct response; MAGALAT, latency to collect food pellet after correct response. 
*Significantly different from the well-performing rats (P ≤ 0.001). 
Well-performing rats 68 ± 2  8 ± 3 97 ± 15 25 ± 5 0.91 ± 0.04 2.3 ± 0.7 
Poorly performing rats 48 ± 2* 28 ± 2* 70 ± 15 19 ± 6 0.86 ± 0.06 3.3 ± 1.4 
 %Correct %ITI Trials %OMI CLATE MAGALAT 
Results are expressed as means ± SEM. %Correct, percentage of correct responses; %ITI, percentage of premature responses; Trials, number of trials completed; %OMI, percentage of omissions; CLATE, latency to correct response; MAGALAT, latency to collect food pellet after correct response. 
*Significantly different from the well-performing rats (P ≤ 0.001). 
Well-performing rats 68 ± 2  8 ± 3 97 ± 15 25 ± 5 0.91 ± 0.04 2.3 ± 0.7 
Poorly performing rats 48 ± 2* 28 ± 2* 70 ± 15 19 ± 6 0.86 ± 0.06 3.3 ± 1.4 
Table 2

Deoxyglucose ratios in the neocortex and basal ganglia in well- and poorly performing rats

 IA (mm)  Well-performing rats (n = 5) Poorly performing rats (n = 5) P
IA, interaural (Paxinos and Watson, 1986). 
*Significantly different from the poorly performing rats (P < 0.05). 
Frontal cortex (area 2) +13.2 3.46 ± 0.24 2.85 ± 0.24 0.08 
  3.56 ± 0.26 2.88 ± 0.24 0.07 
Agranular insular cortex +13.2 3.24 ± 0.25 2.71 ± 0.24 0.13 
  3.52 ± 0.33 2.75 ± 0.26 0.08 
Lateral orbital cortex +13.2 3.49 ± 0.33 3.00 ± 0.20 0.19 
  3.68 ± 0.37 3.00 ± 0.20 0.12 
Ventrolateral orbital cortex +13.2 4.06 ± 0.24 3.33 ± 0.31 0.07 
  4.26 ± 0.25 3.26 ± 0.30* 0.02 
Parietal cortex (area 1) +11.2 3.76 ± 0.35 3.06 ± 0.22 0.10 
  3.95 ± 0.44 3.12 ± 0.27 0.11 
Medial prefrontal cortex +11.2 3.40 ± 0.21 2.74 ± 0.31 0.09 
  3.49 ± 0.26 2.71 ± 0.30 0.06 
Frontal cortex (areas 1–3) +11.2 3.81 ± 0.44 3.09 ± 0.25 0.15 
  3.70 ± 0.34 3.03 ± 0.28 0.13 
Lateral prefrontal cortex +11.2 3.81 ± 0.30 3.11 ± 0.29 0.09 
  4.08 ± 0.47 3.30 ± 0.24 0.14 
Insular cortex +10.2 3.45 ± 0.12 3.12 ± 0.16 0.11 
  3.58 ± 0.17 3.21 ± 0.18 0.14 
Cingulate cortex (Cg1; Cg2) (level of corpus callosum) +10.2 2.83 ± 0.06 2.58 ± 0.15 0.13 
  2.87 ± 0.06 2.57 ± 0.12* 0.03 
Hindlimb and forelimb areas +7.7 3.26 ± 0.19 2.82 ± 0.25 0.16 
  3.27 ± 0.20 2.81 ± 0.28 0.18 
Cingulate cortex (Cg1; Cg2) (level of globus pallidus) +7.7 4.25 ± 0.15 3.38 ± 0.30 *0.02 
  4.27 ± 0.13 3.39 ± 0.30* 0.02 
Parietal cortex (areas 1; 2) +7.7 4.02 ± 0.24 3.34 ± 0.30 0.09 
  4.06 ± 0.31 3.46 ± 0.36 0.21 
Nucleus accumbens (core) +10.2 2.27 ± 0.05 2.24 ± 0.13 0.86 
  2.38 ± 0.09 2.34 ± 0.11 0.80 
Nucleus accumbens (shell) +10.2 2.27 ± 0.13 2.38 ± 0.13 0.55 
  2.35 ± 0.11 2.48 ± 0.11 0.38 
Caudate-putamen +10.2 2.93 ± 0.08 2.81 ± 0.18 0.51 
  3.02 ± 0.13 2.87 ± 0.18 0.48 
Ventral pallidum +8.7 2.00 ± 0.15 1.79 ± 0.14 0.31 
  2.05 ± 0.17 1.81 ± 0.13 0.24 
Globus pallidus +7.7 1.94 ± 0.10 1.73 ± 0.16 0.29 
  2.04 ± 0.17 1.87 ± 0.20 0.49 
 IA (mm)  Well-performing rats (n = 5) Poorly performing rats (n = 5) P
IA, interaural (Paxinos and Watson, 1986). 
*Significantly different from the poorly performing rats (P < 0.05). 
Frontal cortex (area 2) +13.2 3.46 ± 0.24 2.85 ± 0.24 0.08 
  3.56 ± 0.26 2.88 ± 0.24 0.07 
Agranular insular cortex +13.2 3.24 ± 0.25 2.71 ± 0.24 0.13 
  3.52 ± 0.33 2.75 ± 0.26 0.08 
Lateral orbital cortex +13.2 3.49 ± 0.33 3.00 ± 0.20 0.19 
  3.68 ± 0.37 3.00 ± 0.20 0.12 
Ventrolateral orbital cortex +13.2 4.06 ± 0.24 3.33 ± 0.31 0.07 
  4.26 ± 0.25 3.26 ± 0.30* 0.02 
Parietal cortex (area 1) +11.2 3.76 ± 0.35 3.06 ± 0.22 0.10 
  3.95 ± 0.44 3.12 ± 0.27 0.11 
Medial prefrontal cortex +11.2 3.40 ± 0.21 2.74 ± 0.31 0.09 
  3.49 ± 0.26 2.71 ± 0.30 0.06 
Frontal cortex (areas 1–3) +11.2 3.81 ± 0.44 3.09 ± 0.25 0.15 
  3.70 ± 0.34 3.03 ± 0.28 0.13 
Lateral prefrontal cortex +11.2 3.81 ± 0.30 3.11 ± 0.29 0.09 
  4.08 ± 0.47 3.30 ± 0.24 0.14 
Insular cortex +10.2 3.45 ± 0.12 3.12 ± 0.16 0.11 
  3.58 ± 0.17 3.21 ± 0.18 0.14 
Cingulate cortex (Cg1; Cg2) (level of corpus callosum) +10.2 2.83 ± 0.06 2.58 ± 0.15 0.13 
  2.87 ± 0.06 2.57 ± 0.12* 0.03 
Hindlimb and forelimb areas +7.7 3.26 ± 0.19 2.82 ± 0.25 0.16 
  3.27 ± 0.20 2.81 ± 0.28 0.18 
Cingulate cortex (Cg1; Cg2) (level of globus pallidus) +7.7 4.25 ± 0.15 3.38 ± 0.30 *0.02 
  4.27 ± 0.13 3.39 ± 0.30* 0.02 
Parietal cortex (areas 1; 2) +7.7 4.02 ± 0.24 3.34 ± 0.30 0.09 
  4.06 ± 0.31 3.46 ± 0.36 0.21 
Nucleus accumbens (core) +10.2 2.27 ± 0.05 2.24 ± 0.13 0.86 
  2.38 ± 0.09 2.34 ± 0.11 0.80 
Nucleus accumbens (shell) +10.2 2.27 ± 0.13 2.38 ± 0.13 0.55 
  2.35 ± 0.11 2.48 ± 0.11 0.38 
Caudate-putamen +10.2 2.93 ± 0.08 2.81 ± 0.18 0.51 
  3.02 ± 0.13 2.87 ± 0.18 0.48 
Ventral pallidum +8.7 2.00 ± 0.15 1.79 ± 0.14 0.31 
  2.05 ± 0.17 1.81 ± 0.13 0.24 
Globus pallidus +7.7 1.94 ± 0.10 1.73 ± 0.16 0.29 
  2.04 ± 0.17 1.87 ± 0.20 0.49 
Table 3

Correlation coefficients and their significancea for parameters of the five-choice serial reaction time task and [14C]deoxyglucose uptake in cortical areas

  %Correct %ITI Trials CLATE 
%Correct, percentage of correct responses; %ITI, percentage of premature responses; Trials, number of trials completed; CLATE, latency to correct response; R and L, right and left. 
aValues given are r (P). 
Anterior frontal cortex (area 2) 0.58 (0.08) –0.58 (0.08) 0.34 (0.34) –0.07 (0.84) 
 0.62 (0.06) –0.61 (0.06) 0.30 (0.39) –0.08 (0.83) 
Agranular insular cortex 0.51 (0.13) –0.54 (0.10) 0.25 (0.48) –0.06 (0.88) 
 0.60 (0.06) –0.61 (0.06) 0.25 (0.48) –0.02 (0.96) 
Lateral orbital cortex 0.42 (0.22) –0.49 (0.15) 0.22 (0.54)  0.16 (0.67) 
 0.52 (0.12) –0.57 (0.08) 0.20 (0.58)  0.16 (0.73) 
Ventrolateral orbital cortex 0.54 (0.10) –0.56 (0.09) 0.31 (0.39) –0.08 (0.83) 
 0.67 (0.03) –0.69 (0.03) 0.35 (0.33) –0.07 (0.85) 
Parietal cortex (area 1) 0.68 (0.03) –0.53 (0.12) 0.44 (0.20) –0.16 (0.66) 
 0.68 (0.03) –0.49 (0.15) 0.46 (0.18) –0.16 (0.65) 
Medial prefrontal cortex 0.66 (0.04) –0.54 (0.11) 0.40 (0.25) –0.28 (0.43) 
 0.71 (0.02) –0.58 (0.08) 0.40 (0.25) –0.21 (0.57) 
Frontal cortex (areas 1–3) 0.62 (0.06) –0.50 (0.14) 0.44 (0.20) –0.11 (0.77) 
 0.65 (0.04) –0.52 (0.12) 0.46 (0.18) –0.21 (0.55) 
Lateral prefrontal cortex 0.63 (0.05) –0.58 (0.08) 0.25 (0.49) –0.13 (0.71) 
 0.61 (0.06) –0.50 (0.14) 0.35 (0.32) –0.05 (0.89) 
Insular cortex 0.51 (0.13) –0.35 (0.32) 0.44 (0.21) –0.14 (0.69) 
 0.57 (0.08) –0.44 (0.20) 0.29 (0.42) –0.15 (0.67) 
Cingulate cortex (Cg1, Cg2) (level of corpus callosum) 0.36 (0.31) –0.47 (0.18) 0.15 (0.68) –0.09 (0.79) 
 0.51 (0.14) –0.59 (0.07) 0.26 (0.47)  0.11 (0.76) 
Hindlimb and forelimb areas 0.60 (0.07) –0.47 (0.17) 0.54 (0.11) –0.43 (0.22) 
 0.56 (0.09) –0.40 (0.25) 0.64 (0.05) –0.40 (0.26) 
Cingulate cortex (Cg1, Cg2) (level of globus pallidus) 0.67 (0.03) –0.67 (0.03) 0.48 (0.16) –0.25 (0.49) 
 0.72 (0.02) –0.74 (0.01) 0.39 (0.27) –0.26 (0.47) 
Parietal cortex (areas 1,2) 0.62 (0.06) –0.51 (0.13) 0.53 (0.11) –0.30 (0.39) 
 0.56 (0.09) –0.40 (0.26) 0.58 (0.08) –0.38 (0.27) 
Temporal cortex 0.68 (0.03) –0.54 (0.11) 0.46 (0.18) –0.20 (0.57) 
 0.71 (0.02) –0.60 (0.07) 0.44 (0.20) –0.13 (0.73) 
Perirhinal cortex 0.50 (0.14) –0.36 (0.31) 0.35 (0.32) –0.30 (0.39) 
 0.59 (0.07) –0.49 (0.15) 0.35 (0.32) –0.15 (0.69) 
Occipital cortex 0.64 (0.04) –0.51 (0.13) 0.44 (0.20) –0.18 (0.61) 
 0.69 (0.03) –0.52 (0.12) 0.51 (0.13) –0.14 (0.70) 
Entorhinal cortex 0.69 (0.03) –0.63 (0.06) 0.26 (0.46) –0.05 (0.89) 
 0.61 (0.06) –0.51 (0.13) 0.33 (0.35) –0.19 (0.60) 
  %Correct %ITI Trials CLATE 
%Correct, percentage of correct responses; %ITI, percentage of premature responses; Trials, number of trials completed; CLATE, latency to correct response; R and L, right and left. 
aValues given are r (P). 
Anterior frontal cortex (area 2) 0.58 (0.08) –0.58 (0.08) 0.34 (0.34) –0.07 (0.84) 
 0.62 (0.06) –0.61 (0.06) 0.30 (0.39) –0.08 (0.83) 
Agranular insular cortex 0.51 (0.13) –0.54 (0.10) 0.25 (0.48) –0.06 (0.88) 
 0.60 (0.06) –0.61 (0.06) 0.25 (0.48) –0.02 (0.96) 
Lateral orbital cortex 0.42 (0.22) –0.49 (0.15) 0.22 (0.54)  0.16 (0.67) 
 0.52 (0.12) –0.57 (0.08) 0.20 (0.58)  0.16 (0.73) 
Ventrolateral orbital cortex 0.54 (0.10) –0.56 (0.09) 0.31 (0.39) –0.08 (0.83) 
 0.67 (0.03) –0.69 (0.03) 0.35 (0.33) –0.07 (0.85) 
Parietal cortex (area 1) 0.68 (0.03) –0.53 (0.12) 0.44 (0.20) –0.16 (0.66) 
 0.68 (0.03) –0.49 (0.15) 0.46 (0.18) –0.16 (0.65) 
Medial prefrontal cortex 0.66 (0.04) –0.54 (0.11) 0.40 (0.25) –0.28 (0.43) 
 0.71 (0.02) –0.58 (0.08) 0.40 (0.25) –0.21 (0.57) 
Frontal cortex (areas 1–3) 0.62 (0.06) –0.50 (0.14) 0.44 (0.20) –0.11 (0.77) 
 0.65 (0.04) –0.52 (0.12) 0.46 (0.18) –0.21 (0.55) 
Lateral prefrontal cortex 0.63 (0.05) –0.58 (0.08) 0.25 (0.49) –0.13 (0.71) 
 0.61 (0.06) –0.50 (0.14) 0.35 (0.32) –0.05 (0.89) 
Insular cortex 0.51 (0.13) –0.35 (0.32) 0.44 (0.21) –0.14 (0.69) 
 0.57 (0.08) –0.44 (0.20) 0.29 (0.42) –0.15 (0.67) 
Cingulate cortex (Cg1, Cg2) (level of corpus callosum) 0.36 (0.31) –0.47 (0.18) 0.15 (0.68) –0.09 (0.79) 
 0.51 (0.14) –0.59 (0.07) 0.26 (0.47)  0.11 (0.76) 
Hindlimb and forelimb areas 0.60 (0.07) –0.47 (0.17) 0.54 (0.11) –0.43 (0.22) 
 0.56 (0.09) –0.40 (0.25) 0.64 (0.05) –0.40 (0.26) 
Cingulate cortex (Cg1, Cg2) (level of globus pallidus) 0.67 (0.03) –0.67 (0.03) 0.48 (0.16) –0.25 (0.49) 
 0.72 (0.02) –0.74 (0.01) 0.39 (0.27) –0.26 (0.47) 
Parietal cortex (areas 1,2) 0.62 (0.06) –0.51 (0.13) 0.53 (0.11) –0.30 (0.39) 
 0.56 (0.09) –0.40 (0.26) 0.58 (0.08) –0.38 (0.27) 
Temporal cortex 0.68 (0.03) –0.54 (0.11) 0.46 (0.18) –0.20 (0.57) 
 0.71 (0.02) –0.60 (0.07) 0.44 (0.20) –0.13 (0.73) 
Perirhinal cortex 0.50 (0.14) –0.36 (0.31) 0.35 (0.32) –0.30 (0.39) 
 0.59 (0.07) –0.49 (0.15) 0.35 (0.32) –0.15 (0.69) 
Occipital cortex 0.64 (0.04) –0.51 (0.13) 0.44 (0.20) –0.18 (0.61) 
 0.69 (0.03) –0.52 (0.12) 0.51 (0.13) –0.14 (0.70) 
Entorhinal cortex 0.69 (0.03) –0.63 (0.06) 0.26 (0.46) –0.05 (0.89) 
 0.61 (0.06) –0.51 (0.13) 0.33 (0.35) –0.19 (0.60) 
Table 4

Correlation coefficients and their significancea for parameters of the 5-Choice Serial Reaction Time Task and [14C]deoxyglucose uptake in subcortical areas

  %Correct %ITI Trials CLATE 
%Correct, percentage of correct responses; %ITI, percentage of premature responses; Trials, number of trials completed; CLATE, latency to correct response; R and L, right and left. 
aValues given are r (P). 
Nucleus accumbens (core)  0.08 (0.83) –0.03 (0.94) 0.30 (0.40) –0.68 (0.03) 
  0.16 (0.67) –0.02 (0.97) 0.40 (0.25) –0.44 (0.20) 
Nucleus accumbens (shell) –0.03 (0.93)  0.14 (0.70) 0.17 (0.64) –0.63 (0.05) 
 –0.18 (0.62)  0.26 (0.47) 0.25 (0.48) –0.58 (0.09) 
Caudate putamen  0.22 (0.55) –0.13 (0.71) 0.29 (0.41) –0.34 (0.34) 
  0.32 (0.37) –0.15 (0.68) 0.39 (0.26) –0.36 (0.31) 
Ventral pallidum  0.51 (0.13) –0.48 (0.16) 0.19 (0.59) –0.35 (0.33) 
  0.58 (0.08) –0.48 (0.16) 0.31 (0.38) –0.35 (0.32) 
Globus pallidus  0.46 (0.18) –0.39 (0.26) 0.40 (0.26) –0.51 (0.13) 
  0.42 (0.23) –0.26 (0.46) 0.34 (0.33) –0.47 (0.17) 
Substantia innominata 0.63 (0.05) –0.63 (0.05) 0.30 (0.40) –0.34 (0.33) 
  0.52 (0.12) –0.49 (0.15) 0.27 (0.45) –0.31 (0.39) 
Lateral hypothalamus  0.36 (0.31) –0.36 (0.30) 0.16 (0.66) –0.14 (0.71) 
  0.55 (0.10) –0.42 (0.23) 0.30 (0.40) –0.04 (0.90) 
Ventromedial hypothalamus  0.47 (0.18) –0.49 (0.15) 0.12 (0.74)  0.06 (0.88) 
  0.45 (0.20) –0.44 (0.21) 0.14 (0.71) –0.03 (0.93) 
Ventrolateral thalamic region  0.54 (0.11) –0.56 (0.09) 0.35 (0.32) –0.06 (0.88) 
  0.53 (0.12) –0.53 (0.11) 0.33 (0.36)  0.02 (0.95) 
Ventromedial thalamic region  0.44 (0.21) –0.47 (0.17) 0.27 (0.45) –0.10 (0.78) 
  0.48 (0.16) –0.49 (0.15) 0.32 (0.36) –0.06 (0.86) 
Dorsal thalamic region  0.50 (0.14) –0.43 (0.22) 0.38 (0.28) –0.18 (0.63) 
  0.50 (0.14) –0.47 (0.17) 0.43 (0.22) –0.23 (0.52) 
Amygdala  0.47 (0.17) –0.49 (0.15) 0.22 (0.54) –0.11 (0.76) 
  0.50 (0.14) –0.49 (0.15) 0.21 (0.55) –0.07 (0.84) 
Hippocampus 0.69 (0.03) –0.61 (0.06) 0.34 (0.35) –0.14 (0.69) 
 0.73 (0.02) –0.62 (0.06) 0.38 (0.29) –0.18 (0.62) 
Substantia nigra 0.63 (0.05) –0.57 (0.09) 0.44 (0.20) –0.11 (0.76) 
  0.60 (0.07) –0.53 (0.12) 0.43 (0.22) –0.16 (0.66) 
Superior colliculus  0.52 (0.13) –0.49 (0.16) 0.24 (0.51) –0.14 (0.70) 
  0.56 (0.09) –0.49 (0.15) 0.34 (0.33) –0.23 (0.52) 
Raphe  0.52 (0.12) –0.45 (0.19) 0.24 (0.51) –0.07 (0.85) 
  0.55 (0.10) –0.45 (0.19) 0.26 (0.48) –0.09 (0.79) 
  %Correct %ITI Trials CLATE 
%Correct, percentage of correct responses; %ITI, percentage of premature responses; Trials, number of trials completed; CLATE, latency to correct response; R and L, right and left. 
aValues given are r (P). 
Nucleus accumbens (core)  0.08 (0.83) –0.03 (0.94) 0.30 (0.40) –0.68 (0.03) 
  0.16 (0.67) –0.02 (0.97) 0.40 (0.25) –0.44 (0.20) 
Nucleus accumbens (shell) –0.03 (0.93)  0.14 (0.70) 0.17 (0.64) –0.63 (0.05) 
 –0.18 (0.62)  0.26 (0.47) 0.25 (0.48) –0.58 (0.09) 
Caudate putamen  0.22 (0.55) –0.13 (0.71) 0.29 (0.41) –0.34 (0.34) 
  0.32 (0.37) –0.15 (0.68) 0.39 (0.26) –0.36 (0.31) 
Ventral pallidum  0.51 (0.13) –0.48 (0.16) 0.19 (0.59) –0.35 (0.33) 
  0.58 (0.08) –0.48 (0.16) 0.31 (0.38) –0.35 (0.32) 
Globus pallidus  0.46 (0.18) –0.39 (0.26) 0.40 (0.26) –0.51 (0.13) 
  0.42 (0.23) –0.26 (0.46) 0.34 (0.33) –0.47 (0.17) 
Substantia innominata 0.63 (0.05) –0.63 (0.05) 0.30 (0.40) –0.34 (0.33) 
  0.52 (0.12) –0.49 (0.15) 0.27 (0.45) –0.31 (0.39) 
Lateral hypothalamus  0.36 (0.31) –0.36 (0.30) 0.16 (0.66) –0.14 (0.71) 
  0.55 (0.10) –0.42 (0.23) 0.30 (0.40) –0.04 (0.90) 
Ventromedial hypothalamus  0.47 (0.18) –0.49 (0.15) 0.12 (0.74)  0.06 (0.88) 
  0.45 (0.20) –0.44 (0.21) 0.14 (0.71) –0.03 (0.93) 
Ventrolateral thalamic region  0.54 (0.11) –0.56 (0.09) 0.35 (0.32) –0.06 (0.88) 
  0.53 (0.12) –0.53 (0.11) 0.33 (0.36)  0.02 (0.95) 
Ventromedial thalamic region  0.44 (0.21) –0.47 (0.17) 0.27 (0.45) –0.10 (0.78) 
  0.48 (0.16) –0.49 (0.15) 0.32 (0.36) –0.06 (0.86) 
Dorsal thalamic region  0.50 (0.14) –0.43 (0.22) 0.38 (0.28) –0.18 (0.63) 
  0.50 (0.14) –0.47 (0.17) 0.43 (0.22) –0.23 (0.52) 
Amygdala  0.47 (0.17) –0.49 (0.15) 0.22 (0.54) –0.11 (0.76) 
  0.50 (0.14) –0.49 (0.15) 0.21 (0.55) –0.07 (0.84) 
Hippocampus 0.69 (0.03) –0.61 (0.06) 0.34 (0.35) –0.14 (0.69) 
 0.73 (0.02) –0.62 (0.06) 0.38 (0.29) –0.18 (0.62) 
Substantia nigra 0.63 (0.05) –0.57 (0.09) 0.44 (0.20) –0.11 (0.76) 
  0.60 (0.07) –0.53 (0.12) 0.43 (0.22) –0.16 (0.66) 
Superior colliculus  0.52 (0.13) –0.49 (0.16) 0.24 (0.51) –0.14 (0.70) 
  0.56 (0.09) –0.49 (0.15) 0.34 (0.33) –0.23 (0.52) 
Raphe  0.52 (0.12) –0.45 (0.19) 0.24 (0.51) –0.07 (0.85) 
  0.55 (0.10) –0.45 (0.19) 0.26 (0.48) –0.09 (0.79) 
Figure 1.

 Illustrative examples of DG uptake (expressed as ratio = [14C]DG uptake normalized to corpus callosum) obtained from a well-performing rat (A, B) and a poorly performing rat (C, D). Coronal sections pass through the parietal cortex (area 1) (A, C) and through the cingulate cortex and globus pallidus (B, D).

Figure 1.

 Illustrative examples of DG uptake (expressed as ratio = [14C]DG uptake normalized to corpus callosum) obtained from a well-performing rat (A, B) and a poorly performing rat (C, D). Coronal sections pass through the parietal cortex (area 1) (A, C) and through the cingulate cortex and globus pallidus (B, D).

Figure 2.

 The relationship between the percentage of correct responses (%Correct) and DG uptake in (A) in the right and left medial prefrontal cortex (correlations for these relationships were r = 0.657 and 0.710, respectively, P < 0.05) and (B) the right and left parietal cortices (correlations for these relationships were r = 0.678 and 0.684, respectively, P < 0.05). •, well-performing rats ; ♦, poorly performing rats.

Figure 2.

 The relationship between the percentage of correct responses (%Correct) and DG uptake in (A) in the right and left medial prefrontal cortex (correlations for these relationships were r = 0.657 and 0.710, respectively, P < 0.05) and (B) the right and left parietal cortices (correlations for these relationships were r = 0.678 and 0.684, respectively, P < 0.05). •, well-performing rats ; ♦, poorly performing rats.

Figure 3.

 The relationship between DG uptake in the right and left cingulate cortices and (A) percentage of correct responses (%Correct; correlations for these relationships were r = 0.672 and 0.720, respectively, P < 0.05) and (B) percentage of premature responses (%ITI; correlations for these relationships were r = –0.670 and –0.737, respectively, P < 0.05). •, well-performing rats ; ♦, poorly performing rats.

Figure 3.

 The relationship between DG uptake in the right and left cingulate cortices and (A) percentage of correct responses (%Correct; correlations for these relationships were r = 0.672 and 0.720, respectively, P < 0.05) and (B) percentage of premature responses (%ITI; correlations for these relationships were r = –0.670 and –0.737, respectively, P < 0.05). •, well-performing rats ; ♦, poorly performing rats.

Figure 4.

 The relationship between DG uptake in the left ventrolateral orbital cortex cortices and (A) percentage of correct responses (%Correct; the correlation for this relationship was r = 0.673, P < 0.05) and (B) percentage of premature responses (%ITI; the correlation for this relationship was r = –0.694, P < 0.05). •, well-performing rats ; ♦, poorly performing rats.

Figure 4.

 The relationship between DG uptake in the left ventrolateral orbital cortex cortices and (A) percentage of correct responses (%Correct; the correlation for this relationship was r = 0.673, P < 0.05) and (B) percentage of premature responses (%ITI; the correlation for this relationship was r = –0.694, P < 0.05). •, well-performing rats ; ♦, poorly performing rats.

Figure 5.

 Schematic distribution of the cerebral regions where cerebral activity (DG uptake) is related to attentional performances. Regions were anatomically defined according to Paxinos and Watson (Paxinos and Watson, 1986). Cg, cingulate cortex; Ent, entorhinal cortex; Fr1,2,3, frontal cortex; Hip, hippocampus; mPFr, medial prefrontal cortex; Par1, parietal cortex; Occ, occipital cortex; SI, substantia innominata; Sn, substantia nigra; VLO, ventrolateral orbital cortex.

Figure 5.

 Schematic distribution of the cerebral regions where cerebral activity (DG uptake) is related to attentional performances. Regions were anatomically defined according to Paxinos and Watson (Paxinos and Watson, 1986). Cg, cingulate cortex; Ent, entorhinal cortex; Fr1,2,3, frontal cortex; Hip, hippocampus; mPFr, medial prefrontal cortex; Par1, parietal cortex; Occ, occipital cortex; SI, substantia innominata; Sn, substantia nigra; VLO, ventrolateral orbital cortex.

This study was supported by the Academy of Finland. The authors are grateful to Dr B. Bontempi (CNRS UMR 5807, Université Bordeaux I, Talence, France) for his valuable advice. Dr Jukka Jolkkonen (Department of Neurology, University of Kuopio) is acknowledged for his kind help in the preparation on the graphs on autoradiograms. Dr Ewen MacDonald (Department of Pharmacology & Toxicology, University of Kuopio) is acknowleged for the revision of language.

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