Reduced prepulse inhibition as an early vulnerability marker of the psychosis prodrome in adolescence
Article Outline
- Abstract
- 1. Introduction
- 2. Method
- 3. Results
- 4. Discussion
- Role of funding source
- Contributors
- Conflict of interest
- Acknowledgments
- Appendix A. Supplementary data
- References
- Copyright
Abstract
Background
The onset of psychosis is thought to be preceded by neurodevelopmental changes in the brain. However, the timing and nature of these changes have not been established. The aim of the present study was to determine whether three “classic” neurophysiological markers of schizophrenia are also characteristic of young adolescents (12–18
years) at ultra-high risk for psychosis (UHR).
Methods
63 young UHR individuals and 68 typically developing, age-, sex- and IQ-matched controls were recruited for neurophysiological assessment. Data for P50 suppression, prepulse inhibition (PPI) and smooth pursuit eye movements (SPEM) were gathered and compared.
Results
UHR individuals showed reduced PPI compared to controls, which became more pronounced when controls were directly compared to medication-naive UHR individuals (N=39). There were no group differences in P50 or SPEM measures.
Conclusions
These results suggest that PPI is a relatively early vulnerability marker, while changes in other neurophysiological measures may only be detected or affected later during the illness course. Antipsychotic and antidepressant medication may aid in elevating PPI levels and potentially have a neuroprotective effect.
Keywords: Ultra-high risk for psychosis, p50, Prepulse inhibition, Smooth pursuit
1. Introduction
The putative state of disrupted neurobiological function in psychotic disorders is increasingly supported by the identification of illness related biomarkers. Neurophysiological recordings provide a relatively non-invasive observation method and have shown evidence for aberrant neuronal activity in schizophrenia and related disorders (Turetsky et al., 2007, Thaker, 2008). It has been suggested that these abnormalities represent (heritable) traits, rather than state dependent markers, because they are present in unaffected, first-degree relatives and in individuals in remission of psychosis as well (Greenwood et al., 2007, Turetsky et al., 2007, Thaker, 2008). However, the timing of manifestation and predictive validity of these neurobiological markers for the subsequent transition to psychosis remains unknown.
Introduction of the clinical- or ultra-high risk (UHR) design has helped to identify individuals at increased risk of developing psychosis on the basis of sub-syndromal symptoms (Wood et al., 2003). Compared to the general population the eventual incidence of psychosis is high in UHR studies and conversion occurs in relative close proximity of initial intake (Cannon et al., 2008, Yung et al., 2008). Neurophysiological studies in adult UHR individuals have supported the notion that schizophrenia markers are already present before the onset of psychosis (Brockhaus-Dumke et al., 2005, Nieman et al., 2007, Bramon et al., 2008, Brockhaus-Dumke et al., 2008, Frommann et al., 2008, Ozgurdal et al., 2008, Quednow et al., 2008, van Tricht et al., 2010a, van Tricht et al., 2010b), and may become more pronounced in those who continue to develop psychosis (Nieman et al., 2007, Brockhaus-Dumke et al., 2008, van Tricht et al., 2011). This suggests that a progressive worsening of prodromal symptoms is associated with neurophysiological changes. However, if such changes precede the onset of psychosis, these should be present in the at-risk period irrespective of the age of onset. Only a few previously published studies have also included young adolescents at UHR and the outcomes of these studies were partially at odds with each other (Myles-Worsley et al., 2004, Cadenhead et al., 2005), leaving it unclear whether altered neurophysiological measures can be detected at an early stage in life.
To address this issue, the current study investigated three well-established neurophysiological measures, typically abnormal in schizophrenic individuals, in a sample of young adolescents at UHR for psychosis (aged 12–18years): P50 suppression, prepulse inhibition (PPI) and smooth pursuit eye movement (SPEM). We hypothesized that the UHR group would show reduced P50 suppression and PPI compared to typically developing adolescents. For SPEM, we expected that the UHR group would show an increase in number of saccades and a decreased velocity gain.
2. Method
2.1. Subjects
Sixty-three adolescents meeting at least 1 of 4 criteria for UHR (Table 1) were referred by general practitioners or other psychiatric clinics and included in this study. These criteria have previously been published (Sprong et al., 2008, Ziermans et al., 2009) and are similar to frequently used criteria for UHR (Olsen and Rosenbaum, 2006). The control group consisted of 68 typically developing adolescents. The first three inclusion criteria were assessed with the Structured Interview for Prodromal Syndromes (McGlashan et al., 2001). The fourth inclusion criterion was assessed with the Bonn Scale for the Assessment of Basic Symptoms-Prediction List (Schultze-Lutter and Klosterkötter, 2002).
Table 1. Ultra high risk criteria.
1.Attenuated positive symptoms (APS) |
2.Brief, limited or intermittent psychotic symptoms (BLIPS) |
3.30% Reduction in overall level of social, occupational/school-, and psychological functioning (i.e. GAF-score) in the past year, combined with a genetic risk of psychosis (i.e. having a first- or second-degree relative with an established psychotic disorder) (GRD) |
4.Two or more of a selection of nine basic symptoms, i.e. subjective deficits in cognitive, perceptual, and motor functioning (COGDIS) |
Sixty-eight typically developing controls were recruited from secondary schools in the region of Utrecht. They were excluded if they met one of the UHR-criteria, if they or any first degree relative had a history of any psychiatric illness, or if there was a second-degree relative with a psychotic disorder.
Groups were matched for age, gender, IQ and handedness (Table 2). All participants had Dutch nationality and were aged between 12 and 18years. Subjects were excluded if there was evidence of any past or present neurological disorder (e.g. epilepsy). Drug- and alcohol abuse were additional exclusion criteria although UHR subjects were permitted a history of drug use, if symptoms had also been present in the absence of drugs. Figures on (self-reported) smoking, cannabis use and psychopharmacological medication use are provided in Table 2. All individuals had a level of verbal intellectual functioning (VIQ)≥75, as assessed with the Wechsler Intelligence Scales (Wechsler, 1997, Wechsler, 2002). Each individual signed an informed consent and for those younger than 16, parents co-signed. This study was approved by the Dutch Central Committee on Research Involving Human Subjects.
Table 2. Demographics and symptom scores ultra-high risk individuals and controls.
| UHR | Controls | Statistic | |
|---|---|---|---|
| (n=63) | (n=68) | ||
| Sex, M/F (%M) | 38/25 (60) | 31/37 (46) | χ2=2.87, p=0.09 |
| Handedness, R/L/M (%R) | 58/3/2 (92) | 62/3/3 (91) | χ2=1.78, p=0.78 |
| Total IQ, mean (sd) | 100 (13) | 104 (11) | t=−1.71, p=0.09 |
| Age, mean (sd) | 15.7 (2.1) | 15.5 (1.7) | t=1.05, p=0.36 |
| SIPS total score, mean (sd) | 27.1 (13.9) | 2.1 (2.9) | U=18.5, p<0.001 |
| BSABS total score, mean (sd) | 22.8 (15.2) | 1.2 (1.6) | U=81.0.14, p<0.001 |
| GAF-score, mean (sd) | 57.2 (15.0) | 94.4 (13.0) | U=115.0, p<0.001 |
| UHR criterium, n (%) | |||
| 1. APS | 57 (90) | NA | NA |
| 2. BLIPS | 3 (5) | ||
| 3. GRD | 2 (3) | ||
| 4. COGDIS | 34 (54) | ||
| Smokinga, n (%) | |||
| – none | 44 (71) | 64 (97) | χ2=16.66, p<0.001 |
| – occasional | 4 (6) | 0 (0) | |
| – regular | 14 (23) | 2 (3) | |
| Current medication2, n (%) | NA | ||
| – any | 24 (38) | NA | |
| – atypical antipsychotic | 13 (21) | ||
| – antidepressant | 8 (13) | ||
| – psychostimulant | 3 (5) | ||
| – other | |||
| Cannabis use, n (%) | 1 (2) | NA | |
| – never used | 52 (83) | NA | |
| – last regular use3>1month | 7 (11) | ||
| – regular use<1month | 4 (6) |
aData missing for 1 UHR and 2 controls; 2 regular medication only; 3 more than once a week; UHR |
2.2. Stimulus presentation
Subjects were seated upright in an acoustically shielded room, 1 meter (measured from the position of the eyes) in front of a 21-inch computer screen on which visual stimuli were presented. Auditory stimuli were presented binaurally through stereo insert earphones (Eartone ABR). Software settings were calibrated by an artificial ear (Brüel and Kjær, type 4152) to ensure adequate stimulus intensities.
2.3. Recordings
Recordings were obtained from 32 AgAgCl electrodes using a BioSemi Active Two EEG system (Biosemi, Amsterdam). For the P50 measurement, EEG was sampled at 2048Hz, referenced to an additional active electrode (Common Mode Sense) during recording, and stored as a continuous signal. An electrode placed on the left mastoid was used as off-line reference for EEG measurement. For PPI, electromyographic (EMG) activity of the right orbicularis oculi muscle was recorded from bipolar electrodes. One was placed over the medial aspect of the muscle and one displaced 0.5cm laterally. Horizontal and vertical eye movements were recorded using electro-oculography (EOG) to obtain SPEM information. PPI and P50 data were analyzed using the software package Brain Vision Analyzer (Brain Products, München).
2.4. Experimental paradigms
Paradigms used in this study have been described in previous publications (Magnee et al., 2009, Vorstman et al., 2009, van Rijn et al., 2011, Ziermans et al., 2011a). Abbreviated descriptions of paradigms are given below and additional details are provided in the Supplementary Information.
2.4.1. P50 suppressionA block of 36 click pairs (86dB, 1.5ms duration white noise) was presented, with an interstimulus interval of 500ms and an intertrial interval of 10s. Subjects were instructed to count click pairs and report the number afterwards. The P50 ratio was calculated for the Cz electrode as the amplitude of the P50 potential elicited by the testing stimulus divided by the amplitude elicited by the conditioning stimulus (T/C).
The experiment consisted of 24 randomized trials: 12 startle stimuli preceded by a prepulse and 12 startle stimuli with no prepulse. The prepulse and startle stimuli were bursts of white noise (duration 22.5 and 32.5ms, intensity 87 and 106.5dB, respectively, rise/fall 0.1ms) over a 30dB background noise, with a fixed interstimulus interval of 120ms. The intertrial interval varied between 12 and 23s. %PPI was defined as the percentage of reduction of the startle amplitude over prepulse–pulse trials, compared to the pulse alone trials (PPI=100(1−pp/p)), where pp indicates amplitude over prepulse trials and p indicates amplitude over pulse alone trials.
The target was a small, but clearly visible, white moving dot (2 by 2pixels) on a uniform dark-gray background. There were seven trials, each consisting of 5 movements of the dot from left to right and back again with amplitude from left to right of 20° of visual angle. In each trial the dot moved at a constant velocity (sinusoidal motion). Stimulus velocities of 8, 13, 16, 20, 24, 29, and 35°/s were used, and in this order. Saccades are defined as a period of absolute velocity above 35°/s between two successive acceleration peaks of opposite sign. Velocity gain (VG) is defined as mean eye velocity divided by target velocity. For each of the seven frequencies presented, the VG and the number of saccades (NSAC) were calculated. No absolute position of gaze information was available, so it was not possible to determine saccadic type (anticipatory, leading, catch-up etc.).
2.5. Data analysis
Comparison of group characteristics was performed using Student'st-tests for age and IQ scores, χ2-test for gender distribution and smoking status and Mann–Whitney U tests for clinical scores (p<0.05, two-tailed). Dependent variables for the neurophysiological paradigms were: P50 ratio, %PPI, and, for SPEM, VG and NSAC per frequency. For each dependent measure, extreme outliers (>3 standard deviations from the mean) were removed. To check for the occurrence of main effects of stimuli within- and between-subjects, a mixed-model analysis of variance (ANOVA) was used for all paradigms. For PPI, startle amplitudes were log-transformed to diminish the effect of a skewed distribution. %PPI was calculated by using the absolute startle amplitudes. Greenhouse–Geisser corrections were used for SPEM measures in case the assumption of sphericity was violated. ANOVA was applied to test for differences between groups on P50 ratio and %PPI. Group analyses were repeated with medicated individuals excluded from the total UHR sample, to control for potential medication effects. Results were considered significant if p<0.05, two-tailed.
3. Results
3.1. Group characteristics
Details are given in Table 2. Total groups were matched for age, sex and TIQ. Subsequent exclusion of individuals did not affect statistical matching.
3.2. P50 suppression
Due to incomplete data and technical errors, data from 5 control subjects and 1 UHR subject were excluded. Three data outliers (two controls, one UHR) were also excluded. Within-subjects there was significant P50 suppression (F(1,120)=126.99, p≤0.001; Fig. 1), illustrated by a smaller positive response on T stimulus (controls: M=1.3, sd=1.3; UHR M=1.0, sd=1.1) compared to C stimulus (controls: M=3.1, sd=2.0; UHR M=2.7, sd=1.6), but no effect between groups. Additionally, there were no differences between groups for P50 ratio. Repeated analyses with unmedicated UHR subjects only (n=39) yielded similar results.
Fig. 1
P50 auditory evoked potential of Cz-electrode site in response to the conditioning (C-stimulus) and testing stimuli (T-stimulus) in the control group (left) and the ultra-high risk (UHR) group (middle) and an overlay of the waveforms (right).
3.3. Prepulse inhibition
Three subjects (1 control, 2 UHR) were excluded based on incomplete data or technical errors. Additionally, 5 data outliers (3 controls, 2 UHR) were removed from the analyses. PPI suppression was observed within-subjects (F(1,121)=350.27, p<0.001), reflecting greater absolute startle magnitude for pulse alone (controls: M=30.27, sd=28.5; UHR M=32.87, sd=32.2) than for prepulse stimuli (controls: M=8.63, sd=11.4; UHR M=12.97, sd=13.0). There was a significant PPI X group interaction (F(1,121)=5.06, p=0.045), suggesting greater inhibition for controls compared to UHR individuals. When all medicated individuals were excluded (37 UHR remaining), the PPI X group interaction remained significant (F(1,99)=7.10, p=0.009; Fig. 2A). In line with these results, there was a significant overall group effect for %PPI (based on absolute amplitudes), with UHR individuals displaying significantly lower %PPI than the control group (F(1,122)=4.31, p=0.040). After exclusion of medicated UHR individuals this effect became more significant (F(1,99)=7.40, p=0.010; see Fig. 2B). In all analyses pulse alone amplitude was checked for potential confounding influence and did not differ between UHR individuals and controls.
Fig. 2
A. Error bars (mean
±1 standard error) of the log-transformed startle amplitudes for pulse alone and prepulse stimuli. The left panel shows data for the total control and ultra-high risk groups and in the right panel medicated individuals have been excluded from the UHR group. B. Total percentage prepulse inhibition for UHR individuals based on medication type. AD=antidepressant; AP=antipsychotic; St=psychostimulant; AC=anticonvulsant (used as mood stabilizer).
3.4. Smoot pursuit eye movements
Data was incomplete or of insufficient quality for 16 subjects (8 UHR, 8 controls) and one outlier (UHR) was excluded from NSAC analyses. A main effect of frequency within-subjects was found for VG (F(3.7,420.9)=551.39, p<0.001) and NSAC (F(2,220.1)=173.45, p<0.001). Both groups displayed a similar pattern for VG and NSAC per frequency factor (Fig. 3). Repeated analyses for controls versus unmedicated UHR individuals only (n=35) did not affect the outcome.
Fig. 3
Smooth pursuit eye movements as a function of velocity gain (left) and number of saccades per second (right) for ultra-high risk (UHR) individuals (red) and typically developing controls (blue).
3.5. Sex, age and IQ
Paradigms were checked for potential confounders using the mixed-linear models. When sex, age and TIQ were entered as covariates, results remained unchanged for P50. PPI showed significant within-subject interactions for age (F(1,118)=4.71, p=0.032) and TIQ (F(1,118)=7.42, p=0.007), with higher age and TIQ associated with greater PPI, predominantly due to an increase in pulse alone amplitude. Interaction with group was no longer significant (p=0.07) and there were no between-group differences. In the medication naive-groups PPI×TIQ remained significant (F(1,96)=10.55, p=0.002) and the group effect re-appeared (F(1,96)=5.74, p=0.019). Group comparisons were unchanged for SPEM measures, but there was a significant interaction for the medication-naive group comparison of NSAC×age (F(2,166.3)=3.76, p=0.026), showing a decrease in NSAC with increasing age, independent of stimulus velocity.
3.6. Smoking and cannabis use
Influence of smoking status and cannabis use was tested for UHR individuals only, by adding these variables as between-subjects factors. For both variables substance-naive individuals were compared to exposed individuals (see Table 2). No group differences were found based on smoking status or cannabis use.
4. Discussion
This study set out to investigate whether commonly reported neurophysiological markers of the schizophrenia spectrum are also characteristic of the psychosis prodrome during adolescence. Three “classical” paradigms were assessed in a large group of adolescents at ultra-high risk (UHR) for psychosis and typically developing controls. Results showed that prepulse inhibition (PPI) was affected and significantly reduced in UHR individuals compared to controls. This group difference became more apparent when controls were directly compared with medication-naive UHR individuals.
A previous study investigating acoustic PPI using similar UHR inclusion criteria in adult individuals, demonstrated a clear reduction in PPI for both prodromal individuals and unmedicated schizophrenics (Quednow et al., 2008). However, two recent longitudinal PPI studies (Cadenhead, 2011, Ziermans et al., 2011a) have shown mixed results regarding whether reduced PPI is a consistent vulnerability marker over time in UHR individuals, possibly due to differences in paradigms (e.g. interstimulus interval, background noise, number of trials) or confounding factors such as age, sex, IQ, substance use or medication. Regarding medication, the greater reduction of PPI found in medication-naive individuals in this study strengthens the idea that PPI is potentially normalized by psychopharmaceutical medication (Kumari and Sharma, 2002, Oranje et al., 2008), particularly for antipsychotic and antidepressant medication (see Fig. 2B).
The results of the present study indicate that PPI qualifies as a neurobiological vulnerability marker in adolescence. It has been suggested that there are quantitative and qualitative changes in the developmental course of neurophysiological measures towards young adulthood (Wetzel et al., 2006). Interestingly, in this study we found an interaction between PPI and age, showing that level of PPI increases with age, particularly in UHR individuals. If neurobiological vulnerability markers are characteristic of the psychosis prodrome, then during young adolescence detection might be hindered by late maturational processes of the brain (Shaw et al., 2008). The presence of PPI reductions in UHR adolescents suggests, however, that subtle brain changes related to the disease process may already manifest at the level of the cortico–striato–pallido–thalamic (CSPT) circuitry responsible for PPI (Braff et al., 2001, Swerdlow et al., 2001), before large structural brain changes in temporal and frontal gray matter volume, observed in UHR adults (Smieskova et al., 2010), become more apparent.
Our results for auditory P50 suppression are in agreement with a previous study (Cadenhead et al., 2005) that failed to find an overall group difference between controls and adolescents at risk. However, earlier study results in medication-naive UHR adolescents reported a reduced P50 suppression, especially for those with a first degree relative with schizophrenia (Myles-Worsley et al., 2004). Comparison across these two studies and ours is partially restricted however, due the large differences in age range per study (4–18years). More recently, evidence was reported for a P50 suppression deficit in antipsychotic-naïve UHR-adults (mean age: 23.5years) (Brockhaus-Dumke et al., 2008). Changes were more pronounced in those with subsequent transition to psychosis within two years after inclusion, although they were also present in those without transition. A limitation of this study was that the groups were not well matched for size, age or IQ. Taken together, the available studies in individuals at risk are inconclusive about whether reduced P50 suppression can be considered a vulnerability marker during early development. However, the studies above have also shown data that suggests that a genetic high risk combined with clinical symptoms increases the chance of detecting sensory gating changes.
In our study we did not find evidence for abnormal SPEM measures in UHR individuals. One previous study investigated SPEM in adolescent and young adult UHR individuals and found that these individuals had higher number of corrective and non-corrective saccades than controls, but no differences in smooth pursuit gain (van Tricht et al., 2010a). Although we were unable to find group differences in NSAC in our sample, our SPEM method only looked at horizontal eye movements and could not distinguish between corrective and non-corrective saccades, unlike the method use by van Tricht et al. Additionally, a large study comparing a group of genetic high-risk children to healthy controls and individuals with childhood onset schizophrenia showed that only the latter group had saccadic abnormalities (Ross et al., 2005). Similarly, our results suggest that some SPEM abnormalities may potentially be more descriptive of the psychotic state itself than of the preceding period.
Although no gross abnormalities were found for P50 suppression and SPEM, it does not imply a lack of use for these paradigms in high-risk research. The main reason for including these particular markers in our study was because of their frequently replicated deviance scores in individuals with schizophrenia spectrum disorders and the lack of knowledge about their onset. However, neurophysiological parameters are sensitive measures and the use of slightly modified paradigms or alternative parameters could have yielded different results. For example, number of stimuli/epochs was kept relatively low in our paradigms due to time constraints and may have decreased the signal-to-noise ratio. For PPI and P50 we used peak averages as opposed to trial-by-trial measures, preventing analyses of sensitization and habituation effects (Halberstadt and Geyer, 2009). Furthermore, during the P50 assessment participants had to count click pairs, which may have modulated the attention of participants in an unfavorable way for detecting gating differences (Yee et al., 2010). Therefore, follow-up data using identical paradigms are needed to determine whether additional changes occur alongside clinical improvement or deterioration in UHR individuals.
Presence of a psychotic transition in our UHR individuals was monitored during a 2-year follow-up study (Ziermans et al., 2011b). Within the current study eight (13%) UHR individuals had experienced a psychotic transition. However, statistical restrictions did not allow for meaningful, unbiased group analyses. Visual inspection of the data (see Supplementary Information) suggested that, besides a further reduction of PPI, UHR individuals with subsequent psychosis might also show impaired position gain during the smooth pursuit task compared to controls and UHR individuals without psychosis. These findings should be interpreted with caution and confirmed in larger samples of individuals with a psychotic conversion or in longitudinal studies (Cadenhead, 2011, van Tricht et al., 2011, Ziermans et al., 2011a).
In conclusion, this study has provided evidence for changes in PPI, but not P50 gating or SPEM, in a group of young adolescents at risk for psychosis compared to typically developing controls. Reduced PPI may therefore represent one of the earliest vulnerability markers for psychosis, although follow-up studies are required to determine its specificity to a particular clinical phenotype. Furthermore, psychopharmaceutical medication may partially obscure the presence of PPI reductions by normalizing the auditory startle response. Negative findings for P50 and SPEM in this study may be reflective of suboptimal recording procedures, unaddressed confounders or brain maturational processes with the ability to detect vulnerability markers at this age. Alternatively, these markers could be more closely related to the actual onset of psychosis than a high-risk state. Additional results from longitudinal and multi-method neuroimaging studies are required to rule out some of these explanations.
Role of funding source
This work was funded by a grant from ZonMw — The Netherlands organization for health research and development. ZonMW had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
Contributors
Drs. van Engeland, Schothorst, Kemner and Ziermans conceived the idea and methodology of this study. Drs. Schothorst, Sprong and Ziermans were involved in subject recruitment. Drs. Schothorst, Sprong, van Engeland and Ziermans were involved in clinical and diagnostic assessments. Drs. Magnée and Ziermans processed all neurophysiological data. Dr. Ziermans conducted the statistical analyses and. Dr. Kemner contributed in the writing of the manuscript. All authors contributed to and have approved the final manuscript
Conflict of interest
The authors have no competing financial interests to declare in relation to the current work.
Acknowledgments
The authors would like to thank Gert Camfferman, Petra Klaassen and Anneke Schouten for their contributions to this study and Martijn J. Mulder for graphic support.
Appendix A. Supplementary data
Supplementary material.
Table. Group means±standard deviations for P50 and prepulse inhibition (PPI) parameters.
Figure 1 Smooth pursuit position gain (panels A and B) and number of saccades per stimulus velocity (panels C and D). Displayed seperately for all participants (panels A and C; 60 controls, 48 ultra-high risk individuals without psychosis [UHR-NP], 7 ultra-high risk individuals with psychosis [UHR-P]) and medication-naive participants (panels B and D; 60 controls, 30 UHR-NP, 5 UHR-P).
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PII: S0920-9964(11)00544-5
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