Exp Brain Res DOI 10.1007/s00221-011-2888-4
RESEARCH ARTICLE
Predictive smooth eye pursuit in a population of young men: II. Effects of schizotypy, anxiety and depression Emmanouil Kattoulas • Ioannis Evdokimidis • Nicholas C. Stefanis • Dimitrios Avramopoulos Costas N. Stefanis • Nikolaos Smyrnis
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Received: 13 April 2011 / Accepted: 23 September 2011 Ó Springer-Verlag 2011
Abstract Smooth pursuit eye movement dysfunction is considered to be a valid schizophrenia endophenotype. Recent studies have tried to refine the phenotype in order to identify the specific neurophysiological deficits associated with schizophrenia. We used a variation of the smooth eye pursuit paradigm, during which the moving target is occluded for a short period of time and subjects are asked to continue tracking. This is designed to isolate the predictive processes that drive the extraretinal signal, a process previously reported to be defective in schizophrenia patients as well as their healthy relatives. In the current study, we investigated the relationship between predictive pursuit performance indices and age, education, non-verbal IQ, schizotypy and state anxiety, depression in 795 young Greek military conscripts. State anxiety was related to better predictive pursuit performance (increase in residual pursuit gain), while disorganized schizotypy was related to deficient predictive pursuit performance (decreased residual gain).
E. Kattoulas I. Evdokimidis N. Smyrnis Cognition and Action Group, Neurology Department, Medical School, Aeginition Hospital, National and Kapodistrian University of Athens, Athens, Greece E. Kattoulas N. C. Stefanis C. N. Stefanis N. Smyrnis University Mental Health Research Institute, National and Kapodistrian University of Athens, Athens, Greece E. Kattoulas N. C. Stefanis N. Smyrnis (&) Psychiatry Department, Medical School, Aeginition Hospital, National and Kapodistrian University of Athens, 72 V. Sofias Ave., 11528 Athens, Greece e-mail:
[email protected] D. Avramopoulos McKusick–Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
This effect was independent of the effect of disorganized schizotypy on other oculomotor functions supporting the hypothesis that predictive pursuit might be specifically affected in schizophrenia spectrum disorders and could be considered as a distinct oculomotor endophenotype. Keywords Schizophrenia Endophenotype Psychometric Oculomotor Mask pursuit
Introduction More than a century ago, Diefendorf and Dodge (1908) observed that patients with ‘‘dementia praecox’’ had difficulty following an oscillating pendulum with their eyes. Since then, impairment in the performance of smooth eye pursuit movements has been consistently reported for schizophrenic patients and their first-degree relatives (Holzman et al. 1973, 1974; Holzman and Levy 1977; Levy et al. 1993; O’Driscoll and Callahan 2008). Holzman and Matthysse (1990) proposed that smooth eye pursuit could serve as a biological marker for detecting genetic susceptibility to schizophrenic spectrum disorders. The idea behind the approach of biological markers was first stated by Gottesman and Shields (1973). According to this, dysfunction in specific neurophysiological markers (termed as endophenotypes) marks a specific neuronal deficit, which might be closer to the genetic load than the more complex clinical syndrome. According to Gottesman and Gould (2003), a valid endophenotype should be associated with the illness in the population, should be heritable, stateindependent and cosegregate within affected families, and must be present at a higher rate in unaffected family members of the proband compared with the general population.
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Previous studies strongly support the notion that deficits in different eye movement tasks (Calkins et al. 2008; Calkins and Iacono 2000; Braff et al. 2007) and specifically smooth eye pursuit can be considered as valid schizophrenia endophenotypes. A large number of studies have examined smooth pursuit eye movement function in the relatives of schizophrenia patients. The vast majority of studies confirmed the dysfunction in a proportion of the first-degree relatives of schizophrenia probands (Amador et al. 1995; Ettinger et al. 2004; Karoumi et al. 2001 for a review Thaker 2008). As described in the companion paper (Kattoulas et al. 2011), smooth eye pursuit is subserved by a wide and complex neuronal network of cortical and subcortical areas including the frontal eye fields, supplementary eye fields, intraparietal sulcus, precuneus, extrastriate areas (medial temporal cortex and medial superior temporal cortex) and the cingulate cortex (Berman et al. 1999; O’Driscoll et al. 2000; Ilg and Thier 2008; Sharpe 2008). This network combines according to current theories both retinal and extraretinal input to drive eye movement commands (Lencer and Trillenberg 2008). Recent studies have tried to refine the phenotype in order to identify a more specific physiological deficit(s) associated with schizophrenia. For example, studies have looked into the characteristics of initiation pursuit, a more sensitive measure of pursuit performance (Lisberger and Westbrook 1985; Clementz and McDowell 1994), since it does not yet incorporate an extraretinal signal for eye movement. Thus, it is considered to be a measure of the retinal signal used to drive the eye movements. On the other hand, studies trying to evaluate specifically the extraretinal signal driving eye movement have used a different pursuit paradigm, the predictive pursuit, where the target is briefly extinguished during pursuit (Becker and Fuchs 1985; Thaker et al. 1998). Although many studies have looked into the classic, closed-loop pursuit performance in patient groups, family members or healthy control groups, few have investigated performance in the predictive pursuit paradigm. Thaker et al. have specifically investigated the extraretinal component of smooth pursuit in different groups (Thaker et al. 1998, 1999; Thaker 2008), using different measures of pursuit performance. The results of these studies support the idea that a specific deficit in predictive pursuit is evident in schizophrenia patients, even when no deficit is apparent in the classic closed-loop pursuit (Thaker et al. 1999), as well as their first-degree relatives, especially those with schizophrenia spectrum personalities (Thaker et al. 1998). In a recent fMRI study (Hong et al. 2005), imaging data from the predictive pursuit paradigm were collected from psychotic patients and healthy subjects who had matched performance in the closed-loop pursuit parading (similar pursuit gain). The results provided functional-anatomical evidence
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supporting reduced function in the extraretinal motion processing brain circuits and increased dependence on immediate retinal motion information for schizophrenia patients. This retinal input seems to be used in order to compensate for reduced extraretinal signaling during sustained visual tracking. Thus, a specific dysfunction in the extraretinal motion processing might be responsible for previously reported deficits in the closed-loop gain of schizophrenia patients (Thaker et al. 2003). The predictive pursuit paradigm was administered as a part of the smooth pursuit task in the Athens Study of Psychosis Proneness and Incidence of Schizophrenia cohort (ASPIS, Smyrnis et al. 2003, 2004). In our previous report (Kattoulas et al. 2011), we presented results concerning the characteristics of the predictive smooth pursuit performance in this sample, as well as their relationship with the performance in other oculomotor and cognitive tasks. The current report examines the relationship between psychometric measures of schizotypy as well as anxiety and depression (introduced as state-dependent confounding factors) in this sample and predictive pursuit performance. We further address the question whether in the large sample of apparently healthy individuals, there is one or more small groups that exhibit a combination of neurophysiological (predictive pursuit) and psychological (high schizotypy) phenotypic expressions of high risk for the development of schizophrenia. Previous studies have shown that healthy individuals displaying high schizotypy characteristics also display deviant pursuit performance (Holahan and O’Driscoll 2005; Siever et al. 1994), but none of them have used the predictive pursuit paradigm.
Methods Participants and materials In our first study on predictive pursuit (Kattoulas et al. 2011), we have already presented the demographic characteristics of the ASPIS sample that were included. All participants were young Greek men (aged 18–25) recruited from the Greek Air Force and were apparently healthy. They all had an initial medical assessment by a team of medical doctors of all specialties (military personnel), including neurological and psychiatric evaluation and evaluation of substance abuse, before entering military service. A subtotal of 1,388 individuals of the total 2,075 participants in the ASPIS sample performed the predictive pursuit task. From those subjects, 201 (14.5% of the 1,388) did not comply with the criteria for successful performance of the task (see Kattoulas et al. 2011), so the analyses of task performance were performed on the remaining sample of 1,187 subjects.
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We administered to a randomly selected subset of 1,657 of the ASPIS sample (80% of participants), a battery of self-rated questionnaires assessing among other variables (a) schizotypy with the perceptual aberration scale (PAS, Chapman et al. 1978) and the schizotypal personality questionnaire (SPQ, Raine 1991); (b) current psychopathology with the Symptom Checklist 90 revised scale (SCL90-R, Derogatis et al. 1974) translated and standardized in the Greek population (Ntonias et al. 1990). Confirmatory factor analysis revealed that a four-dimensional model of SPQ best fitted the observed data (Stefanis et al. 2004). The first factor loaded on the subscales of odd beliefs and odd perceptual experiences (F cognitive-perceptual, cog-per). The second factor loaded on suspiciousness, social anxiety, no close friends and constricted affect (F negative). The third factor loaded on odd behavior and odd speech (F disorganization), and the fourth factor loaded on social anxiety, suspiciousness and ideas of reference (F paranoid). Four validity items were used to recognize individuals that responded at random. These items were of the type ‘‘please answer this question by ticking box 4.’’ We excluded 296 subjects (20.8% of the 1,657) that responded incorrectly in at least one of these items from all analyses including the use of psychometric evaluations. The analyses presented in the current study testing the relation of predictive pursuit to anxiety, depression and schizotypy included the intersection of the two sample sets, namely of the valid responders for the psychometric questionnaires and of the 1,187 subjects that successfully performed the predictive pursuit task. This final sample included 795 subjects. Apparatus-procedure A detailed description of the apparatus for eye movement measurements and the experimental paradigm used is given in our previous reports (Smyrnis et al. 2003; Kattoulas et al. 2011). The quantitative assessment of the mask smooth pursuit is also described in our previous report (Kattoulas et al. 2011). Three indices of performance in the predictive pursuit task were extracted for each subject. Latency was used as a measure of the time until an initial decrease in pursuit speed was observed. Deceleration time was used as a measure of the time from the end of the latency period until the residual speed is achieved and stabilized. Finally, residual gain was estimated as the ratio of final eye velocity to initial eye velocity. Statistical analysis We used two approaches to analyze the relation of psychometric scores to predictive pursuit performance.
The first approach was to test the effects of psychometric score variables on pursuit performance globally within the total sample without grouping into subgroups with specific characteristics. For this purpose, we used the multiple regression model to test for the effects of the psychometric variables (schizotypy, anxiety and depression) on each one of the three pursuit performance indices (latency, deceleration time and residual gain). In each multiple regression analysis, we used the psychometric variables (schizotypy, anxiety and depression) as independent variables and each one of the three predictive pursuit performance indices as the dependent variable. The second approach was based on the definition of specific subgroups within the total sample. In this group analysis, we used the criterion of 2 standard deviations (2 SD) to derive groups with a very high schizotypy score. Note that these groups were not mutually exclusive. We thus derived a high PAS score group (N = 46 subjects, PAS score [18), a high SPQ score group (N = 19 subjects, SPQ score [53) and four groups of individuals with similarly defined high scores in each of the four factors of SPQ (cognitive-perceptual group N = 34 subjects; negative group N = 32 subjects; disorganization group N = 17 subjects; paranoid group N = 15 subjects). The high PAS and high SPQ groups were partially overlapping, that is 9 individuals belonged to both groups. Two more groups of high anxiety (N = 37 subjects) and high depression (N = 40 subjects) were derived from SCL90-R scores on anxiety and depression. Again, these groups were partially overlapping, that is 18 individuals belonged to both groups. A t test with dependent variables, the three indices of predictive pursuit performance, and grouping variable, the two groups (high score group vs. the remaining sample), was used to assess possible group differences on each one of the pursuit indices of performance. Initially, Levene’s test was used for each t test, in order to assess for homogeneity of data variance between the two groups. All statistical analyses were performed using the STATISTICA 7.0 software (STATSOFT Inc. 1984–2001).
Results Descriptive statistics Table 1 presents the mean latency, deceleration time and residual gain for all subjects that were included in this sample. It also presents the descriptive statistics for the PAS score, SPQ total score and SCL90-R anxiety and depression scores.
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Exp Brain Res Table 1 Descriptive statistics of predictive pursuit and psychometric indices N
Mean (SD)
795
165.36 (40.35)
Deceleration time (ms)
795
109.69 (51.28)
Residual gain
795
0.37 (0.22)
SCL90-R anxiety
788
8.32 (6.57)
SCL90-R depression
788
14.32 (8.51)
PAS score
763
7.63 (5.32)
SPQ total score SPQ factorcognitive-perceptual
763 762
27.63 (12.21) 0.20 (0.11)
SPQ factornegative
762
0.19 (0.12)
SPQ factordisorganized
762
0.42 (0.20)
SPQ factorparanoia
762
0.45 (0.18)
Latency (ms)
whether the association between disorganized schizotypy and residual gain was independent of the effects of closedloop pursuit or active fixation. We thus performed another multiple regression analysis with residual gain as the dependent variable. As independent variables, we used this time the psychometric indices (anxiety, depression, PAS score and the four SPQ factors’ scores) as well as two indices of closed-loop pursuit performance (pursuit gain in the 30°/s and saccade frequency in the 10°/s condition) and saccade frequency in the fixation task with the presence of distracting targets. Once again, the regression was significant (F10,617 = 3.98, P \ 10-4). As can be seen in Table 2, residual gain was correlated with all oculomotor indices of performance as expected from our previous analysis. More importantly, the correlation between disorganized schizotypy and residual gain remained significant in this analysis.
Effect of psychometric characteristics on predictive smooth pursuit performance
Difference in predictive pursuit performance in high psychometric score groups
Multiple regression was used to test the relationship between anxiety, depression, PAS total score and SPQ total score (independent predictor variables) and each one of the three predictive smooth pursuit indices. There was a significant correlation between psychometric variables and residual gain (F4,747 = 3.14, P \ 0.05). Residual gain increased significantly with increasing levels of anxiety (b = 0.14, P \ 0.05). We did not observe significant correlations between any of the other psychometric variables and residual gain. Finally, no significant correlations between psychometric variables and predictive pursuit latency (F4,747 = 0.53, P [ 0.05) or deceleration time (F4,747 = 0.29, P [ 0.05) were observed. Another regression analysis was performed by substituting the total SPQ score with the scores for the four SPQ factors (see ‘‘Methods’’). Once more, a significant correlation between psychometric variables and residual gain (F7,743 = 2.24, P \ 0.05) was observed. Residual gain increased significantly with increasing levels of anxiety (b = 0.14, P \ 0.05) and decreased with increasing levels of disorganized schizotypy (factor 3 of SPQ: b = -0.20, P \ 0.05). None of the other psychometric variables were associated with residual gain, and finally, once more, none of the psychometric variables were associated with predictive pursuit latency (F7,743 = 0.47, P [ 0.05) or deceleration time (F7,743 = 0.56, P [ 0.05). In part I of this report (Kattoulas et al. 2011), we observed that residual gain was correlated with some indices of performance in other oculomotor tasks, namely closed-loop pursuit and fixation with the presence of distracting targets. Since we have previously observed that these specific tasks were also influenced by disorganized schizotypy (Smyrnis et al. 2004, 2007), we investigated
In this analysis, we used a set of t tests to compare the performance indices of predictive pursuit (repeated measures–dependent variable) of groups with scores [2 SD in PAS, SPQ total score, SPQ factor scores, SCL90-R anxiety and depression with the same indices (latency, deceleration time and gain) for the remaining sample (see ‘‘Methods’’ section). None of the Levene’s tests for equality of variance was statistically significant, indicating that the two
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Table 2 Effects of schizotypy, anxiety, depression, closed-loop pursuit and active fixation on predictive pursuit residual gain Residual gain SCL90-Ranxiety SCL90-Rdepression
0.124 (0.048) -0.090 (0.14)
PAS score
0.029 (0.62)
SPQcog-per
0.022 (0.79)
SPQnegative SPQdisorganized SPQparanoia P_G30
0.099 (0.13) 20.229 (0.03) 0.071 (0.37) 20.125 (0.002)
P_SF10
0.091 (0.024)
F_DIS
0.112 (0.005)
Multiple regression analysis results for the regressions between gain of the predictive pursuit task and the psychometric variables as well as the indices of performance of the closed-loop pursuit and fixation task. Standardized regression coefficients (b) and their p for their relative contribution are presented. Statistically significant values (P \ 0.05) are presented in bold. The independent factors were SCL90R anxiety and depression scores, PAS score, the four SPQ factor scores, P_G30: gain of the smooth pursuit task at 30°/s target speed, P_SF10: saccade frequency in the smooth pursuit task at 10°/s target speed and F_DIS saccade frequency in the fixation task with the presence of distracting targets
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groups for all comparisons could be considered homogeneous in terms of variance. Only two significant differences were observed for the comparisons of the high score groups with the remaining sample. As can be seen in Table 3, subjects scoring high in the anxiety scale of SCL-90R, as well as subjects scoring high in the negative schizotypy factor, displayed higher residual gain than the remaining sample. In order to clarify whether the association between negative schizotypy and predictive pursuit gain is a dependent on state anxiety, we performed an Analysis of Covariance using anxiety as confounding continuous predictor and high negative schizotypy as a fixed factor. In this analysis, the effect of high negative schizotypy was not significant (F1,754 = 3.79, P [ 0.05), while the effect of anxiety was significant (F1,754 = 7.04, P \ 10-2), indicating that the association between high negative schizotypy and residual gain was dependent on state anxiety.
Discussion We investigated possible associations between the three indices of predictive smooth pursuit performance (latency, deceleration time and residual gain) and schizotypal characteristics (a general schizotypy index as inferred by PAS and total SPQ scores as well as specific schizotypy factors, namely odd perceptual experiences, negative, disorganization and paranoid symptoms) as well as current state anxiety and depression (measure with SCL90-R). We did not observe significant correlations between latency or deceleration time and any of the psychometric variables. We observed two independent associations between state anxiety and disorganized schizotypal characteristics with residual gain. Increasing anxiety was associated with an
increase in residual gain, while increasing disorganized schizotypy was associated with a decrease in residual gain. Thus, state anxiety was related to better predictive pursuit performance, while disorganized schizotypy was related to deficient predictive pursuit performance. Previous studies have documented a deficit in predictive pursuit measured as lower residual gain for schizophrenia patients (Thaker et al. 1999), as well as their healthy relatives (Thaker et al. 1998, 2003). In one of these studies, Thaker et al. (2003) have observed the same deficit in schizophrenia patients’ relatives with high schizotypal personality characteristics compared with community subjects and patients’ relatives without schizotypal characteristics. The current study then extends these previous observations in a large sample of apparently healthy subjects. According to our knowledge, this is the first report investigating the effect of schizotypy and current state psychopathology on the performance of the predictive pursuit in a large sample of apparently healthy subjects. The current study showed that the deficit in predictive pursuit performance was related to a particular dimension of schizotypy, namely the disorganized factor. These characteristics that resemble the disorganization symptoms of schizophrenia have been linked to the cognitive deficit in schizophrenia and more specifically to the hypothesized frontal lobe deficit in the disorder (Weinberger 1988). The predictive pursuit deficit in disorganized schizotypy observed in this study is in accordance with the results previously described for the performance in the closed-loop pursuit in the same sample (Smyrnis et al. 2007). In that study, we observed that the subgroup of individuals with scores[2 SD in the disorganized schizotypal characteristics displayed deficient closed-loop smooth eye pursuit performance as indicated by lower gain and higher saccade frequency. The difference between that study and the current
Table 3 Comparison of predictive pursuit performance between high psychometric score groups and the population Latency Mean
Deceleration time High
t value (p)
Mean
High
Gain t value (p)
Mean
High
t value (p) -0.45 (0.65)
PAS score
164.9
171.6
-1.10 (0.27)
110.0
109.6
0.06 (0.95)
0.37
0.39
SPQ total
165.1
172.2
-0.76 (0.45)
109.8
119.6
-0.82 (0.41)
0.37
0.36
0.36 (0.71)
SPQ Fcog-per
165.3
167.8
-0.36 (0.72)
109.5
122.7
-1.44 (0.15)
0.37
0.38
-0.05 (0.95)
SPQ Fnegative
165.2
168.8
-0.49 (0.62)
110.7
96.9
1.48 (0.14)
0.37
0.45
22.11 (0.03)
SPQ Fdisorganized
165.2
172.6
-0.76 (0.45)
110.1
109.9
0.02 (0.99)
0.37
0.44
-1.2 (0.23)
SPQ Fparanoia
165.5
160.7
0.45 (0.65)
109.9
121.1
-0.83 (0.41)
0.37
0.37
0.14 (0.89)
SCL90-R Anx SCL90-R Depr
165.9 165.9
155.9 158.1
1.47 (0.14) 1.20 (0.23)
109.6 109.0
111.8 115.7
-0.25 (0.80) -0.79 (0.43)
0.37 0.37
0.45 0.38
22.19 (0.03) -0.24 (0.81)
The table presents the mean values for the high psychometric score groups (high) and the remaining population (mean) for all three predictive pursuit indices of performance as well as the result of the t test comparisons of the two groups. Statistically significant differences (P \ 0.05) between the two groups are presented in bold. Psychometric scores include PAS score, the total SPQ score, the four SPQ factor scores and the SCL90R anxiety and depression scores
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one was that the association between disorganized schizotypy and closed-loop pursuit performance was restricted to the high score group, while the association between disorganized schizotypy and predictive pursuit performance was observed on the whole sample and not on the high score group. This difference suggests that predictive pursuit might be a more sensitive but less specific endophenotype compared with the closed-loop pursuit performance of the cognitive deficit in the schizophrenia spectrum. Interestingly, in a previous study (Smyrnis et al. 2004), the disorganization factor of SPQ was also correlated with fixation instability in the fixation task with the presence of distracting targets in the same sample. Adding these previous results to the current results, we can summarize that disorganized schizotypy was correlated with worse performance in predictive pursuit, closed-loop pursuit and active fixation tasks. In our previous report (Kattoulas et al. 2011), we also observed that lower residual gain in predictive pursuit was correlated with better performance in the closed-loop pursuit and the fixation task with distracters. We thus explored further the association between disorganized schizotypy and residual gain by including as predictors the indices of closed-loop pursuit and fixation task performance. In this analysis, we confirmed that the association was independent of the effects of these other oculomotor tasks. Thus, the deficit in predictive pursuit performance with increasing disorganized schizotypy is independent of the deficit observed for the same schizotypy factor in closed-loop pursuit and active fixation oculomotor tasks performance, suggesting a specificity for this endophenotype in its effects on the schizophrenia spectrum. The literature on specific schizophrenia symptoms and oculomotor performance is inconsistent. In some studies, negative symptoms have been associated with deficient antisaccade performance in patient groups, while others showed no significant differences for negative versus nonnegative symptomatology (for a review Gooding and Basso 2008). Grouping patients according to deficit and non-deficit symptoms revealed increased antisaccade latency for the deficit group (Nkam et al. 2001) and deficient pursuit initiation in the smooth pursuit task (Hong et al. 2003). The literature on the effect of schizotypal traits of healthy subjects on oculomotor tasks is even more inconsistent (for antisaccade Gooding and Basso 2008, for smooth pursuit O’Driscoll and Callahan 2008). According to our knowledge, this study along with our previous publications (Smyrnis et al. 2004, 2007) linking components of schizotypy traits with oculomotor performance is based on the largest sample of apparently healthy subjects ever examined. It is known that the neural substrate of smooth eye pursuit involves an extended network of cortical and subcortical areas including the frontal eye fields, supplementary eye fields, intraparietal sulcus, precuneus, extrastriate
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areas (medial temporal cortex and medial superior temporal cortex) and the cingulate cortex (Berman et al. 1999; O’Driscoll et al. 2000). Neuroimaging studies (PET and fMRI) of smooth pursuit eye movements in schizophrenia patients or their first-degree relatives have shown decreased frontal network activation (O’Driscoll et al. 1999; Tregellas et al. 2004). In a recent neuroimaging study, Hong et al. (2005) have compared fMRI activation during predictive pursuit performance of two groups of subjects, a healthy population group and schizophrenia patients, based on matched performance in the closed-loop pursuit. This study provided functional anatomical evidence supporting the notion of reduced function in the extraretinal motion processing pathway in schizophrenia, including among others frontal and supplementary eye fields. Increased activation in medial occipitotemporal cortex suggested an increased dependence on immediate retinal motion information to compensate for reduced extraretinal signaling during sustained visual tracking. Based on this evidence and our results of the independent effect of disorganization symptoms on closed-loop pursuit, predictive pursuit and active maintenance of fixation, we could speculate that the cognitive dysfunction in schizophrenia spectrum could be decomposed in more than one distinct endophenotypes each related to a specific functional-anatomical substrate. The effect of state anxiety is an interesting finding, since in our previous studies on the effect of psychometric variables on different eye movement variables in the same population, we have only reported a weak effect of state anxiety and depression on antisaccade performance (Smyrnis et al. 2003) but not with the other oculomotor variables as the fixation task (Smyrnis et al. 2004) or the closed-loop smooth pursuit task (Smyrnis et al. 2007). In order for smooth eye pursuit performance to be considered a possible schizophrenia endophenotype, it should be a trait characteristic, stable in time and unaffected by clinical condition. Still, studies in healthy subjects report conflicting results for test–retest stability (Roy-Byrne et al. 1995; Ettinger et al. 2003) of the classic closed-loop pursuit. Most of the studies for schizophrenia patients indicate that the most important measures of eye-tracking performance in psychiatric patients are not significantly influenced by neuroleptic medication or clinical state and are stable across time (Calkins et al. 2003; Campion et al. 1992; Sweeney et al. 1994). In a recent study (Kallimani et al. 2009), we observed that pursuit task performance of schizophrenia patients was not stable over time, although again this instability was not related to the changes in psychopathological status of these patients. In that study, psychopathology was assessed by the Greek version of PANSS (Positive and Negative Syndrome Scales) (Kay et al. 1987; Lykouras et al. 1997) evaluating specifically psychotic features and not state anxiety. The
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results presented in the current study favor the hypothesis that state anxiety might exert a small but significant enhancing effect in the predictive pursuit gain and thus should be taken into account in future research of predictive pursuit performance. In conclusion, this study confirmed a specific correlation between disorganized schizotypy personality traits in healthy individuals and lower residual predictive gain and provided support for the hypothesis that predictive pursuit might be an indicator of frontal dysfunction in schizophrenia spectrum disorders that is distinct from other oculomotor endophenotypes such as closed-loop pursuit or maintenance of active fixation. The effect of current state anxiety on predictive gain raises questions on the specificity of this index and should be taken into account in future research, since anxiety symptoms are often present in individuals with schizotypal personality characteristics. Further research is needed to clarify whether predictive gain could be considered a valid and reliable schizophrenia spectrum endophenotype. Acknowledgments This work was supported by the grant ‘‘EKBAN 97’’ to Professor C.N. Stefanis from the General Secretariat of Research and Technology of the Greek Ministry of Development. ‘‘Intrasoft Co’’ provided the technical support for this project.
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