Impaired performance of alpha7 nicotinic receptor knockout mice in the five-choice serial reaction time task

Rationale Nicotinic receptors have been implicated in attentional performance. Nicotine can improve attention in animals and humans, but knowledge about relevant receptor subtypes is very limited.
Introduction
Nicotinic acetylcholine receptors are pentameric cation channels, so-called because of the actions of nicotine in binding to these receptors and bringing about channel opening. Twelve subunits of neuronal nicotinic receptors have been identified, α2-10 and β2-4. From these subunits the two most common nicotinic receptors formed in the brain are the heteromeric α4β2 and the homomeric α7 nicotinic receptors, at approximately 85 and 10% of total nicotinic receptors, respectively. The α7 nicotinic receptors are especially highly expressed in the hippocampus where, as well as mediating cholinergic neurotransmission, their high Ca++ permeability (Seguela et al. 1993) engenders the capacity to modulate glutamatergic and GABAergic neurotransmission both pre- and post-synaptically (Berg and Conroy 2002). This study aims to shed further light on the role of α7 receptors in behaviour and in the behavioural effects of nicotine.
Nicotine is the second most commonly abused drug in the world after alcohol; cigarette smoking, with its well-established risks of chronic disease, constitutes a great burden on societies worldwide. Considerable efforts have been expended in investigating the function of nicotinic receptors in nicotine addiction and in other neuropsychiatric conditions, and in mediating the cognitive effects of nicotine. For example, cigarette smoking is approximately twice as common in patients with schizophrenia, as it is within the general population. The α7 nicotinic receptor has been implicated in the pathogenesis of schizophrenia and more specifically in the P50 sensory gating deficit measurable in approximately 90% of schizophrenics, about 50% of their relatives and about 10% of control subjects (Waldo et al. 1991). The sensory gating deficit in schizophrenia is thought to reflect an input dysfunction that allows greater passage of information through the hippocampus for processing by other brain areas (Freedman et al. 2002). The density of α7 nicotinic receptors in hippocampus and cingulate cortex from schizophrenics is considerably reduced (Freedman et al. 1995; Marutle et al. 2001). It might, therefore, be expected that performance of tasks requiring sustained attention would require α7 nicotinic receptors.
Attempts to investigate the functional role of α7 nicotinic receptors at the behavioural level have most often involved the use of selective agonists and antagonists to these receptors. Another avenue of research that has recently become possible uses genetically modified mice with specific deletions of genes for neuronal nicotinic receptors. Mice with targeted deletion of the gene for the α7 nicotinic receptor lack α-bungarotoxin binding sites in the hippocampus and fast, rapidly desensitizing nicotinic currents in hippocampal neurons (Orr-Urtreger et al. 1997). Viability and brain morphology appear normal in these mice; of special interest is the lack of any impairment of sensorimotor gating as measured with both auditory and tactile stimuli in the prepulse inhibition paradigm (Paylor et al. 1998). This result is consistent with the lack of improvement in prepulse inhibition with nicotinic α7 agonists (Olivier et al. 2001; Schreiber et al. 2002), although such agonists are able to improve auditory gating in animal models of sensory gating deficit (O’Neill et al. 2003).
In other behavioural studies, α7−/− mice have not proved to be considerably different in behaviour from wild-type littermates (Paylor et al. 1998). In locomotion, α7−/− mice had a tendency towards less horizontal and vertical activity and spent significantly more time in the centre of the open field than the wild-type mice, but in the light–dark test of anxiety there were no significant differences between the two groups (Paylor et al. 1998). In the delayed matching-to-place task in the Morris water maze, α7−/− mice were slightly impaired in comparison with wild-types, as shown by a longer time to find the hidden platform (Fernandes et al. 2005). Nicotine-induced locomotor depression, hypothermia, seizures, drug discrimination and tolerance were unchanged in α7−/− mice (Franceschini et al. 2002; Tritto et al. 2004; Stolerman et al. 2004; Naylor et al. 2005). However, the α7−/− mice were less sensitive to impairments in contextual learning brought about by ethanol (Wehner et al. 2004).
The series of experiments presented in this paper use the five-choice serial reaction time task (5-CSRTT), a commonly used model of attentional performance that is considered analogous to the continuous performance test used in human research. The test requires responses to visual stimuli presented in one of five locations in one wall of an operant conditioning chamber. In a previous study Young et al. (2004) found that α7−/− mice took longer to acquire the task than wild-type C57 Bl/6J mice and made more omission errors, but they were not different with respect to accuracy or the latencies of correct responses. Young et al. (2004) also reported significant improvements in accuracy and reductions in omission errors at several doses of nicotine in C57 Bl/6J mice, including a reduction in the latency of correct responses with a 0.3-mg/kg dose of nicotine. In this paper, the first published report of nicotine improving the performance of mice in the 5-CSRTT and the effects seen were not dose-related, but the findings were important and need replication and investigation under a wider range of conditions.
Several studies have reported that nicotine enhances the attentional performance of rats in the 5-CSRTT (Hahn and Stolerman 2002; Hahn et al. 2002, 2003a), although the use of selective nicotinic agonists and antagonists has provided no evidence for involvement of the α7 receptor. The relatively specific α7 agonist AR-R17779 had no effect upon task performance in an experimental procedure in which administration of nicotine, epibatidine and isoarecolone all improved performance (Hahn et al. 2003a). These agonists all have much higher relative affinities than AR-R17779 for heteromeric nicotinic receptors. The α4β2 nicotinic agonist SIB1765F has also been shown to improve 5-CSRTT performance in rats (Grottick and Higgins 2000). The α7 antagonist methyllycaconitine did not block the effects of nicotine on response latency and anticipatory responding, whereas dihydro-β-erythroidine, an antagonist at heteromeric receptors, did block the effect of nicotine on these measures (Blondel et al. 2000; Grottick and Higgins 2000). However, the α7 agonist GTS-21 has been reported to improve performance in various tests of attention and memory in healthy human volunteers when given in repeated doses (Kitagawa et al. 2003) and in a test of memory in monkeys 24 h after administration (Briggs et al. 1997). The small number of drugs investigated and their limited selectivity indicates a need for investigations on the role of α7 receptors in attention by another approach, such as the α7−/− mouse.
The aims of the present study were, therefore, to investigate the attentional performance of α7−/− mice using the 5-CSRTT and to determine whether the gene deletion influenced any effects of nicotine on performance. If the α7 nicotinic receptor is important in attentional mechanisms then it should be possible to confirm the attentional deficits reported by Young et al. (2004) in α7−/− mice. If nicotine improves attention, at least in part, through these receptors then it should also be possible to show that nicotine-induced attentional enhancement is diminished in such animals. The procedures used in the initial stages of training were based on those of Patel et al. (2006) because they were close to those used in studies of rats where nicotine improved performance (Hahn et al. 2002; Hahn and Stolerman 2002). Mice were also trained with both fixed and variable inter-trial intervals (ITI) so that α7−/− and wild-type animals could be compared for their abilities in coping with the temporally unpredictable stimuli in the variable ITI condition. In the later stages of the study, some parameters were modified to make the training procedure more similar to that of Young et al. (2004) so as to facilitate comparisons and to clarify reasons for certain differences in the results obtained.
Materials and methods
Animals
The animals used were descendants of α7−/− mice produced by Orr-Urtreger et al. (1997), bred from heterozygotes purchased from the Jackson Laboratory, USA (B6.129S7-Chrna7tm1 Bay, stock no. 003232). The mice were at backcross generation N8, the background strain was C57BL/6, donor strain 129S7 via ABI 2.1 ES cell line; subsequent breeding was always carried out using heterozygous pairs. Twenty-four male α7−/− mice and 24 male wild-type littermates were used in initial training. Genotypes were determined with the methods used by Stolerman et al. (2004), and all mice were injected with TROVAN sterile transponder chips (Mid Fingerprint, Dorset UK) at age 10 weeks. These groups were divided into sub-groups of 12 mice each trained with fixed and variable ITI. One wild-type mouse was eliminated from the study at an early stage due to an unresolvable eye infection. Table 1 lists the subsequent parameter changes that took place as training proceeded.
Table 1Parameters for all experimentsStudyStimulus durationLimited holdFixed ITIVariable ITITime-outAnticipatory time-outSessions to criteria1a60022 (0.25–3.75)00760044 (0.5–7.5)20210044 (0.5–7.5)2025144 (0.5–7.5)2042444 (0.5–7.5)2071544 (0.5–7.5)20281b154NA20NA2a12NA4 (0.5–7.5)22152b12NA4 (0.5–7.5)22NAStudies 1a and 2a refer to experiments looking at baseline (undrugged) performance at initial and final parameters, whereas studies 1b and 2b refer to the first and second of the experiments with nicotine. For experiment 1a, where ITI values are shown for both fixed and variable ITI mode, these refer to settings for different groups of mice. All times stated are in seconds. For variable ITI the mean, smallest and largest values are shown.NA Not applicable
Food restriction was started at age 11–12 weeks and behavioural training began a week later. The mice were kept at 80% of their free-feeding weights as calculated from a previously obtained growth curve for animals from the same colony. The animals were weighed daily and fed accordingly. All animals had access to water while in their home cages and were individually housed in a room at 21°C and on a 12-h light/dark cycle, with lights coming on at 0700 hours.
The studies complied with local ethical requirements and were carried out in accordance with the Animals (Experimental Procedures) Act, 1986.
Apparatus
Sound-insulated and ventilated enclosures containing aluminium operant chambers were used (CENES, Cambridge, UK). The chambers measured 14 cm across × 12 cm high × 11–13 cm deep. The back wall of the chamber was curved so that each nose-poke hole was equidistant from the feeder tray at the centre of the front of the chamber. There were five nose-poke holes 1 cm in diameter, 2 cm above the floor, 1.5 cm apart and 1.5 cm deep. Each hole had a green-light-emitting diode at the end and a photoelectric cell at the opening. The two house lights were situated 10 cm up the sides of the chamber. An Acorn computer running Arachnid software (Paul Fray, Cambridge, UK) under RISC OS collected the data.
Training procedure
The mice were trained for 5 days each week, Monday to Friday. The training procedure was based on that of Patel et al. (2006) and was modified from that of Humby et al. (1999) so as to resemble more closely the procedures in previous work carried out with the 5-CSRTT in rats (Bizarro et al. 2004). After the mice were habituated to handling, training began with four sessions of 5, 10, 15 and 15 min for the mice to habituate to the test chambers. The house lights were not illuminated at this stage. Before the next two sessions that were 15 min each in duration, 15–20 food pellets were placed in feeder trays before the start of each session. This was followed by two sessions of 15- and 20-min duration, respectively, in which food pellets were presented according to fixed-time schedules of 15 and 30 s. Nose-poke training then started with four 30-min sessions of non-spatial training with the stimulus duration set at 60 s and with an ITI of 2 s. As noted above, subgroups of mice were trained with ITI of fixed duration, whereas for other groups, individual ITI varied around the mean value (for details, see Table 1). In non-spatial training sessions, all five holes were illuminated simultaneously and a nose-poke in any hole was reinforced by delivery of a food pellet. For the first of these sessions, 25 mg food pellets (PJPPP-0020, Sandown Chemical, UK) were placed at the front of each of the nose-poke holes to encourage the mice to nose-poke.
For spatial training, the house lights were on at all times except during time-outs. Each session lasted for 30 min and there was no limit to the number of trials in a session. A stimulus was randomly presented in one of the five holes and a response in the correct hole during the stimulus duration or the limited hold (LH) period after the stimulus was reinforced and followed by a 10-s allowance for the time taken to eat the food pellet. This was sufficient for mice to consume the food and return to attending to the task; the next ITI began at the end of the 10-s allowance for eating. In sessions in which there was a time-out, this was initiated by a nose-poke response into an incorrect hole during the stimulus duration or LH. Premature responses during the ITI in experiment 1 were recorded but had no programmed consequences. Time-outs involved turning off the lights for 2 s and were followed immediately by the start of the next ITI.
Experiment 1a: initial acquisition of attentional task
Table 1 describes the stages of training, with criteria for progression to the next stage being that the mean percentage of correct responses for each group was greater than 70% and the mean response latency for each group was less than the stimulus duration for two consecutive days. The training parameters were not advanced if the mice had not been trained the day before. Acquisition was considered to be complete when the mean percentages of correct responses was stable for two consecutive weeks.
Experiment 1b: first set of nicotine tests
For testing the effects of nicotine, a reduced number of 15 wild-type and 15 α7−/− mice were used because it was known from previous studies that this was a sufficiently large group size for assessing drug effects (Humby et al. 1999; Hahn and Stolerman 2002). The selected animals were those that had all been trained at a similar time of day previously and no other selection criterion was used. Up to this stage, some mice had been trained with fixed and some with variable ITI. All selected mice were switched to fixed ITI mode before drug testing started to reduce the number of independent variables; all other parameters remained unchanged. These mice were given saline injections subcutaneously in the flank after training on each of the 3 days before the first nicotine test day to allow habituation to the procedure to take place. There were four test days on consecutive Tuesdays and Fridays. Mice were allocated to a dosing schedule for injection of saline or 0.001, 0.01 or 0.1 mg/kg nicotine 10 min before the start of each test session; each mouse was tested once at each dose by the end of the study and dosing sequences were random. Young et al. (2004) found that these doses of nicotine significantly improved the task performance of nicotine-naïve mice.
Experiment 2a: retraining after changing parameters
Seven weeks after the end of experiment 1b, retraining of the same 30 mice commenced, followed by alterations of experimental parameters to make them resemble more closely those used by Young et al. (2004). The changes entailed reducing the LH from 5 to 2 s, using variable ITI mode only, and including a 2-s time-out for anticipatory responses. House lights were extinguished during the time-outs during which no stimuli were presented, and the presentation of the next stimulus was delayed by the duration of the time-out. Although mice originally trained with fixed ITI mode were at this time trained exclusively in variable ITI mode, there were no differences in performance as a function of this variation in training history. Table 1 lists the details of all the changes in task parameters. The animals were trained under the altered parameters for 7 weeks, by which time performance was relatively stable. One α7−/− mouse developed an unusual behavioural phenotype. This mouse was markedly hyperactive in its home cage, required approximately twice as much food as the other animals to maintain its weight and made anticipatory responses at a rate 5–10 times greater than the next most impulsive mouse. No data from this mouse are presented.
Experiment 2b: second set of nicotine tests
A second experiment with nicotine began immediately after the end of experiment 2a. The mice were injected with saline for 3 days before the start of the test sessions so that they might habituate to the injection procedure. There were eight test sessions on consecutive Tuesdays and Fridays. Mice were allocated to a dosing schedule for injection of saline or 0.001, 0.003, 0.01, 0.03, 0.1, 0.3 or 1 mg/kg nicotine 10 min before the start of each test session. Each mouse was tested once at each dose by the end of the study and dosing sequences were random.
Data analyses
The six behavioural measures selected for analysis were as follows:
Accuracy (percentage of correct responses): 100 × correct responses/(correct + incorrect responses)Omissions (percentage of omission errors): 100 × omission errors/stimuli presentedLatency of correct responses: the mean time between stimulus onset and a nose-poke in the correct holeLatency of incorrect responses: the mean time between stimulus onset and a nose-poke in any incorrect holeReinforcers earned: equal to the number of correct responsesAnticipatory response rate: the mean number of responses per second during the ITI calculated by using the following formula: total number of responses in all ITI of a session/number of trials/ITI length in seconds.
The accuracy measure was not calculated when the sum of correct and incorrect responses was 15 or less. For analysis of variance (ANOVA), percentage data for accuracy and omissions were arc-sine transformed, latencies were log-transformed and anticipatory response data were subject to square root transformation, as in previous studies (Hahn et al. 2002, 2003a; Patel et al. 2006). For post hoc tests, the Bonferroni correction for multiple comparisons was applied to t tests. Graphs show untransformed data. Results for each measure were analysed by ANOVA for repeated measures using Unistat 5.0 for Excel, three-factor ANOVA was used to examine data from experiment 1a and two-factor ANOVA was used for experiments 1b, 2a and 2b. Further details of the factors are given below, at relevant places in the Results section.
Drugs
Nicotine hydrogen tartrate (Sigma, UK) was dissolved in isotonic saline with pH adjusted to 7 with NaOH solution. All injections were given s.c. in the flank in a volume of 1 ml/100 g and all doses were those of the nicotine base.
Results
Experiment 1a: initial acquisition of attentional task
There were no significant differences between any of the groups with respect to any parameter of performance at the first stage of training with the stimulus duration set at 60 s and the ITI at 2 s. Figure 1a) shows the similarity in the changes in accuracy for α7−/− and wild-type mice over these first seven sessions. It can be seen that the data show signs that the mice began to learn the task from the very earliest stage of training. Results for other variables are not shown because they were similarly negative with respect to genotype.
Fig. 1Experiment 1a: accuracy of 5-CSRTT performance for wild-type (●) and α7−/− mice (○) during the first seven sessions of training in the 5-CSRTT. Results show main effects of genotype and training day as means±SEM for groups of 23 mice. Subgroups of mice with each genotype were trained with fixed and variable inter-trial intervals without effect on performance (data not shown)
During subsequent stages of training there were progressively greater demands made upon the animals’ ability by altering task parameters, such as reducing the stimulus duration (Table 1). Differences between the genotypes then emerged, including changes in accuracy, response latencies and anticipatory responding. The data for intermediate stages of training are not shown for brevity; they largely replicated the observations made when the final set of task parameters was in operation. The performance of the animals was relatively stable during the last 10 days of training under the final parameters and these data were selected for analysis and are presented next.
Data for the final 10 days of training were examined by three-factor analysis of variance, the factors being genotype, ITI mode, and days. This analysis yielded main effects of genotype on accuracy [F(1, 42)=8.67, p<0.01], reinforcers earned [F(1, 42)=12.3, p<0.01], anticipatory response rate [F(1, 42)=4.50, p<0.05] and arguably, on correct response latency [F(1, 42)=3.96, p=0.053]. Figure 2a shows that α7−/− mice were much less accurate than wild-types, were slightly slower to make a correct response, earned fewer reinforcers and responded more frequently during the ITI. Neither the three-way (group × ITI mode × day) nor any two-way interaction was significant for any of the parameters measured. For changes across the ten successive days of training, omission errors [F(9, 378)=4.99, p<0.0001], reinforcers earned [F(9, 378)=3.28, p<0.001] and anticipatory response rate [F(9, 378)=2.11, p<0.05] showed significant effects. Most of these effects did not show consistent patterns over days (Fig. 2) and are not considered further.
Fig. 2Experiment 1a: main effects of genotype and day of training on performance of wild-type (●) and α7−/− mice (○) during the last 10 days before drug testing began (n=23). Subgroups of mice of each genotype were trained in fixed (F) and variable (V) inter-trial interval modes; inset bar graphs illustrate the significant main effects of this variable. All data shown are means±SEM (n=23)
For ITI mode, there were significant main effects for two variables, correct response latency [F(1, 42)=9.27, p<0.01] and reinforcers earned [F(1, 42)=6.25, p<0.02], with those mice trained on variable ITI mode being slower to make correct responses and obtaining fewer reinforcers. The inset bar graphs in Fig. 2c and e illustrate the significant main effects of ITI mode.
Experiment 1b: first set of nicotine tests
Data were examined with two-factor analysis of variance, the factors being genotype and drug (nicotine) dose. There were no significant effects of nicotine (0.001–0.1 mg/kg) upon any measure of performance [largest F(3, 78)=2.32, p=0.082, for correct response latency]. The main effect of genotype was significant for accuracy [F(1, 26)=4.42, p<0.05] and reinforcers earned [F(1, 26)=6.55, p<0.02]. Table 2 shows means±SEM. for both wild-type and α7−/− mice, for each attentional parameter, over the 30-min sessions. As in experiment 1a, α7−/− mice were less accurate than wild-types and earned fewer reinforcers. Genotypes did not differ significantly for other variables, although inspection of Table 2 reveals trends for omission errors, correct response latencies and anticipatory response rate that were in the same direction as the effects seen in experiment 1a. There were no significant interactions between genotype and nicotine.
Table 2Mean±SEM effects of nicotine (0.001–0.1 mg/kg) on attentional parameters in the 5-CSRTT in α7 knockout (KO) mice and their wild-type (WT) littermatesAttentional parametersNicotine (mg/kg)Accuracy (percent correct)Omission errors (percent omissions)Correct response latency (s)WTKOWTKOWTKO0.088.9±1.784.7±2.327.4±2.830.5±1.80.79±0.030.85±0.030.00189.7±1.384.2±2.725.7±2.230.3±1.70.78±0.020.84±0.040.0188.0±2.181.7±2.527.4±2.331.5±2.40.80±0.020.82±0.020.1088.8±2.083.2±1.826.1±2.529.6±2.40.81±0.020.89±0.03Incorrect response latency (s)Reinforcers earned (30 min)Anticipatory responses/sWTKOWTKOWTKO0.02.27±0.192.25±0.2289.0±2.982.7±2.50.042±0.0100.052±0.0100.0012.05±0.242.45±0.1190.8±2.582.9±3.00.038±0.0080.059±0.0100.012.32±0.162.16±0.1488.1±2.080.3±2.70.045±0.0130.061±0.0130.102.32±0.202.41±0.1590.1±2.982.6±2.30.041±0.0110.063±0.013
Experiment 2a: retraining after changing parameters
After the parameter changes listed in Table 1, animals were retrained on the task for several weeks and the relatively stable performance during the last ten sessions of training before tests with nicotine was selected for analysis. Data were examined by means of two-factor analysis of variance, the factors being genotype and day of training. The analysis yielded significant effects of genotype on omission errors [F(1, 29)=20.4, p<0.001] and reinforcers earned [F(1, 29)=19.7, p<0.001], but not on accuracy, correct or incorrect response latency or anticipatory response rate [maximum F(1, 29)=1.73]. The α7−/− mice omitted responses to a greater percentage of stimuli and earned fewer reinforcers than wild-types; these effects were clearly manifested during each of the 10 days of training (Fig. 3b,e).
Fig. 3Experiment 2a: main effects of genotype and training day on performance of wild-type (●) and α7−/− mice (○) after reduction of limited hold from 5 to 2 s and introduction of 2-s time-out for anticipatory responding (all mice trained in variable ITI mode). Results shown are for the last 10 days of training with the preceding altered parameters before the second set of tests with nicotine. Data shown are means±SEM (n=14–15)
There were also small but significant differences between correct response latencies and anticipatory responding over days. Anticipatory responding occurred at a lower rate in the later stages of training [F(9, 241)=7.07, p<0.0001]; the latency of correct responses also varied over days, but no clear pattern was evident [F(9, 241)=2.12, p<0.05]. The day of training had no effect on any other parameter. There were no significant genotype × day interactions.
Experiment 2b: second set of nicotine tests
These data were examined by two-factor analysis of variance, the factors being genotype and drug (dose of nicotine). There were no effects of genotype [F(1, 27)=2.79] or drug [F(7, 179)=1.44] on the percentage of correct responses; nor was there a significant group × genotype interaction [F(7, 179)=0.97]. For omission errors there was a significant effect of genotype [F(1, 27)= 16.7, p<0.0001] and of drug [F(7, 189)=82.4, p<0.0001] and additionally, a drug × group interaction was observed [F(7, 189)=6.40, p<0.0001]. From Fig. 4b it can be seen that the effect of genotype arises from the slightly larger overall number of omission errors made by the α7−/− mice. In view of the significant interaction, this difference was examined separately at each dose of nicotine by means of t tests with Bonferroni corrections for multiple comparisons. These analyses yielded significant differences between the genotypes at nicotine doses of 0.003, 0.01, 0.1 and 1.0 mg/kg; in all cases the percentage of omission errors was greater in the α7−/− than in the wild-type animals. Nicotine increased omission errors markedly at the largest dose tested, and this effect was greater in the α7−/− than in the wild-type mice.
Fig. 4Experiment 2b: main effects of genotype and nicotine dose on performance of wild-type (●) and α7−/− mice (○) after reduction of limited hold from 5 to 2 s and introduction of 2-s time-out for anticipatory responding (all mice trained in variable ITI mode). Data shown are means±SEM (n=14–15). Asterisks represent differences between genotypes at each dose of nicotine determined by t tests with Bonferroni correction; *p<0.05, **p<0.01)
There was no main effect of genotype on correct latency [F(1, 27)=1.47]. The main effect of drug on this measure was significant [F(7, 183)=22.4, p<0.0001] and this appeared to be entirely due to an increase in latency at the largest dose of nicotine (Fig. 4c). The interaction was not significant [F(7, 183)=1.63]. There was also a significant effect of drug on incorrect latencies [F(7, 166)=2.23, p<0.05), although its origin is not obvious from Fig. 4d and there was no dose–response relationship.
With respect to reinforcers earned, there were significant effects of genotype [F(1, 27)=20.7, p<0.0001], drug [F(7, 189)=82.0, p<0.0001] and a group × drug interaction [F(7, 189)= 8.71, p<0.0001]. The overall number of reinforcers earned was slightly greater in wild-type than in α7−/− mice, and Fig. 4e shows that this difference was significant at four of the eight dose levels of nicotine that were tested. Nicotine itself decreased the number of reinforcers at the largest dose tested and this effect was greater in α7−/− than in wild-type mice. Finally, there was a significant effect of nicotine [F(7, 189)=15.8, p<0.0001] but not genotype [F(1, 27)=1.10] on anticipatory responding. The effect of drug may be attributed to a decrease in the rate of responding at the 1.0 mg/kg dose of nicotine (Fig. 4f). A significant drug × genotype interaction was seen [F(7, 189)=2.39, p<0.05], but post hoc tests did not identify a significant difference between the genotypes at any dose level of nicotine.
Discussion
The five-choice serial reaction time task revealed impairments in the performance of the α7 nicotinic receptor knockout mice on a variety of measures, thus supporting the view that these receptors play a role in attentional performance. Analysis through variation in task parameters suggested that the primary deficit was an increase in omission errors; a general slowing in performance would have been reflected in corresponding increases in response latencies and decreases in anticipatory responding. Thus, the α7−/− mice failed to detect significantly more stimuli than the wild-types, but when they did detect a stimulus they responded to it quickly and correctly. These findings confirm and extend the key observations of Young et al. (2004) and support the emerging consensus that although α7−/− mice show deficits in the performance of several cognitive tasks, the differences from controls are modest and are manifest only with specific tasks used under precisely defined conditions (Paylor et al. 1998; Wehner et al. 2004; Fernandes et al. 2005). We now discuss in detail some differences between our results and those of Young et al. (2004) and indicate how they can be reconciled by considering parametric variations in the procedures used.
In the present study, α7−/− mice did not differ from wild types until the later stages of training, where the more stringent task parameters placed greater demands upon their cognitive ability (Fig. 2). These differences in the baseline (undrugged) performance of the α7−/− mice and wild-type mice were largely replicated in our first study on the response to nicotine (Fig. 3). The impaired accuracy and increased response latency suggested an attentional impairment, whereas the increase in anticipatory responding suggested greater impulsivity. Keller et al. (2005) also found that the amount of anticipatory responding was increased in α7−/− mice, although they used a different behavioural procedure.
There is often an inverse relationship between accuracy and impulsivity in the 5-CSRTT, and from a comparison of inbred mouse strains it was argued that a decrease in accuracy could be secondary to increased impulsivity (Patel et al. 2006). The observed change in impulsivity may have contributed to the decrease in accuracy in experiment 1a, which should, therefore, be interpreted cautiously. We did not observe slow acquisition of task performance like that seen by Young et al. (2004); a primary effect on anticipatory responding may have contributed to their finding, too, rather than a deficit in learning ability. This interpretation is supported by the absence of impairments in α7−/− mice in a variety of other learning tasks (Paylor et al. 1998) and is especially likely with the definition of accuracy used by Young et al. (2004), which included the number of anticipatory responses in the denominator for calculation of the proportion of correct responses.
During experiment 1a we did not observe any difference between α7−/− and wild-type mice with respect to omission errors. Interestingly, Young et al. (2004) reported an increase in omission errors in the α7−/− mice. The difference in durations of the limited holds (LH) used in the two studies may explain this apparent disagreement. The LH, the period in which responses are recorded as being correct or incorrect following stimulus termination, was 2 s in Young et al. (2004) and 5 s in the final stage of experiment 1a. When, with a long LH, a stimulus is not detected, ‘impulsive’ responses in the period of the LH are more likely to be recorded as incorrect than as correct responses, as there are four times more incorrect response options than there are correct options. The increased impulsivity observed in α7−/− mice may, therefore, have contributed to the observed reduction in accuracy.
At the same time, an increase in omission errors such as that reported by Young et al. (2004) may have been masked, as training with a long limited hold gives the animals more time to make an impulsive response. The trend (p<0.06) towards longer correct response latencies in α7−/− mice in experiment 1a might also be explained by late ‘correct’ impulsive responses rather than slower reactions. Both groups had short response latencies and the differences between them were eliminated when impulsive responses were punished and the LH was reduced in experiment 2a. Additional and more detailed data on the temporal and spatial patterning of errors and systematic studies of the effects of punishment and of varying LH is needed to establish the validity of this interpretation.
In an attempt to understand the origin of the differences between the present results and those of Young et al. (2004), we retrained the mice with the LH reduced from 5 to 2 s, used only variable ITI and introduced a 2-s time-out for anticipatory responses; these task parameters resembled closely those of Young et al. (2004). In experiment 2a, carried out under these altered conditions, there was a clear increase in omission errors in the α7−/− mice. In contrast, the percentage of correct responses was similar in wild-type and α7−/− animals and the previously observed differences in the latencies of correct responses and in anticipatory response rates were no longer seen. This pattern of deficits in α7−/− mice was indistinguishable from that observed by Young et al. (2004).
The absence of a difference in anticipatory responding between genotypes may have been associated with the reduction in its rate as a consequence of the time-out contingency in experiment 2a; the use of variable ITI in the retraining phase may also have been important because the initial difference between the wild-type and α7−/− mice in anticipatory responding was greater in animals trained with fixed interval than with variable interval (Fig. 2). This observation might reflect impaired utilisation of temporally predictable stimuli in the α7−/− mice, rather than differences in impulsivity per se. Nevertheless, caution is needed because the altered performance after parametric changes may also have been associated with the longer training and greater age of the animals at this stage.
In experiment 1b, the absence of any significant effect of nicotine even in wild-type mice could have been attributed to the parameters of the schedule maintaining the behaviour. Nevertheless, the doses of nicotine used were within the range reported to improve accuracy and reduce omission errors in mice (Young et al. 2004). It, therefore, seemed possible that a nicotine effect would emerge when the training parameters were adjusted to resemble those used by Young et al. (2004). However, this was not the case and in experiment 2b; no dose of nicotine improved performance on any variable. The largest dose of nicotine impaired performance; omission errors and the latencies of correct responses increased, whereas reinforcers earned and anticipatory responses decreased (Fig. 4f). This pattern of results can be attributed to a general deficit in operant response rates produced by the very large (1 mg/kg) dose of nicotine. The lack of change in accuracy is compatible with explanations in terms of motor impairment rather than an attentional deficit. The data in Fig. 4a indicate that these effects of nicotine were also present in the α7−/− mice and, in some cases, their magnitude was increased (e.g. omission errors). The reason for this unexpected observation is unclear; it may represent a little more than a minor enhancement at certain doses of nicotine of the significant main effect of genotype on omission errors.
Why was there no improvement in attentional performance by any dose of nicotine? A ceiling effect associated with the very high baseline accuracy offers only a partial explanation because the baseline rate of omission errors was high (30%), but there was still no nicotine-induced improvement on either measure. Prior exposure to nicotine might be relevant, but Young et al. (2004) obtained positive effects of nicotine at 0.003 and 0.3 mg/kg in mice previously exposed to the drug and in rats, nicotine-induced attentional enhancement did not show tolerance during 6 weeks of daily exposure to the drug (Hahn and Stolerman 2002). The parameters for training and testing during experiment 2b were also very similar to those used by Young et al. (2004). On the basis of the available information it appears that nicotine-induced improvement of performance in wild-type mice with a C57BL/6 background is not a robust phenomenon.
This aspect of the study contrasts with the good agreement of the present results with those of Young et al. (2004) with respect to the performance of α7−/− and wild-type mice in experiments 1a and 2a. Comparisons between C57BL/6 and DBA/2 mice in the 5-CSRTT have also been carried out in two different laboratories, and there was excellent agreement between the results; in each case, the DBA/2 mice were the poorer performers, exhibiting increased impulsivity and lower accuracy (Greco et al. 2005; Patel et al. 2006). DBA/2 mice had reduced levels of α7 expression and impaired sensory gating that was corrected by α7 agonists (Stevens et al. 1998).
The present work did not investigate the mechanisms underlying the impaired performance of α7−/− mice, but some suggestions can be made. Investigations of brain areas important for attentional performance using localised lesions and drug administration have demonstrated the involvement of the medial prefrontal cortex and D1 dopamine receptor activation within this area (Muir et al. 1996; Granon et al. 2000; Hahn et al. 2003b). Dopamine utilisation in the frontal cortex also appears to be reduced in individual rats that perform badly in the task (Puumala and Sirvio 1998), whereas the α7 agonist GTS-21 elevated dopamine and noradrenaline concentrations in the fronto-parietal cortex, which is implicated in attentional function (Summers et al. 1997); it is possible that a reduction or lack of α7 receptors might lead to a deficit in dopamine levels in the fronto-parietal cortex, although changes in neurotransmitter systems other than dopamine may also contribute.
It can be suggested, speculatively, that the α7−/− mouse may have some value as a model for very specific aspects of the cognitive dysfunctions in schizophrenia. Diminished frontal dopamine has been hypothesised to be important in schizophrenia (Davids et al. 2003; Laruelle et al. 2003), and post-mortem studies have shown diminished numbers of α7 receptors in brains of schizophrenics. The α7−/− mouse has not been tested for an auditory sensory gating deficit, the endophenotype of schizophrenia in which the α7 nicotinic receptor has been strongly implicated (Freedman et al. 2002) and which may offer a cognitive explanation for the attentional deficits observed.
In conclusion, this study confirms that α7−/− mice exhibit a weakened ability to detect visual stimuli in a test of sustained attention, suggesting that the presence of α7 receptors is necessary for normal task performance. Manipulating task parameters can alter the apparent nature of the deficit, suggesting a need to compare different strains of mice under more than one set of conditions to characterise them adequately. While this observation encourages further investigation of the value of α7 receptor agonists in neuropsychiatric states such as dementia (Kitagawa et al. 2003) and schizophrenia (Martin et al. 2004), the lack of effect with nicotine in the present experiments is a major concern. Identification of a protocol for assessing attention-enhancing effects of nicotine in mice may require further modifications of protocols or the use of different strains of animal.

nicotinic receptor
nicotine
knockout
mice
attention
addiction
cognition
schizophrenia
drug abuse
behavior


Impaired working speed and executive functions as frontal lobe dysfunctions in young first-degree relatives of schizophrenic patients

The aim of the investigation was to detect neuropsychological markers, such as sustained and selective attention and executive functions, which contribute to the vulnerability to schizophrenia especially in young persons. Performance was assessed in 32 siblings and children of schizophrenic patients and 32 matched controls using Wisconsin Card Sorting Test, Colour-Word-Interference-Test, Trail Making Test, and d2-Concentration-Test. The first-degree relatives showed certain impairments on all four tests, in particular, slower times on all time-limited tests. These results suggest the need for more time when completing neuropsychological tasks involving selected and focused attention, as well as cognitive flexibility, as a possible indicator of genetic vulnerability to schizophrenia.
Introduction
Schizophrenic disorder is characterized by clinical heterogeneity and certain neurobiological alterations. The world-wide constant prevalence of schizophrenia disease suggests an genetic influence. Especially high-risk-studies have shown a broad genetic component in the traits of schizophrenia [20, 23, 39, 43]. However, little is currently known about the nature of the transmission of inheritance. Furthermore, genetic predisposition may be clinically unexpressed. Thus, it is necessary to examine ill, stabilized or recovered patients as well as unaffected individuals who are at an increased genetic risk for schizophrenia disease. Cognitive processes are a directly expression of brain functions and especially their interactions. Thus, failured, prolonged, complicated or rigided ways of thinking in persons with a genetic risk reflect disturbed neuronal connections. Studying cognitive processes of unaffected first-degree relatives with a genetic risk for schizophrenia in comparison with healthy controls allows estimating the genetic impact of the disease on infomation-processing only if both groups were matched. Then they only differ in their genetic risk and we can lead back the differences to the impact of the genes. Various vulnerability models [41, 58] and certain neuropsychological indicators of attentional and cognitive abilities such as selective attention, cognitive flexibility, inhibitory control and naming skills were examined in detail among schizophrenic patients and risk persons [11, 14, 40]. Therefore, the idea of cognitive endophenotypes is an important approach in the research of genetic risk of schizophrenia since years [24, 52]. In research the Stroop Test (FWIT), the Trail Making Test (TMT), and the Wisconsin Card Sorting Test (WCST), have been used especially to assess executive or higher-order functions such as working memory, cognitive adjustment and maintenance of set. However, only few studies have applied the d2-Concentration-Test (d2) or the above-mentioned tests to a young population with a genetic risk.
Some studies have determined lower WCST-scores among schizophrenic patients. These have achieved a lower percentage of correct trials, fewer completed categories, and have made more preservative errors [16, 47]. Egan et al. [10] also determined impairments using the WCST and other tests among 193 unaffected siblings and even worse scores on these and other tests among 147 of their relatives who were patients. Further studies [12, 37, 38, 47, 48, 56] also indicate a genetic component regarding impairments among relatives of schizophrenic patients as compared to relatives of other patients with affective disorders and as compared to normal controls. Stratta et al. [53] showed lower WCST-scores among 92 schizophrenic patients, but not among 25 first-degree relatives. Other studies were also not able to detect any change in WCST-parameters among schizotypic persons or subjects with a genetic risk [28, 36]. Laurent et al. [33] showed deficits only in subgroups of relatives with higher rates of physical anhedonia and negative schizotypal symptoms. Therefore, the authors suggest that performance on the WCST is more likely a feature inherent to the disease process rather than an index of genetic susceptibility to the illness. WCST-deficits in schizotypic individuals [18, 42] support the hypothesis that executive function deficits may precede the onset of schizophrenia or indicate cognitive impairments in schizophrenic-spectrum syndromes.
Performance on the Stroop was also analyzed in detail among schizophrenic patients using various test versions. Impairments could be found repeatedly in patient groups. Nevertheless, some studies were not able to replicate these findings [33, 47]. In a review of 32 studies [19], inconsistent findings and the contribution of thought disorders and special components of the attention process to the Stroop-effect were emphasized. In genetic risk studies there are contradictory findings of poor performance among children, siblings and parents of schizophrenics [4, 9, 33, 47].
Impairments on the TMT, especially in the second part, could also often be determined in schizophrenic patients [33, 47, 57]. However, only a few studies were able to demonstrate lower scores among those at genetic risk [10, 33].
Some investigations showed impaired performance among schizophrenic patients or relatives on all of these tests: the Stroop, the TMT, and the WCST [13, 21, 27]. Only the investigations by Schreiber et al. [49] are known to have used the d2-Concentration-Test on schizophrenic patients and risk groups. On this test, patients, but not unaffected risks, performed more poorly (working speed and error rate) than did the controls. Furthermore, other tests have also shown reduced psychomotor speed and reaction time among schizophrenics [2, 49].
The findings described above suggest a subtle deficit in maintained and focused attention together with a subsequent impairment of stimulus discrimination among schizophrenics and schizotypical subjects. Due to these results, we assume these impairments principally to be an expression of the disease process, personality traits or symptomatology, such as schizoid, paranoid, extraversion, or anhedonia. Among subjects with a genetic risk for schizophrenia, cognitive dysfunctions have also been repeatedly determined [5, 32]. However, scores are inconsistent and display different impairment profiles [54]. Thus, it may be concluded that subgroups with various neurobiological and genetic profiles as well as certain symptomatology during the process of the disease can be distinguished and the vulnerable cognitive domains revealed.
The aim of this study (as part of an ongoing project called “Neurobiological and neuropsychological risk factors for schizophrenia”; [30]) is to consider the special cognitive abilities of working speed, inhibitory control, cognitive flexibility and executive functions, such as working memory, maintenance of set and naming skills using various tests among juvenile unaffected first-degree relatives of schizophrenic patients as compared to controls. Our hypothesis is that the genetic risk group of unaffected children and siblings of schizophrenics will score lower on the tests than will healthy controls. Based on frequently used tests and the results presented above, we selected the WCST, the TMT and the FWIT (Farbe-Wort-Interferenz-Test) as the German Version of the Stroop-Paradigm. A number of important studies [5, 11, 14, 32] refer to these test when assessing cognitive functioning and neuropsychological risk indicators in schizophrenic patients and persons at high risk, because these tests make demands on cognitive processes based in the frontal lobes, a brain area vulnerable to potential antecendents of schizophrenic disorder. All of these tests are supposed to have high effect sizes for test variation with a medium level of difficulty. Additionally, we used the d2-Test because it, in particular, makes demands on concentration and discrimination between similar stimuli under high time pressure in routine tasks.
Methods
Subjects
A total of 32 children and siblings of schizophrenic patients without any clinical manifestation of schizophrenic symptoms (mean age in years: 16.0; males/females: 13/19) and 32 controls (mean age in years: 16.2; males/females: 13/19)—carefully matched for age (range from 12 to 21 years), sex and educational level—were examined. Additionally, in order to screen for the performance comparability of groups with respect to personal background data, the intellectual quotient was assessed using the Standard Progressive Matrices (SPM; [26] according to [37]). We assessed the educational level of probands and controls and also of parents of probands and controls as an indicator of their socio-economic status. Both groups did not differ significantly (Table 1). Demographic characteristics are summarized in Table 2. The high-risk subjects were drawn from a sample of schizophrenic in and outpatients of the Department of Psychiatry and Child- and Adolescent Psychiatry of the University of Jena and of two additional departments of psychiatry in the county of Jena. First-degree relatives of in and outpatients were asked for participation. The controls were recruited from all school types within the German School System. We also placed an ad in the local newspaper. People were asked to participate in a high-risk study conducted by the University of Jena as part of a neuropsychological study. Since recruitment was difficult we included both children and siblings of schizophrenic patients. Although siblings are a little more likely to be taken ill at schizophrenia, the genetic risk on average is nearly the same for children and siblings [51]. We made up only one group for relatives, because the risk of schizophrenia is almost equal for both. The study was approved by the ethical commission of the University of Jena. All tests took place in the Department of Child and Adolescent Psychiatry in Jena. After a full explanation of the procedures to the subjects, written informed consent was obtained from the young persons and their parents.
Table 1Education level of probands and controlsProbands with genetic riskControlsSchool typeLerning-Disabled School 11Secondary School1514Grammer School/Advanced  Technical College/University10/313/2Technical College/Vocational School32Education level of the parents (always the highest qualification)Secondary School31Technical College/Vocational School1717Advanced Technical College/University1214Groups did not differ significantly Table 2Demographic characteristics of probands Demographic dataSubjectsProbands with genetic riskControlsAge (years)16.0 ± 2.416.2 ± 2.0Gender (male/female)13/1913/19Intelligence (SPM-IQ) Non verbal IQ101.6 ± 13.8100.5 ± 9.7Data of age and intelligence as mean values ± standard deviationGroups did not differ significantly
Diagnostic screening and evaluation
Psychiatric status of both groups was determined according to the Schedule for Affective Disorders and Schizophrenia (SADS) or the Kiddie-SADS [8] by a board-certified psychiatrist or psychologist. First- or second-degree relatives of controls showing any schizophrenic symptoms were excluded, as were all probands suffering from internal or neurological disorders, schizotypical personality, affective or eating disorders, or diseases with a possible neurobiological basis, such as attentional deficit disorder or dyslexia. One subject in each group had an adjustment disorder, one had a minor depressive episode, two had social behavioral disorders without any symptoms of attentional deficit disorder, and one an enuresis. First-degree relatives showed no scores on SADS or were below threshold for diagnostic relevance. Inclusion criteria were an IQ of 70 or above according to SPM [31, 44] and an age between 12 and 21 years.
Neuropsychological evaluation
We used the (WCST; [22]) in order to examine the probands’ executive functions such as working memory and maintenance of set as well as cognitive flexibility. The probands were asked to successively place 128 response cards, showing symbols drawn in different combinations of color, form, and number, under one of four stimulus cards. After ten consecutive correct trials, the criteria according to which the response cards were selected changed. The examiner provided information only on correct or incorrect choices and the probands had to find out by themselves according to what criteria the response cards were to be placed. We evaluated the percentage of correct trials (WCST/PCT), the percentage of perseverative errors (WCST/PPE), and the number of completed categories (WCST/NC).
The FWIT [1] as a version of the Stroop-Test was used to determine general alertness, naming skills and selectivity. At first the probands were asked to read aloud a list of color names printed in black, second to name the color of strokes, and third to name the color of different colored words. This run was repeated two times and the time required for reading was assessed. Finally, we calculated time needed (FWIT/A), naming skills (FWIT/N), selectivity of relevant information (FWIT/S), and number of errors on the interference task (FWIT/E).
The (TMT; [46]) provides information about psychomotoric speed, cognitive flexibility and working memory. Part A of the TMT requires the subjects to connect numbers in rising sequence. For Part B of the TMT, a line is drawn to connect alternating numbers and letters, starting with the number one, and then to the letter A, also in rising sequence. We determined time required for TMT/A and TMT/B as an indicator for working speed as well as cognitive flexibility using a ratio of TMT/A and TMT/B.
The d2-Concentration-Test (d2; [3]) provides information about the ability to discriminate between similar stimuli, measured by speed and accuracy of performance. The probands have to mark all “d”s that have two strokes within an array of 14 lines, with 20 s allowed per line. The probands must also discriminate between similar stimuli, because there are “d”s with one or more than two strokes, and “p”s with strokes. We counted the number of all scanned characters measuring the working speed (d2/GZ as the German expression for number of scanned characters). Furthermore, the ratio of incorrectly marked characters (d2/F as the German expression for error rate) to all scanned characters (d2/GZ) as an indicator of accuracy was assessed.
Results
The primary aim of the data analysis was to evaluate group differences for the parameters described above and to examine connections between the variables. Therefore, we tested our hypothesis that the genetic risk group of unaffected children and siblings of schizophrenics would perform more poorly on the tests than would healthy controls. Furthermore, we assumed that relatives of schizophrenics would show a wider pattern of impairments than would healthy controls. All statistical analysis were done using SPSS software (version 10.1).
The multivariate analysis of variance (MANOVA) showed significant differences between the group of children and siblings of schizophrenic patients and the controls for certain variables of performance, as well as a connection to age, intelligence and gender. In order to determine the normal distribution of parametric statistics for each variable, the score distributions of each group were examined using the Kolmogorov–Smirnov-test. Homogeneity of variance of the groups was tested using the Levene Test. If there was no normal distribution, as was the case for the variables WCST/NC and TMT/F, then the non-parametric Mann–Whitney-U-test was used to evaluate group differences. Otherwise comparisons were made using the t-test. Mean scores (±standard deviation) are shown in Table 3. Correlations were determined using the non-parametric Spearman–Rank-test, with selected values shown in Table 4. Inhomogeneous variances arose in the variables WCST/NC, WCST/PCT, and FWIT/F. The level of significance was fixed at 0.05 for all tests.
Table 3Comparison of neuropsychological data between high risk and control probandsNeuropsychological variableSubjectsComparisonsRisk probands (n = 32)Controls (n = 32)p-valuesWCSTPCT, Percentage of correct trials77.7 ± 10.584.0 ± 6.10.002+PPE, Percentage of perseverative errors9.8 ± 3.57.9 ± 2.80.010NC, Number of complete categories5.7 ± 0.86.0 ± 0.20.042*FWITA, Working speed 47.6 ± 8.052.2 ± 6.30.012N, Naming skill49.5 ± 7.049.0 ± 8.3nsS, Selectivity skill54.7 ± 7.155.5 ± 8.4nsE, Number of errors during the interference task14.9 ± 6.39.8 ± 5.10.000*TMTA, Time to execute TMT part A35.3 ± 10.627.8 ± 9.90.002B, Time to execute TMT part B83.0 ± 32.663.0 ± 18.60.002E, Number of errors0.47 ± 1.020.25 ± 0.57ns*A/B, Ratio of times of part A to B0.462 ± 0.1670.452 ± 0.121nsd2GZ, Number of treated signs396.7 ± 97.7448.9 ± 92.10.016FGZ, Rate of errors to number of treated signs3.6 ± 2.13.1 ± 2.9nsns, Non-siginficant differencesStatistical analysis by t-test, not Bonferoni-corrected*Non-parametric test (Mann–Whitney-U-test)+p-values for inhomogeneous variances Table 4Selected correlations between the test variables for all probands (n = 64), shown as rho of Spearman-rank correlationWCSTFWITTMTD2PCTErrorsAction AGZWCSTPCT, Percentage of correct trials−0.354**0.316*−0.357**−0.387**FWITErrors−0.354**−0.471**0.347**−0.331**Action, Speed of a general alertness0.316*−0.471**−0.416**0.627**TMTA, Time to execute TMT part A as an inverse indicator of working speed−0.357**0.347**−0.416**−0.463**d2GZ, Number of treated signs as an indicator of working speed0.387**−0.331**0.627**−0.463***Correlation two-sited significant at the level of 0.05 **Correlation two-sited significant at the level of 0.01
Group differences
Compared to the controls, first-degree relatives of schizophrenic patients were significantly impaired in their performance on certain parts of all tests. For the WCST variables “percentage of correct trials” (PCT), “percentage of perseverative errors” (PPE), and “number of complete categories” (NCC), the analysis showed significantly worse results in the index group. The second finding of the three tests with time-limited demands, the FWIT, TMT, and d2-test, was that subjects with a genetic risk for schizophrenia worked at a significant slower rate as compared to the controls. Even though the variance of the significant results is greater in the high-risk group, these differences are not caused by extreme values. Statistical differences between the groups could not be detected for error rate on the TMT or on the d2, for cognitive control and flexibility or for naming skills on the FWIT (FWIT/N). However, first-degree relatives of schizophrenic patients produced significantly more mistakes on the interference task of the FWIT as compared to their controls.
Effects of age, intelligence and gender
These effects were taken into account in the analysis. As generally known, intelligence and speed on the FWIT, TMT, and d2, correct categorization on the WCST, as well as naming skills, improve with age for all probands. Intelligence correlated significantly with all measured performance except for selectivity and naming on the FWIT and for accuracy on the d2. With regard to gender effects (for a review, see [17, 55]) for all probands, females showed a significantly higher cognitive flexibility on the TMT/B. Among all probands and within the risk group, males achieved significantly higher accuracy when demands were placed on cognitive adjustment during the interference task of the FWIT. They scored significantly lower on naming skills than did the females. No gender effects were found in the control group.
Relationships between variables
The correlation between working speed and all time-limited tests (FWIT, TMT, d2) was highly significant, as was the correlation between speed and the PCT during the WCST. There is a positive connection between the accuracy of the d2 and the TMT. However, only the accuracy of the interference task on the FWIT correlated to parts of the WCST scores and the working speeds on the FWIT, TMT, and d2. While speed on the TMT is lower in the index group, there was no difference between the two groups regarding the ratio of the times required to complete the TMT/A and the TMT/B. Variable naming correlated only with general alertness on the FWIT. And selectivity on the FWIT did not correlate with any other performance variable.
Additionally, we did some factor analyses to see if different cognitive test variables could reflect the same cognitive function or a general vulnerability for schizophrenia. Surprisingly, after orthogonal rotation we found three factors explaining almost 60% of variance. But the other 4 of 7 components had “Eigenwerte” between 0.55 and 0.97, so that we found almost as many factors as variables. After graphic representation, we are very cautious with an interpretation of the results of this factor analyses.
Discussion
This study examined the hypothesis that children and siblings of patients with schizophrenia, without a schizophrenic-spectrum disorder, show a pattern of decreased attentional and cognitive performance when compared to controls. Several findings of this study can be emphasized.
First, subjects at risk for schizophrenia were slower on all three time-limited tests; the FWIT, TMT, and d2. These results suggest that young persons with a genetic risk need much more time to achieve the same accuracy as the controls during routine work on the d2 and the TMT/A. The correlations among working speeds for all time-limited tests indicate that these tests assess the same components of an attentional process and that this may reflect a general vulnerability. On the other hand, on tasks with no time structure or constraints high risks showed a lower ability to establish, maintain or adjust a required conceptual level during the WCST. This finding suggests impaired executive functions, working memory, vigilance and cognitive flexibility, which cannot be compensated by using more time, as compared to the controls. Furthermore, it is fair to assume that parameters of the prolonged and more complex tasks of the WCST measure different cognitive processes than do those of the time-limited tests. Nevertheless, there are correlations between WCST-scores and other scores, especially regarding working speed.
Second, gender effects are greater within the risk group. In this group, females showed a worse selectivity, but only by making more mistakes. In contrast, females performed significantly better than males on naming skills on the FWIT. These findings support the assumption of gender-specific vulnerability markers, in particular that males with a genetic risk show vulnerability in respect to naming skills [50].
Third, our findings regarding cognitive flexibility and naming skills on the FWIT are not reflected in the working speed for all time-limited tasks or in the intelligence of the probands. There is no correlation between these variables. However, there are significant correlations between the accuracy of the interference task on the FWIT with both the working speed for all time-limited tasks, as well as with WCST performance. Thus, both groups needed the same time to perform the interference task on the FWIT, but the high-risk probands made more mistakes. This is an indicator of impaired selectivity in distinguishing relevant from irrelevant stimuli. The results of the TMT test can be attributed to an impaired action speed but not to a lower ability of cognitive adjustment. Thus, the results do not allow a clear comparison of selectivity and naming performance in both groups. Probably the FWIT subtests, with their demands on naming and cognitive adjustment, reflect other aspects of the attentional process as compared to other parts of the tests used.
These results in first-degree relatives of schizophrenic patients without schizotypical or affective symptoms only partly confirm prior studies. Some studies show no differences on the Stroop and the WCST [28, 33]. Only the scores on the WCST variables, “completed categories,” “percentage of perceptual level response,” and “perseverative errors,” are repeatedly lower among the genetic risks as compared to the healthy controls, especially among high-risk probands affected by symptoms such as anhedonia or those that are schizotypical [33, 56]. TMT/B impairments could also be detected in groups with a genetic risk for schizophrenia [10, 33] suggesting a lower cognitive flexibility, again, particularly for subjects affected by schizoid or paranoid symptoms, extraversion or anhedonia [15, 34]. In contrast, we found lower working speeds on the TMT/A in the risk group, but not decreased selectivity on the ratios of the TMT subtests. However, we were able to confirm lower scores on the WCST among the risks without these special symptoms.
Some methodical issues must be discussed with respect to these findings. The sample size and power, the survey, the type of relatives and of control subjects may also account for the differences and the different results as compared to other studies. One factor relates to the motivation of the probands. Since they know themselves to be members of the index group it is possible that siblings or children of patients lack confidence in their own cognitive abilities and are, thus, less motivated than those in the control group. Nevertheless, abstract-logical intelligence is equal in both groups, although these scores are not influenced by time constraints. No ceiling or floor effects could be found so it may be assumed that all tests were sufficiently difficult. Furthermore, it is especially important to note that schizophrenia is etiologically heterogeneous. The higher standard deviation on almost all test variables relates to an inhomogeneous feature in the index group. Although subjects with a clinical diagnosis of a schizotypical personality disorder (SPD), affective or personality disorder were excluded, links to personality traits or other symptoms as described for our probands could also account for the lower scores in the risk group. We examined relatively small sample sizes. Therefore, an analysis of subgroups would not have been effective. In particular, a possible impact of the SPD dimensions could affect cognitive abilities, as suggested by the available studies of schizotypical persons. One could examine such effect by including a third group, namely patients with schizophrenic disorder. We took in consideration to include also patients into our statistical analysis in order to look for a continuum in behavioral symptoms and cognitive performances. In our case, the patients group was too heterogenous. We did not succeed in matching three parallel groups of first-degree relatives, healthy controls and patients. Therefore, our focus lies on cognitive impairments of relatives of schizophrenic patients. Especially the recruitment of adolescent patients and much more of their relatives is a fussy issue in the research of neuropsychological indicators of cognitive abilities. Nevertheless, there is a need for further neuropsychological research to evaluate the impact of subtypes of schizophrenia on cognitive abilities. The discussion concerning the variability of risk groups is also reflected in our results with respect to the contrast of trait markers versus prodromali, the sensitivity of test versions and the specificity to schizophrenic disease [6, 7, 25, 29]. Furthermore, comparisons between the patients and their symptomatology during the course of the disease, and between subjects with genetic or symptomatical risks and controls may provide more insight into possible traits signaling vulnerability to schizophrenia. It is important to identify risk factors for schizophrenia as early as possible because of the poor prognoses for early-onset schizophrenia [35, 45]. The design of the present study did not allow the analysis of such a complex issue. Effects are currently being evaluated in order to determine patterns of cognitive vulnerability of risk subgroups. However, the results from our ongoing project did confirm a link to genetic influences, because both groups were matched for age, gender and intelligence. So the differences between probands and healthy controls are due to their relationship with schizophrenic patients, respectively to the genetic vulnerability of first-degree relatives. Our results demonstrate that attentional impairments as shown by the data of the FWIT, TMT, and d2 may be a trait indicator of the disease and so, too, may deficits of cognitive adjustment and executive functions as determined by the WCST. We were able to show that starting at a young age persons at risk work more slowly in general and not only on specific interference tasks. This suggests a more easily distracted working memory even among these young subjects. Therefore, our findings support the need for future studies based on the same and similar multiple tasks in order to gather more evidence in the search to identify subgroups of vulnerable subjects.

attention
schizophrenia
genetic vulnerability
cognitive functions