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Previous Article | Next Article
The Journal of Neuroscience, September 1, 2002, 22(17):7809-7817
Conceptual, Spatial, and Cue Learning in the Morris Water Maze in Fast or Slow Kindling Rats: Attention Deficit Comorbidity
Hymie Anisman and Dan C. McIntyre Institute of Neuroscience, Carleton University, Ottawa, Ontario, Canada, K1S 5B6
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
Rat lines selectively bred for differences in amygdala excitability, manifested by "fast" or "slow" kindling epileptogenesis, display several comorbid features related to anxiety and learning. To assess the nature of the learning deficits in fast kindling rats, performance was evaluated in several variants of a Morris water-maze test. Regardless of whether the location of the platform was fixed or varied over days (matching-to-place task), the fast rats displayed inferior performance, suggesting both working and reference memory impairments. Furthermore, when the position of the platform was altered after the response was acquired, fast rats were more persistent in emitting the previously acquired response. The poor performance of fast rats was also evident in both cued and uncued tasks, indicating that their disturbed learning was not simply a reflection of a spatial deficit. Moreover, fast rats could be easily distracted by irrelevant cues, suggesting that these animals suffered from an attentional disturbance. Interestingly, when rats received several training trials with the platform elevated, permitting them to develop the concept of facile escape, the performance of fast rats improved greatly. The performance disturbance in fast rats may reflect difficulties in forming a conceptual framework under conditions involving some degree of ambiguity, as well as greater distractibility by irrelevant cues. These various attributes of the fast rats may serve as a potentially useful animal model of disorders characterized by an attention deficit.
Key words: seizure susceptibility; genetic differences; spatial learning; cued learning; concept formation; attention
INTRODUCTION
Transgenic and knock-out mice have been used extensively in an effort to identify processes underlying pathological states (Crawley, 1999
). However, because many pathologies involve multiple gene effects, or gene-environment interactions, an alternative approach has involved rat/mouse lines selectively bred to exhibit high or low levels of a given phenotype and then relating these to specific neuroanatomical, physiological, and chemical factors (Dudek and Underwood, 1993
Not unexpectedly, comorbid features are often evident in selectively bred animals, likely reflecting either genetic pleiotropic or linkage effects (Parmigiani et al., 1999
The CNS differences between the fast and slow rats are widespread, being evident with respect to excitability differences in parahippocampal cortices (piriform and perirhinal) and the hippocampus (McIntyre et al., 1999a
MATERIALS AND METHODS
Animals and housing conditions
The procedures described in the present investigation were approved by the Carleton University Animal Care Committee and met all guidelines set out by the Canadian Council on Animal Care. Rats of the fast and slow lines were selectively bred from an original parent population composed of Wistar and Long-Evans Hooded rats. These lines, both of which are pigmented (hooded), were initially established at McMaster University (Hamilton, Ontario) through selective breeding without brother × sister matings. After the F11 generation they were relocated to Carleton University, Institute of Neuroscience, where the differences in seizure susceptibility between these lines have remained despite relaxation of the selection procedure. Development of amygdala-kindled convulsions in fast rats normally requires ~10 trials, whereas 30 or more trials are required in slow rats (McIntyre et al., 1999aApparatus
Behavioral testing was conducted in a white, circular, polypropylene pool (158 cm in diameter, 60 cm height) that was filled with water (21°C, 37.5 cm deep) made opaque by the addition of ~2000 cc of powdered milk. A clear Plexiglas, adjustable platform (35 cm height, 14 cm circumference), could be submerged 2.0 cm below the water surface or elevated 0.5 cm above the water level. In the case of cued training, unless specified otherwise, a proximal cue comprising a black cardboard in the shape of a cross (10 × 7 × 2.5 cm) hung above (25 cm) the platform. The pool was situated in a laboratory that contained assorted extra-maze cues, and the experimenter remained in the same position in the room throughout all testing trials. In some experiments, video recordings of the animals' performance was recorded by a camera situated on the ceiling, directly above the center of the pool. The videotape records were analyzed using a Smart Tracking System (San Diego Instruments, San Diego, CA).Behavioral procedures
The simple Morris water-maze test requires that an animal, placed in different start positions, must find the location of a submerged platform. This simple response, however, may involve several components, some of which are not readily discerned in the typical Morris water-maze test. For instance, rats must initially learn the concept that a platform exists and that escape is possible. Additionally, once the platform is found they must maintain that memory over the short term, based on the platform's position during the immediately preceding trials (working memory), and over longer periods between days (reference memory) (Whishaw et al., 1995Statistical analyses
The latency data for each of the experiments was subjected to mixed model ANOVAs with days and trials within days as within-group variables. All other variables were treated as between-group variables. The means comprising main effects and the means for simple effects of significant interactions, or for those interactions where a priori predictions had been made, were compared by Bonferonni corrected t tests. When within-group comparisons were made, such as those involving days-to-trials effects, Tukey's comparisons were used. Analyses of the latencies to reach the platform during acquisition and the total distance rats swam were found to be highly correlated (r > 0.90 on most trials), and hence only latency data are presented. When the purpose of the experiment was to determine the specific swim path that the rats used, then the swim patterns are described.
RESULTS
Experiment 1: forced swim performance in the fast and slow rats
Active swimming varied as a function of the rat line × trials interaction (F(2,16) = 3.90; p < 0.05). The multiple comparisons indicated that during the first trial the two lines of rats did not differ from one another, with both spending almost all of their time engaged in active responses (Table 1). In the fast rats, active swimming remained unchanged throughout the three trials, whereas in slow rats it declined over the three trials and was replaced by floating.
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Table 1. Time engaged in active swimming (seconds) over 3 min periods Experiment 2a-b: spatial learning in a fixed platform position paradigm
Figure 1 shows the average latency (collapsed over the four trials each day) for the fast and slow rats to reach the fixed platform over each of 4 d. The ANOVA showed that latencies declined over days in each of the lines (F(3,42) = 18.37; p < 0.01) and that the performance of slow rats was superior to that of fast rats (F(1,14) = 24.59; p < 0.01). By the last two trials of the fourth test day, both rat lines exhibited latencies of <10 sec. Analyses of the videotape records did not reveal any specific search patterns unique to either rat line, which is not surprising given the rapid acquisition in both lines. The most notable difference between the lines was simply that during the initial test days slow rats were more likely to swim directly to the platform, whereas fast rats tended to make more directional or turning errors.
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Figure 1. Mean (±SEM) acquisition latencies (left panel) to reach the platform on four consecutive days (averaged over 4 trials per day) among fast and slow rats tested in a fixed position Morris water-maze paradigm. Time spent in the goal quadrant (i.e., the quadrant in which the platform had been located previously) over four 15 sec periods is shown in the right panel.
On the probe trial, the time that rats spent in the quadrant that had previously contained the platform varied as a function of the rat line × blocks of time interaction (F(3,42) = 26.60; p < 0.01). As shown in Figure 1 (right panel) and confirmed by the post hoc tests, the slow rats initially spent more time in this quadrant than did the fast rats, but this difference declined thereafter. In contrast, the remaining time spent in the quadrant increased in the fast rats. As a result, the initial between-group difference was absent during the second half of the probe trial. Examination of the probe trial video records indicated that of the eight slow rats, four swam directly to the platform (within a 30 cm band), whereas four approached the platform after making a relatively wide arc. In each instance, they remained in the goal quadrant for a few seconds before leaving this area. Of the fast rats, four swam directly to the platform, two made a wide arc in swimming to the platform, and two swam in the wrong direction. In contrast to the slow rats that lingered in the goal quadrant, all fast rats swam through the platform's previous location, and then when they reached the far wall of the pool they turned and left the quadrant. As a result, they initially spent little time in the quadrant. However, as reflected in Figure 1 (right panel), over the course of the trial, these fast rats tended to return to the location where the platform had been situated.
In the experiment assessing the effect of changing the platform position, initial acquisition was again found to improve over the 4 d (F(3,30) = 30.22; p < 0.01), and the performance of Slow rats was superior to that of fast rats (F(1,10) = 19.36; p < 0.01). As shown in Figure 2 (left panel), however, by the fourth training day this difference was modest. On the fifth day, when the position of the platform was altered, performance was found to improve over trials (F(3,30) = 3.78; p < 0.05), and the performance of slow rats was superior to fast rats. Figure 2, right-hand panel, shows the mean latencies on each trial on this test day. Interestingly, the performance of the fast rats (averaged over these four trials) was as poor as it had been on the first training day of initial acquisition. In contrast, the performance of slow rats was markedly superior to that evident on the initial training day. In effect, although the change in position resulted in negative transfer relative to that seen on the preceding day, the slow rats clearly gained from the experience of the preceding 4 d. It is of interest, as well, that although all rats of both lines initially swam to the position of the platform, this tendency was more persistent in the fast rats. Indeed, throughout the four test trials (on day 5), all fast rats initially swam toward the position where the platform had been located previously. On the latter two trials, this tendency persisted, although once they swam past the previous platform location they soon found the new platform location. In the slow line, the tendency to swim toward the original platform location was seen only on the first two trials, after which all but one of these rats swam directly to the new location. Clearly, the fast rats, like their slow counterparts, had learned the response, but the fast rats were less likely to abandon this response even after the platform had been moved.
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Figure 2. The left panel shows the mean (±SEM) latencies to reach the platform on four consecutive acquisition days (averaged over 4 trials per day) among fast and slow rats tested in a fixed position Morris water-maze paradigm. The right panel shows the latencies on four consecutive trials among rats tested on a fifth day under conditions in which the platform was placed in a new location.
Experiment 3: spatial learning in a matching-to-place paradigm
Predictably, acquisition in the more difficult variable-platform position paradigm progressed more slowly than in the fixed-position paradigm. The ANOVA indicated that the latency to the platform varied as a function of the line × days × trials interaction (F(35,630) = 2.23; p < 0.01). The multiple comparisons showed that performance improved over days and trials in both lines. However, performance among slow rats was superior to that of fast rats throughout the 6 d of testing (Fig. 3). On the initial trial of the first test day, the two rat lines exhibited similar response latencies. On that day, the performance of the slow rats improved markedly over the eight trials, with latencies falling to ~15 sec by the last three trials. In contrast, the fast rats exhibited inferior acquisition with very little improvement over trials. Latencies over trials declined more rapidly in the slow rats than in the fast rats. During the initial 2 test days, the superiority of the slow rats was clearly evident throughout the test session; this effect was slightly diminished during the next 4 d, particularly during the later trials of each session as the fast rats eventually acquired the appropriate response.
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Figure 3. Mean (±SEM) latencies to reach the platform on eight consecutive trials over 6 d (in 2 d blocks) in a matching-to-place paradigm among fast and slow rats. In this paradigm the position of the platform varied over days so that rats were required to remember the position of the platform based on the initial trial(s) of that day.
It is particularly interesting that over days, the trial 1 performance of slow rats improved slightly, although the position of the platform varied from day to day, suggesting that these rats were acquiring the general concept that a platform existed or they were developing a more efficient search strategy. In contrast, there was little improvement on trial 1 performance over the 6 d in the fast rats (Table 2).
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Table 2. Latencies of trial 1 on each of 6 consecutive days in a matching-to-place paradigm Experiment 4: cue learning in a fixed platform position paradigm
Performance in the cued task varied as a function of the line × days × trials interaction (F(9,270) = 2.16; p < 0.05). The multiple comparisons indicated that performance improved over days in both lines, but the performance of slow rats was superior to that of fast rats. On the first day, the superior performance of slow rats was only evident during the third and fourth trials, whereas on the ensuing 2 d the difference between the rat lines was apparent throughout all trials. On the last day of testing, in which all animals had essentially acquired the response, the rat line difference was limited to the first testing trial (Fig. 4, left panel).
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Figure 4. Mean (±SEM) latencies to reach a platform cued by an overhanging stimulus on four consecutive days (averaged over 4 trials per day) among fast and slow rats. The cued platform was always in the same location (left panel) or varied on each trial of each day (right panel).
Experiment 5: cue learning in a variable platform position paradigm
Figure 4 (right panel) shows the performance of the two rat lines in a cued paradigm in which the position of the platform changed on every trial. Changing the position of the platform on each trial of each day, while signaling the platform location, provided a method of assessing cued learning independent of spatial factors. In this test, the latencies to reach the platform were only moderately longer than they had been when the location of the platform was consistent across trials and days (compare Fig. 4). Latencies to reach the platform among slow rats again were shorter than that of fast rats (F(1,20) = 17.78; p < 0.01). Although performance improved over trials and days in both lines, the superiority of slow rats was maintained. However, as seen in Figure 4, by the fourth day the difference between the rat lines was small. Indeed, in this test, performance of the fast rats was far superior to that seen when the platform location was not cued (Fig. 3), attesting to the fact that the generally poorer performance of fast rats was not caused by visual impairments.
Experiment 6a-d: influence of pretraining on performance in a fixed platform and a matching-to-place task
If rats received four pretraining trials with an elevated platform (in a fixed platform position paradigm) and were tested subsequently with the platform submerged, both rat lines performed well, and the difference normally observed between them was entirely eliminated (F < 1). In this case, the latencies of fast rats on days 1-4 were 35.67 ± 3.97, 11.63 ± 1.46, 7.87 ± 0.99, and 8.08 ± 1.07, whereas those of slow rats were 28.50 ± 3.81, 16.33 ± 2.71, 10.62 ± 1.10, and 10.16 ± 1.08. This result may be compared with the earlier experiments that showed substantial differences in latencies between the fast and slow rats in the fixed platform paradigm.
Pretraining with an elevated platform also appeared to minimize the differences between the rat lines tested in the matching-to-place test. Figure 5 shows the performance on days 1-6 (in blocks of 2 d) among rats that received either one, three, or eight trials of pretraining with the raised platform. In rats that received only a single pretraining trial, the line differences were marked and sustained. The ANOVA indicated that performance varied as a function of the line × days × trials interaction (F(35,490) = 1.57; p < 0.05). As observed in the earlier variable-platform position paradigm, where rats had not received pretraining, the multiple comparisons revealed that on the first trial of the first test day, the performance of the two lines was similar, but on the ensuing trials, performance improved more rapidly in the slow rats. As a result, the two lines differed markedly throughout the remainder of the test session. This difference was maintained throughout the 6 test days, although the difference was less marked on days 5 and 6, particularly during the later trials (Fig. 5, top panel).
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Figure 5. Mean (±SEM) latencies to reach the platform on eight consecutive trials over 6 d (in 2 d blocks) in a matching-to-place paradigm among fast and slow rats. On the day before the initiation of the training rats received either one, three, or eight training trials in which the platform was elevated (top, middle, or bottom panel, respectively).
When rats were given three pretraining trials with the elevated platform, subsequent acquisition latencies were still found to vary as a function of the line × trials interaction (F(7,98) = 2.21; p < 0.05). Generally, the line difference was least notable during the first trial of each day, became more pronounced on the intermediate trials, and was modest at the end of a session. The line × days interaction was not significant, but the performance of the two lines was similar on the last 2 test days. As seen in Figure 5 (middle panel), on the last 2 test days, the trial 1 latencies were moderately reduced relative to those observed on the first day, but on subsequent trials performance improved dramatically so that the difference between fast and slow rats was minimal. It is significant that comparisons between the rats that received one versus three pretraining trials with an elevated platform indicates that the latter rats gained appreciably more positive transfer than those rats that received only one such trial. Indeed, as seen in Figure 5, on days 3-4 of testing, the performance of rats that received three trials with the elevated platform was similar to the day 6 performance of rats that received only one trial.
Finally, after eight trials of pretraining with the elevated platform, performance in the submerged platform test varied as a function of the interactions between lines × days (F(5,65) = 6.79; p < 0.01) and lines × trials (F(7,91) = 2.51; p < 0.05). The multiple comparisons indicated that the performance of both lines improved over days and trials within each day. The difference between the lines was evident primarily on the initial 2 test days, but was absent thereafter, as the performance of the fast rats improved (Fig. 5, bottom panel). Importantly, even the trial 1 latencies declined dramatically over days, and by the last test day, the trial 1 latencies were very rapid, because animals had apparently learned the strategy that the submerged platform was located within a circumscribed area.
Experiment 7: influence of nonspatial pretraining on performance in a matching-to-place task
To assess whether distal cues contributed to the facilitative effects of pretraining with an elevated platform, rats received pretraining with distal cues visible or absent (the pool having been surrounded by a curtain during pretraining). On the pretraining day, during which latencies to reach an elevated platform were evaluated, there were no differences as a function of the rat line (F < 1: mean = 19.75 ± 1.64 and 19.01 ± 1.59 for fast and slow rats, respectively) or as a function of whether distal cues were present or absent during training with the elevated platform (F < 1: mean ± SEM = 20.29 ± 1.58 and 18.47 ± 1.65 for rats tested with distal cues absent or present).
Figure 6 shows the latencies to reach the platform on each of eight trials (collapsed over the 4 test days) between fast and slow rats as a function of the pretraining condition. Response latencies in this paradigm varied as a function of the rat line × pretreatment condition × trials interaction (F(14,336) = 2.26; p < 0.01). The post hoc tests indicated that on the first daily trial, performance was comparable regardless of the rat line or the treatment condition. Among the slow rats the response was generally acquired quickly, and hence the performance-enhancing effects of the pretraining procedure were modest, being limited to trials 2-4. In the absence of any pretreatment, the performance of the slow rats was superior to that of the fast rats. However, among pretrained fast rats, performance was superior to that seen among non-pretrained fast rats. Importantly, in both rat lines, pretraining enhanced performance, regardless of whether distal cues were present or absent. Thus, the experience with the elevated platform itself, rather than training in the context of distal cues, was primarily responsible for the superior performance observed.
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Figure 6. Mean (±SEM) latencies to reach the platform on eight consecutive trials (collapsed over 4 d) in a matching-to-place paradigm among fast (left) and slow (right) rats. On the day before the commencement of testing, rats of each line received either (1) no treatment, (2) eight training trials with an elevated platform and distal cues present, or (3) eight training trials with an elevated platform and distal cues absent (achieved by surrounding the pool with an opaque curtain).
Analysis of swim paths generally paralleled the latencies to reach the platform. Indeed, even on the first day of testing, the effects of the pretraining procedures were detectable in that correct responses (i.e., adopting a direct path to the platform) varied as a function of the rat line and the pretraining condition (F(1,48) = 4.29, F(2,48) = 4.94; p < 0.05). In the absence of pretraining, slow rats emitted more correct responses than did fast rats (mean ± SEM = 2.11 ± 0.20 and 1.00 ± 0.37, respectively). In slow rats, pretraining enhanced performance (2.79 ± 0.36 and 2.89 ± 0.26 among those trained with and without the curtain), and this effect was even more pronounced among fast rats (2.22 ± 0.57 and 2.56 ± 0.47 with and without the curtain, respectively). Thus, it seems that pretraining with an elevated platform, regardless of whether distal cues were present, enhanced later performance in a matching-to-place test.
Experiment 8: search strategies adopted under ambiguous conditions
Fast and slow rats received eight training trials in which the position of the submerged platform varied over trials. Thus, the latency to find the platform stemmed from the rats either developing an effective search strategy (e.g., swimming in progressively smaller concentric circles or traversing the pool, as opposed to exhibiting thigmotaxic responses) or being more adept swimmers. The latencies of the slow rats to find the platform were significantly shorter than that of the fast rats (F(1,20) = 15.24; p < 0.01). The line × trials interaction was not significant, but the results over trials prove to be particularly informative. Specifically, as seen in Figure 7 (left panel), on the first three trials, performances of the two lines were essentially indistinguishable from one another. Thereafter, latencies in slow rats declined by ~40% over the test session, whereas those of fast rats remained fairly stable. As a result, by the fourth trial, the difference between the lines reached statistical significance. Evidently, slow rats were gaining from the experience, or at least they were developing an effective search strategy, which did not appear to be the case for fast rats.
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Figure 7. Mean (±SEM) latencies to reach a submerged platform (left panel) and the thigmotaxic tendency (proportional distance swum along the pool perimeter relative to the total distance traversed; right panel) on eight consecutive trials. The location of the platform varied from trial to trial and was unsignaled; therefore, learning the specific platform location was not possible.
Inspection of the video records indicated that the two rat lines exhibited very clear differences in their search styles. Figure 7 (right panel) shows the distance that rats swam along the edge of the pool (i.e., within 10 cm) as a proportion of the total swimming distance. The ANOVA indicated that swimming along the perimeter (measured as a proportion of distance swum along the perimeter over the total distance traversed) varied as a function of the rat line × trials interaction (F(1,22) = 2.73; p < 0.01). The multiple comparisons indicated that on the initial two trials, both rat lines favored a thigmotaxic response (swimming along the pool edge). Thereafter, however, the slow rats tended to make increasingly more pool traverses, and swimming along the pool edge declined. In contrast, although thigmotaxis also declined somewhat in the fast rats, they still continued to swim along the edge of the pool, and pool crossing occurred less frequently.
Experiment 9: influence of a distractor stimulus on cued variable-position performance
The latencies to reach the platform over the 4 test days for each of the two rat lines in the presence or absence of the distractor stimulus are presented in Figure 8. The ANOVA indicated that performance varied as a function of the line × distractor stimulus interaction (F(1,36) = 6.62; p = 0.01). Pairwise comparisons confirmed that among fast rats the presence of a distractor significantly increased the latencies relative to that seen in the absence of distractor cues. In contrast, the presence of distractor stimuli had no effect on the performance of slow rats. Although the effect of the distractor stimulus did not interact significantly with days, it appears from Figure 8 that the effect of the distractor was more notable during the initial days of testing. Clearly, in this experiment, the fast rats were more susceptible to the disruptive effects of distractor cues than were the slow rats.
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Figure 8. Mean (±SEM) latencies to reach the platform on 4 consecutive days (4 trials per day) in a cued variable-position paradigm among fast and slow rats. Rats of each line were tested under conditions in which only the cue signaling the platform position was present (no distractor) or in which the platform was signaled but another overhanging stimulus, acting as a distractor, was also present on each trial (distractor).
DISCUSSION
It is not unusual for animals selected for a particular phenotype to exhibit comorbid characteristics remote to the selection criteria (Elias et al., 1975
Ordinarily, spatial learning deficits associated with hippocampal disturbances are less evident when the position of the platform is cued by proximal stimuli (Aggleton et al., 1986
In addition to the poor performance in the simple spatial and cued tests, fast rats displayed markedly inferior performance in a matching-to-place task. In effect, having discovered the platform position on the first trial of each day, fast rats benefited less than slow rats, suggesting inferior working memory in the fast line. Interestingly, the lines not only differed with respect to between-trials performance, but they were also distinguishable on the basis of their first trial response across days. In effect, unlike their slow counterparts, it appeared that fast rats were not acquiring or remembering the general concept of escape or applying it well over days. Furthermore, when the platform location was unsignaled and varied between trial on a given day, making it impossible to predict or learn its precise location, only the performance of slow rats improved over trials. The slow rats seemed to acquire the concept of escape and adopted strategies that improved performance (e.g., increased pool crossings). In contrast, fast rats seemed less able to acquire the concept and were more likely to maintain ineffective thigmotaxic responses rather than adopt more appropriate strategies. Thus, fast rats appeared behaviorally less flexible than slow rats.
It has been reported that although NMDA receptor-dependent long-term potentiation (NMDA-LTP) in the dentate gyrus is important for spatial learning (Moser et al., 1998
In accordance with the suggestion that fast rats suffer from attention problems (McIntyre and Anisman, 2000
In summary, fast rats displayed marked behavioral impairments in a Morris water-maze test that appeared to encompass several distinct deficits. In this respect, the poor performance among fast rats was not limited to spatial tasks, being noticeable even when the position of the platform was signaled by an overhanging cue. Furthermore, fast rats seemed to suffer an attentional disturbance in that they were readily distracted by irrelevant stimuli. Moreover, unlike slow rats that seemed to adopt effective search strategies even when the platform location varied from trial to trial, fast rats abandoned thigmotaxic responses less readily and thus tended not to use effective search strategies. Yet the conceptual difficulties that fast rats appeared to endure were attenuated simply by giving them limited training with an elevated platform. Once rats acquired the concept that an accessible platform existed, performance disturbances were attenuated even in the fairly difficult matching-to-place test.
As in ADHD, in which impulsivity also is a common comorbidity (Hooper and Olley, 1996
We have argued that fast rats may serve to model a syndrome such as ADHD. By themselves, the data of the present investigation do not speak to this syndrome. However, the behavior of these rats should be viewed in a broader context, including the fact that fast rats show diminished response inhibition and impulsivity. From such a perspective, the seizure-prone fast rats can provide a new animal model of ADHD with impulsivity. As already indicated, they show many attributes that define that eclectic human syndrome, including a predilection for seizures, which ordinarily is evident in up to 20% of ADHD individuals compared with <2% of the normal population (Wolf and Forsythe, 1978
FOOTNOTES Received April 17, 2002; revised June 4, 2002; accepted June 17, 2002.
This work was supported by the Canadian Institutes of Health Research. H.A. holds a Canada Research Chair in Neuroscience. We are indebted to Charlene Dodd for her assistance.
Correspondence should be addressed to Hymie Anisman, Carleton University, Institute of Neuroscience, Life Science Research Building, Ottawa, Ontario K1S 5B6, Canada. E-mail: hanisman{at}ccs.carleton.ca.
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