Int J Psychiatry Clin Pract 2015; Early Online: 1–9. © 2015 Informa Healthcare ISSN 1365-1501 print/ISSN 1471-1788 online. DOI: 10.3109/13651501.2015.1016041
ORIGINAL ARTICLE
Neurological soft signs and prepulse inhibition of the startle reflex in psychosis: A pilot study
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Elisa Ira1, Chiara Bonetto1, Martina Zanoni1, Alessandro Bottacini1, Rodolfo Mazzoncini1, Lisa Martini1, Angela Garozzo1, Antonio Lasalvia2, Michele Tansella1, Mirella Ruggeri1 & Sarah Tosato1 1
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Department of Public Health and Community Medicine, Section of Psychiatry, University of Verona, Italy and 2Unit of Psychiatry, University Hospital of Verona, Verona, Italy
Abstract Objective. Prepulse inhibition (PPI) of the startle reflex deficit and neurological soft signs (NSS) are two markers of vulnerability to psychosis. This study investigated the possibility of a PPI–NSS relation due to a putative common biological substrate, hypothesizing that patients with higher NSS scores also show higher PPI deficits. Moreover, we examined the possibility of an association of PPI deficits and NSS with negative symptoms. Methods. Fifteen subjects with psychosis and fifteen healthy controls underwent PPI and NSS evaluations. Results. Patients did not exhibit higher PPI deficits but only higher NSS rates (p 0.01), as compared with healthy controls. Higher NSS rates were not associated with PPI deficits, and NSS sensory integration signs correlated positively with negative symptoms (p 0.01). Conclusion. Our study supported the hypothesis that NSS are trait markers whereas PPI deficits state markers and that their putative common biological substrate is not sufficient to determinate an association between them. The study hypothesis, however, needs further investigation. Key words: PPI, NSS, sensory integration signs, motor sequencing signs, schizophrenia (Received 28 April 2014; accepted 29 January 2015)
Objective Prepulse inhibition (PPI) of the acoustic startle reflex is an operational measure of sensorimotor gating (Geyer et al. 1990): PPI refers to the normal unlearned reduction in the amplitude of the startle reflex occurring when a weak stimulus (the prepulse) precedes an intense auditory startle-eliciting stimulus (pulse). Reduced PPI has long been considered a biomarker for vulnerability to schizophrenia (Thaker 2011). Recently, evidence have suggested that PPI may be influenced by the course of the illness and by the pharmacologic treatment received (Meincke et al. 2004; Minassian et al. 2007), pointing out to the importance of conducting studies on patients at early phase of illness. Relatively few studies have been conducted on early phase of schizophrenia (Aggernaes et al. 2010; Mackeprang et al. 2002; Ludewig et al. 2003; Meincke et al. 2004; Kumari et al. 2007a; Quednow et al. 2008), finding that antipsychotics may partially restore PPI deficit (Kumari et al. 2007a), which was mostly detected in drug-naïve subjects. Only one study has been conducted on individuals with first-episode psychosis and did not find a PPI deficit (Cadenehead 2011). Consequently, PPI may be considered a state marker of psychosis and since evidence are not conclusive; studies on patients with a short duration of illness and pharmacological treatment are encouraged. The brain correlate of PPI deficit in psychosis is still unclear: there is evidence of the major role played by Correpsondence: Dr Sarah Tosato, Department of Public Health and Community Medicine, Section of Psychiatry, University of Verona, Policlinico G.B. Rossi, P.le L.A. Scuro 10, 37134 Verona, Italy. Tel: 39 (045) 812 4441. Fax: 39 (045) 8124155. E-mail: [email protected]
reduced prefrontal cortex volume in contributing to this deficit (Kumari et al. 2008). The finding is significant because this brain area has been found to be reduced in psychosis (Smieskova et al. 2010) and its altered functioning and volume is thought to be linked to the emergence of negative symptoms (Goff and Evins 1998; Goghari et al. 2010). Interestingly, frontal/prefrontal cortex reduction is involved in another deficit which characterizes psychosis, neurological soft signs (NSS) (Dazzan and Murray 2002; Cuesta et al. 2002; Negash et al. 2004; Whitty et al. 2006; Chan et al. 2010). NSS are minor neurological deficits clustering in four main domains (primary neurological signs, sensory integration, motor coordination, and sequencing of complex motor acts signs), reflecting the involvement of different functional areas thereby (Buchanan and Heinrichs 1989). Specifically, sensory integration signs and motor sequencing signs have shown an association with frontal/prefrontal cortex reduction as detected by MRI studies conducted on patients with early psychosis (Dazzan et al. 2004; Venkatasubramanian et al. 2008). These results indicate a certain degree of overlapping between brain regions underlying PPI and NSS; therefore, they strongly suggest that sensory integration and motor sequencing signs may be associated with PPI deficits, perhaps due to prefrontal cortex alterations. If this association was true, this would be worth of interest since evidence suggest that NSS may be considered trait markers for psychosis (Chen et al. 2005; Boks et al. 2006). Thus, this association would indicate the presence of a disrupted biological substrate underlying PPI in psychosis irrespective of the presence of an evident PPI deficit.
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Therefore, we conducted a pilot study, in order to evaluate the presence of a PPI and neurological deficits on a sample of patients with early psychosis, hypothesizing that PPI deficits would be associated with high levels of sensory integration and motor sequencing signs. Moreover, as prefrontal alteration has been associated with negative symptoms, we investigated the potential association between these symptoms and both NSS and PPI deficits.
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Methods Participants The sample consisted of a cohort of psychotic patients with a total duration of illness equal to or less than 5 years, in contact with the South Verona Community-based Mental Health Service, Italy. Cases identified at intake by the South Verona Psychiatric Register (Amaddeo and Tansella 2009) were assigned a research diagnosis of psychosis using the Item Group Checklist of the Schedule for Clinical Assessment in Neuropsychiatry (WHO 1992). Diagnosis was obtained by two senior psychiatrists who had previously received a formal training (S.T. and A.L.); they used all information available in the clinical records, integrated with interviews to the patients’ case managers if needed. Only patients with a confirmed ICD-10 diagnosis of psychosis, either non-affective or affective (F20–29; F30.2, F31.2, F31.5, F31.6, F32.3, F33.3) were included in the present study. Patients presenting a history of neurological disease, prior electro-convulsivant treatment, prior traumatic brain injury, or mental retardation (IQ 70) were excluded from the study. Additionally, pregnant, lactating, or menstruating women were not recruited. Moreover, for the present study, a healthy control group matched for gender, age, years of education, and ethnicity was recruited through notices posted at the University Hospital of Verona, Verona, Italy. Furthermore, since higher NSS motor sequencing scores have been found to be associated with lower cognitive ability in first-episode psychosis (Dazzan et al. 2008), we have selected a comparison group of healthy subjects matched for premorbid IQ. Exclusion criteria were the same as those adopted for the psychiatric patients. The absence of psychiatric disorders was ascertained via two schedules: the Mini International Neuropsychiatric Interview (M.I.N.I. Plus) (Bonora et al. 1995), to exclude any psychiatric disorder in Axis I; and the Structured Clinical Interview for DSM disorders (SCID-II) (Spitzer et al. 1993), to exclude any psychiatric disorder in Axis II. All participants signed informed consent forms. Clinical assessment A set of standardized instruments was used to collect clinical information. For the patient group only, a semi-structured interview and the Positive and Negative Syndrome Scale (PANSS) (Kay et al. 1987) were used to evaluate severity of psychotic symptoms; the scale yields positive symptoms, negative symptoms, and general psychopathology scale scores. In all participants, premorbid IQ was estimated with the Italian version of the National Adult Reading Test (Nelson
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1991; Sartori et al. 1997). A semi-structured questionnaire was used to collect information on tobacco consumption, and the Clinician Alcohol and Drug Use Scale (CAUS) (Mueser et al. 1995) was used to assess alcohol and drug consumption. Moreover, family history of mental illness was assessed with the Family Interview for Genetic Studies (FIGS) (Maxwell 1992). All researchers involved in the assessments received specific training. An inter-rater reliability session was held to test the consistency of evaluation among the researchers. The inter-rater reliability for PANSS was 0.90 (Cronbach’s Alpha). Startle response measurement Firstly, all participants’ hearing was tested by audiometer: anyone unable to detect tones at 45 dB at 500, 1000, or 6000 Hz was excluded. Participants were seated comfortably in an armchair during hearing test and the whole experiment. A commercially available electromyography (EMG) startle system (EMG SR-LAB, San Diego Instrument, San Diego, CA, USA) was used to examine the eyeblink component of the acoustic startle response. Participants were asked to keep their eyes open during the experiment. They were told that the experiment was to measure their reaction to a number of noise bursts, but they received no specific instructions to attend to these or to ignore them. Since nicotine has been found to modulate PPI (Hong et al. 2008), all subjects were required to refrain from smoking cigarets for at least one hour before testing. The eyeblink component of the acoustic startle response was indexed by recording the EMG activity of the right orbicularis oculi muscle after positioning two miniature electrodes (In Vivo Metric, Healdsburg, CA) filled with conductive gel. A ground electrode was placed behind the right ear over the mastoid. All electrode resistances were less than 10 kOhms. EMG activity was band-pass filtered (30–300 kHz). Rectified EMG was digitalized and stored every millisecond from the onset of the startling stimulus until 250-ms post stimulus. Acoustic startle and prepulse stimuli were presented binaurally through headphones (Model EC-9A 6cc, Coll Health, Mississauga, ON). The session began with a 5-min acclimation period of 70-dB background noise, which continued throughout the session, followed by four trial blocks. Block 1 consisted of five pulse-alone trials at an intensity of 115 B and a duration of 40 ms. Blocks 2 and 3 consisted of 40 trials each, containing 8 pulse-alone, 8 no-stimulus trials, and 24 prepulse-pulse trials presented in pseudorandom order (8 presentations of each of the four types of prepulse trials, described below). The prepulse stimuli consisted of 20 ms of 86-dB white noise, which preceded the startle by 30, 60, or 120 ms. Similarly to Block 1, Block 4 consisted of five pulse-alone trials. The EMG data were inspected on a trial-to-trial basis (to exclude person-specific erroneous trials) and were scored by the system’s analytic program for response amplitude (in arbitrary analog-to-digit units). Neurological soft signs An expanded, previously validated version of the Neurological Evaluation Scale (NES) (Buchanan and Heinrichs 1989)
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was used to assess NSS. The NES is a structured scale with 26 items presenting scores as pertaining to four main subscales reflecting different functional areas and showing good construct validity (Buchanan and Heinrichs 1989). The subscales are as follows: I. “primary neurological dysfunction,” which reflects a dysfunction that can be identified by a standard neurological examination, and includes the cranial nerves, eye movement, lateralizing limb pyramidal signs, and frontal release signs; II. “sensory integration dysfunction,” which reflects a dysfunction in the integration of sensory information and includes signs such as right/left confusion, astereognosis, agraphesthesia, and audiovisual integration; III. “motor coordination dysfunction,” which reflects signs of motor incoordination, and includes tests, such as tandem walk and the finger-to-nose test; IV. “sequencing of complex motor acts,” which reflects the ability to perform complex motor sequences and includes tests such as the fist-ring and the fist-edge-palm. The scale was administered in a standardized manner as specified for each item and according to a fixed order. Each item is rated on a scale from 0 to 2 (0 no abnormality; 1 mild, but definite impairment; and 2 marked impairment), and snout and suck reflexes are scored either 0 or 2. NSS assessment was always performed by a physician blind to diagnosis and psychopathology, and inter-rater reliability was good (from r 0.81 to 0.93). We analyzed each sub-scale score separately, as this approach better represents the diversity of neurological dysfunction versus evaluation via global score (Buchanan and Heinrichs 1989). Data processing and statistical analyses T-test for continuous measures and Fisher’s exact test for categorical variables were used to compare the patients’ versus controls’ demographic and clinical characteristics. Startle reactivity was indexed by the mean amplitude of the first block of pulse-alone trials as well as by the mean amplitude of the first (unique) acoustic pulse-alone trial (Braff et al. 1999). PPI was calculated as the percent decrement in startle magnitude in the presence of prepulse, compared with the magnitude without the prepulse, %PPI=[(1 prepulse amplitude/pulse amplitude) 100]. For each participant, all blink response variables were averaged for each trial type (pulse alone, 30, 60, and 120 ms prepulse-pulse) within each block (block 1, 2, 3, and 4, as described above). We calculated PPI for block 2 as suggested by previous reports (Braff et al. 1992; Braff et al. 1999). To examine only consistent PPI measures, each trial was transformed into z-scores by considering each participant’s score mean and standard deviation. Values of 2 standard deviation (SD) were excluded from subsequent analyses. Participants presenting mean startle amplitude of 30 μV in any of the pulse-alone trial blocks were considered non-responders and were also therefore excluded from subsequent analyses. Since PPI has been found to be affected by gender (Kumari 2011) and smoking status (Kumari and Gray 1999),
Neurological soft signs and prepulse inhibition 3 these variables were used as covariates in an analysis of covariance (ANCOVA) of PPI data. Correlations among PPI, NSS, and social and clinical variables were yielded by Pearson’s coefficient. As suggested in a previous study (Dazzan et al. 2004), participants with low versus high NSS scores were identified using the median value of the corresponding dimension. All tests were bilateral at p 0.05. Statistical analyses were performed using SPSS 17.0. Results Twenty-six patients suffering from psychosis with a total duration of illness equal to or less than 5 years satisfied the inclusion criteria and were asked to participate in the study. Two refused to participate because they were in the acute illness phase, and six stable patients refused to participate, although they were asked for a total of three times at monthly intervals. Eighteen patients accepted to undergo PPI evaluation and to complete the clinical evaluations. Three of these were not Caucasian and were excluded from the study to avoid potential bias due to ethnicity, given that ethnic differences in PPI have been reported between African-Americans and European-Americans (Hasenkamp et al. 2008) and between Caucasians-Americans and Asians-Americans (Swerdlow et al. 2005). The patients’ PPI sample therefore consisted of 15 participants. Thirteen of these also accepted to undergo NES evaluation. Eighteen healthy controls were recruited through advertisements and accepted to undergo PPI evaluation and clinical assessment. Three were excluded for family history of mental disorders. Thus, 15 healthy control participants matched for gender, age, and years of education were included in the sample; these also accepted to undergo NES evaluation (see Figure 1). The participants’ clinical and demographic characteristics are presented in Table I. No differences emerged for smoker status, alcohol/drug consumption, age, education, or IQ between patients and controls. Patients exhibited more positive family history of other mental disorders than controls but none had a family history of psychosis. Prepulse inhibition Three healthy controls were subsequently excluded from the analysis: one due to equipment failure and two were considered non-responders according to our criteria. The non-responder rate (13.3% of the subjects) is consistent with previous findings obtained using the same cutoff criteria to discriminate non-responder subjects, among healthy individuals (Evans et al. 2005). The initial reactivity to the first (unique) acoustic pulsealone trail was not significantly different between patients and controls (t-test: 95.20 sd 48.32 vs. 67.85 sd 41.41, p 0.123 for all controls; 84.70 sd 30.24, p 0.548 for responders). The mean pulse-alone amplitude of the first block of pulsealone trials did not differ between patients (78.00 sd 31.74) and normal subjects (56.60 sd 30.72 for all controls; 68.34 sd 24.26 for responders) (t-test, p 0.083 for all controls; p 0.424 for responders).
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Figure 1. Recruitment process.
Figure 2 shows PPI (%) measured in three inter-stimulus interval (ISI) conditions (i.e., ISI 30, 60, and 120 ms) in patients versus controls. Patients exhibited slightly reduced PPI% in the 60- and 120-ms conditions, and controls showed
slightly reduced PPI% in the 30-ms condition. All of these differences, however, were not statistically significant. ANCOVA adjusted for the effect of gender and smoke did not show any significant effect.
Table I. Characteristics of the study subjects (mean SD). Patients Number of subjects Males Age (years) Years of education Premorbid IQ Handedness right Current smoker CAUS abstinent Use without impact on functioning CDUS abstinent Use without impact on functioning FIGS Positive for psychosis Positive for other mental disorders Age of onset (years) Months of illness Diagnosis (non-affective psychosis) Number of medicated patients (at the moment of the evaluation) Typical antipsychotics Atypical antipsychotics Unmedicated for antipsychotics Antipsychotics PDD/DDD lifetime 1 (number of patients) 1 (number of patients) 1 (number of patients) PANSS Total score Positive dimension score Negative dimension score General psychopathology score
15 9 35.6 10.8 11.3 4.1 101.2 10.7 15 6 3 12 13 2 4 8 31.3 10.3 54.7 15.3 11
Controls 15 9 35.8 12.2 12.7 3.9 106.5 7.4 14 5 6 9 14 1 0 0
p value (t-test) p 1.000a p 0.963 p 0.374 p 0.182 p 1.000a p 1.000a p 0.427a p 1.000a p 0.100a P 0.002a*
2 10 3 7 4 4 2.63 0.73 3.09 1.06 2.22 1.30 2.60 0.79
CAUS, Clinical Alcohol Use Scale; CDUS, Clinical Drug Use Scale; FIGS, Family Interview for Genetic Studies; PANSS, Positive and Negative syndrome Scale a
Fisher’s exact test.
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Table III. Comparison between patients and controls in the level SD of NSS dimensions and total score (t-test).
Primary signs Motor coordination Sensory integration Motor sequencing Total score
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Figure 2. PPI (%) measured in three conditions (ISI 30, 60, 120 ms) in patients and controls.
Correlations between PPI and social/clinical variables As shown in Table II, no significant correlations were found between PPI% and age, age of onset, illness duration (in months), PANSS subscale scores, and medication dosage, respectively. Neurological soft signs Table III shows the mean values for each NSS subscale. In comparison with controls, individuals with psychosis had significantly higher mean scores for total (p 0.000), primary (p 0.002), motor coordination (p 0.002), sensory integration (p 0.033), and motor sequencing (p 0.025) signs. Correlations between NSS and social and clinical variables Table IV illustrates the positive correlations yielded among primary signs, sensory integration, total score, and months of illness. Correlations between NSS sensory integration and total scores were observed for negative symptoms, general psychopathology, and total PANSS score, but not for the positive dimension. Moreover, correlations were observed between NSS primary signs and positive dimension, and between NSS motor sequencing and total and general psychopathology scores, as well.
Table II. Correlations between % of PPI in each condition, age, age of onset, months of illness, dosage of antipsychotics, and dimensions of the PANSS in the patients (n 15) (Pearson’s coefficient). %PPI Age Age of onset Months of illness PDD/DDD PANSS Total Positive dimension Negative dimension General psychopathology *p 0.05 **p 0.01
30 ms
60 ms
120 ms
0.243 0.249 0.27 0.156
0.103 0.052 0.397 0.059
0.047* 0.070 0.231 0.340
0.104 0.157 0.150 0.023
0.402 0.103 0.381 0.456
0.051 0.215 0.017* 0.008**
Patients (N 13)
Controls (N 15)
p value
6.5 3.3 1.8 1.5 2.9 1.5 2.1 2.2 13.5 6.1
2.8 1.8 0.6 0.2 1.6 1.5 0.5 0.9 5.1 3.0
p 0.002 p 0.002 p 0.033 p 0.025 p 0.000
Correlations between PPI and NSS Patients with higher NSS scores (above the median) were compared, in terms of PPI%, with patients presenting lower NSS levels (below the median). No significant differences were observed (Table V). The same comparison could not be conducted for healthy controls, due to their low NSS levels. Discussion To our knowledge, this is the first study exploring the association between the presence of NSS and PPI deficit in a sample of patients with early psychosis. However, at least in our sample, this association could not be detected. Our patients (who were clinically stable and mostly treated with atypical antipsychotics) showed a slight, but not significant PPI deficit at the 60-ms and 120-ms condition compared with healthy subjects. The absence of a significant difference between the two groups is in line with a previous study conducted on patients with early psychosis (Cadenhead 2011). Moreover, the presence of a mild, even not significant, PPI deficit among patients may be explained considering that our patients were mostly treated with atypical antipsychotics. Kumari et al. (2007b) detected a similar result on a sample of patients with schizophrenia treated with atypical antipsychotics, whereas those treated with typical medications exhibited a significant PPI deficit. This pattern of graded response (i.e., progressive increase in PPI from unmedicated patients through those medicated with typical antipsychotics to those under atypical ones) is especially strong in responders to a particular drug (Kumari et al. 2007b) and, in line with this, our patients were stable and responders to the pharmacological treatment received. This result would support the hypothesis the PPI deficit is a state marker for psychosis. Moreover, it has been suggested that medication status affects the relationship between symptoms and PPI in schizophrenia, as this association has been observed in unmedicated patients with schizophrenia only (Duncan et al. 2006). Consistently, PPI was not associated with negative symptoms. Regarding NSS, as reported in previous literature, our sample of patients with a duration of psychosis equal to or less than 5 years, has significantly higher mean NSS scores in comparison with controls—a result in accordance with previous studies conducted on patients with psychosis (Cuesta et al. 2002; Mohr et al. 2003; Arango et al. 1999; Venkatasubramanian et al. 2003). Specifically, we found higher levels of sensory integration signs in people with psychosis than in controls. These results confirm findings from a previous study testing NSS in people with chronic psycho-
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Table IV. Correlations between each subscale of NSS, age, age at onset, months of illness, dosage of antipsychotics, and psychopathology dimensions of the PANSS in the patients (n 13) (Pearson’s coefficient). NES subscales Primary signs
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Age Age at onset Months of illness PDD/DDD PANSS Total Positive dimension Negative dimension General psychopathology
0.110 0.049 0.622* 0.244 0.611 0.692** 0.539 0.547
Motor coordination 0.030 0.017 0.246 0.473
Sensory integration
Motor sequencing
Total score
0.209 0.275 0.600* 0.144
0.425 0.487 0.409 0.175
0.137 0.221 0.702** 0.354
0.850** 0.521 0.803** 0.882**
0.013 0.236 0.110 0.017
0.609* 0.252 0.632 0.628*
0.771** 0.540 0.753** 0.751**
*p 0.05 **p 0.01
sis (Cuesta et al. 2002), but are in contrast with another one conducted in a FEP sample (Dazzan et al. 2008), suggesting that sensory integrative deficits could become higher during the progression of psychosis. We also detected higher rates of motor sequencing signs confirming previous findings thereby (Cuesta et al. 2002). Given that higher motor sequencing scores have been found to be associated with lower cognitive ability in first-episode psychosis (Dazzan et al. 2008), we have selected a comparison group matched for premorbid IQ, so that we could ascertain that a similar result would not represent a false positive due to the general cognitive impairment which characterizes psychosis (Reichenberg et al. 2010). Since general cognitive ability (i.e., IQ) is not associated with PPI (Csomor et al. 2008; Giakoumaki et al. 2006), the selection of a comparison group matched for premorbid IQ do not represent a confounding factor for PPI measurement. With respect to primary and motor coordination scores, patients presented higher rates than controls, confirming findings from previous studies carried on patients with chronic schizophrenia (Mohr et al. 2003; Arango et al. 1999) and first-episode psychosis (Sanders et al. 1994; Bachmann et al. 2005; Mayoral et al. 2008). These results would support the hypothesis that NSS are trait markers for psychosis.
Furthermore, NSS were associated with negative symptoms in accordance with previous studies (Tosato and Dazzan 2005), and consistently with the hypothesis that “negative symptoms are the behavioral correlates of an underlying central nervous system disorder, possibly affecting the integrity of the frontal lobe” (Tosato and Dazzan 2005). Finally, given the involvement of the prefrontal cortex in both PPI and in NSS, as detected by imaging studies, we hypothesized that patients presenting sensory integration and motor sequencing signs would also have low level of PPI scores. We did not confirm this hypothesis in our sample of patients affected by early psychosis, and unfortunately it could also not be tested in healthy controls due to their low NSS levels. Our results would indicate the absence of an altered biological substrate underlying PPI in psychosis irrespective of the presence of an evident PPI deficit. It may be argued that antipsychotic treatment has restored the neurobiological substrate of PPI. This seems to be modulated by the cortico-striato-thalamic-pallido-pontine circuitry, as detected in animal studies (for reviews see Koch and Schnitzler 1997; Swerdlow et al. 2001). Imaging studies on healthy subjects and unmedicated patients suffering from schizophrenia revealed an association between PPI deficit
Table V. Comparison between patients with high (above median) versus low (below median NSS) in the mean % PPI SD (t-test). Patients High NSS (N 5)
%PPI 30 ms
60 ms
120 ms
Primary signs Sensory integration Motor sequencing Motor coordination Primary signs Sensory integration Motor sequencing Motor coordination Primary signs Sensory integration Motor sequencing Motor coordination
42.73 19.30 14.80 26.84 31.30 29.28 28.07 19.43 64.75 9.13 43.40 23.36 46.93 24.72 44.68 20.27 52.22 20.94 57.00 14.99 44.08 13.47 55.73 18.48
Low NSS (N 8) 73.56 24.46 41.26 24.42 30.98 28.81 32.43 31.67 48.45 28.32 61.79 22.49 58.18 23.93 59.18 24.88 61.03 20.47 58.04 23.97 63.66 20.37 58.49 22.02
p value p 0.247 p 0.094 p 0.986 p 0.806 p 0.245 p 0.185 p 0.454 p 0.331 p 0.470 p 0.925 p 0.110 p 0.832
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and gray matter volume in several brain areas involved in this circuitry (Hammer et al. 2013; Kumari et al. 2005). However, imaging studies conducted on medicated patients suffering from schizophrenia indicated an association only with reduced prefrontal cortical gray matter volume, independently by the antipsychotic treatment received (Kumari et al. 2008). These data suggest that while antipsychotic treatment may restore the neurobiological substrate of PPI, prefrontal cortex volume seems to remain altered in medicated patients. Frontal/prefrontal cortex is associated with the presence of sensory integration and motor sequencing signs as well (Dazzan et al. 2004; Venkatasubramanian et al. 2008). However, these signs were found to also be associated with other cortical region volume (e.g., temporal) and with subcortical structures volume (e.g., putamen, globus pallidus, and thalamus) both in drug-naïve and in medicated patients with psychosis (Dazzan et al. 2004; Venkatasubramanian et al. 2008). These findings suggest that other brain areas’ alterations may give rise to the presence of NSS in our sample. Thus, prefrontal cortex alteration may be a common biological substrate underlying both PPI and NSS but this may not be sufficient to determinate an association between the two biomarkers, possibly due to the involvement of other cerebral regions. The main limitation of this study is the small sample of subjects that we adopted due to difficulties in recruiting patients available to undergo both PPI and NSS examinations; on the other hand, to reduce the influence of confounding variables we selected only Caucasian patients and we matched them with control subjects for gender, age, years of education, and IQ. Conclusion The present article represents a pilot study, which interestingly explores a possible association between the presence of NSS and PPI deficit in early psychosis. Our results support the hypothesis that NSS are trait markers, whereas PPI is a state marker and that the putative common biological substrate is not sufficient to determinate an association between the two biomarkers. Although our explorative analysis failed to detect any association, in light of the considerations reported herein this hypothesis needs further investigation. Key Points • Prepulse inhibition (PPI) of the startle reflex deficit and neurological soft signs (NSS) are two markers of vulnerability to psychosis. • PPI and NSS may share a common biological substrate, consequently PPI deficits may be associated with the presence of NSS • Moreover PPI deficits and NSS may be associated with negative symptoms. • In our sample, patients did not exhibit higher PPI deficits but only higher NSS rates as compared with healthy controls. • Higher NSS rates were not associated with PPI deficits, NSS sensory integration signs correlated positively with negative symptoms.
•
Our study supported the hypothesis that NSS are trait markers whereas PPI deficits state markers, and that their putative common biological substrate is not sufficient to determine an association between them.
Ethical considerations This study is conducted according to globally accepted standards of good clinical practice, in agreement with the Declaration of Helsinki and in keeping with local regulations. Formal ethical approval for conducting the study has been sought and obtained by the Ethics Committee “Comitato Etico per la Sperimentazione, Azienda Ospedaliera Universitaria Integrata di Verona” (http://www.ospedaleuniverona.it/Istituzionale/Comitati/Sperimentazione/), which has approved the study protocol, the information sheets (patient and control versions), and the informed consent sheets (patient and control versions) on January 27th, 2010 (Prot. N. 5513/CE, Date: February 4th, 2010). Acknowledgements We are grateful to all participants who contributed to the study. We also thank Mr. Agostino Scaglia for his technical support. The project was supported by the grant “Promoting research to improve quality of care: The Verona WHO Centre for mental health research’’ from Fondazione Cariverona to Professor Tansella and Professor Ruggeri. Dr. Ira was funded by the FSE grant “Acquisizione nuove tecniche automatizzate di studio di immagini di risonanza magnetica nucleare cerebrali” from the Veneto Region. Statement of interest None to declare. References Aggernaes B, Glenthoj BY, Ebdrup BH, Rasmussen H, Lublin H, Oranje B. 2010. Sensorimotor gating and habituation in antipsychotic-naive, first-episode schizophrenia patients before and after 6 months’ treatment with quetiapine. Int J Neuropsychopharmacol 13:1383–1395. Amaddeo F, Tansella M. 2009. Information systems for mental health. Epidemiol Psichiatr Soc 18:1–4. Arango C, Bartko JJ, Gold JM, Buchanan RW. 1999. Prediction of neuropsychological performance by neurological signs in schizophrenia. Am J Psychiatry 156:1349–1357. Bachmann S, Bottmer C, Schröder J. 2005. Neurological soft signs in first-episode schizophrenia: a follow-up study. Am J Psychiatry 162:2337–2343. Boks MP, Selten JP, Leask S, Van den Bosch RJ. 2006. The 2-year stability of neurological soft signs after a first episode of non-affective psychosis. Eur Psychiatry 21:288–290. Bonora LI, Conti L, Piccinelli L, Tansella M, Cassano GB, Lecrubier Y. 1995. Mini International Neuropsychiatric Interview (Mini), Italian version, Le scale di valutazione in Psichiatria. In: Conti L, editor. Digest Neuropsichiatria Milano: Utet. Braff DL, Grillon C, Geyer MA. 1992. Gating and habituation of the startle reflex in schizophrenic patients. Arch Gen Psychiatry 49:206–215. Braff DL, Swerdlow NR, Geyer MA. 1999. Symptom correlates of prepulse inhibition deficits in male schizophrenic patients. Am J Psychiatry 156:596–602. Buchanan RW, Heinrichs DW. 1989. The Neurological Evaluation Scale (NES): a structured instrument for the assessment of neurological signs in schizophrenia. Psychiatry Res. 27:335–350.
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Cadenhead KS. 2011. Startle reactivity and prepulse inhibition in prodromal and early psychosis: effects of age, antipsychotics, tobacco and cannabis in a vulnerable population. Psychiatry Res 188:208–216. Chan RC, Xu T, Heinrichs RW, Yu Y, Wang Y. 2010. Neurological soft signs in schizophrenia: a meta-analysis. Schizophr Bull 36:1089–104. Chen EY, Hui CL, Chan RC, Dunn EL, Miao MY, Yeung WS, et al. 2005. A 3-year prospective study of neurological soft signs in firstepisode schizophrenia. Schizophr Res 75:45–54. Cuesta MJ, Peralta V, Zarzuela A, Calvo R, García M, Serrano F. 2002. Neurological soft-signs in psychosis: threshold criteria for discriminating normal controls and for predicting cognitive impairment. Schizophr Res 58:263–271. Csomor PA, Stadler RR, Feldon J, Yee BK, Geyer MA, Vollenweider FX. 2008. Haloperidol differentially modulates prepulse inhibition and p50 suppression in healthy humans stratified for low and high gating levels. Neuropsychopharmacology 33:497–512. Dazzan P, Lloyd T, Morgan KD, Zanelli J, Morgan C, Orr K, et al. 2008. Neurological abnormalities and cognitive ability in first-episode psychosis. Br J Psychiatry 193:197–202. Dazzan P, Morgan KD, Orr KG, Hutchinson G, Chitnis X, Suckling J, et al. 2004. The structural brain correlates of neurological soft signs in AESOP first-episode psychoses study. Brain 127:143–153. Dazzan P, Murray RM. 2002. Neurological soft signs in first-episode psychosis: a systematic review. Br J Psychiatry 43:50–57. Duncan EJ, Bollini AM, Lewison B, Keyes M, Jovanovic T, Gaytan O, et al. 2006. Medication status affects the relationship of symptoms to prepulse inhibition of acoustic startle in schizophrenia. Psychiatry Res 145:137–145. Evans LH, Gray NS, Snowden RJ. 2005. Prepulse inhibition of startle and its moderation by schizotypy and smoking. Psychophysiology 42:223–231. Geyer MA, Swerdlow NR, Mansbach RS, Braff DL. 1990. Startle response models of sensorimotor gating and habituation deficits in schizophrenia. Brain Res Bull 25:485–498. Giakoumaki SG, Bitsios P, Frangou S. 2006. The level of prepulse inhibition in healthy individuals may index cortical modulation of early information processing. Brain Res 1078:168–170. Goff DC, Evins AE. 1998. Negative symptoms in schizophrenia: neurobiological models and treatment response. Harv Rev Psychiatry 6:59–77. Goghari VM, Sponheim SR, MacDonald AW III. 2010. The functional neuroanatomy of symptom dimensions in schizophrenia: a qualitative and quantitative review of a persistent question. Neurosci Biobehav Rev 34:468–486. Hammer TB, Oranje B, Skimminge A, Aggernæs B, Ebdrup BH, Glenthøj B, Baaré W. 2013. Structural brain correlates of sensorimotor gating in antipsychotic-naive men with first-episode schizophrenia. J Psychiatry Neurosci 38:34–42. Hasenkamp W, Norrholm SD, Green A, Lewison B, Boshoven W, Keyes M, Duncan E. 2008. Differences in startle reflex and prepulse inhibition in European-Americans and African-Americans. Psychophysiology 45:876–882. Hong LE, Wonodi I, Lewis J, Thaker GK. 2008. Nicotine effect on prepulse inhibition and prepulse facilitation in schizophrenia patients. Neuropsychopharmacology 33:2167–2174. Kay SR, Fiszbein A, Opler LA, 1987. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 13:261–276. Koch M, Schnitzler HU 1997. The acoustic startle response in rats–circuits mediating evocation, inhibition and potentiation. Behavioural Brain Res 89:35–49. Kumari V, Gray JA. 1999. Smoking withdrawal, nicotine dependence and prepulse inhibition of the acoustic startle reflex. Psychopharmacology 141:11–15. Kumari V, Soni W, Sharma T 1999. Normalization of information processing deficits in schizophrenia with clozapine. Am J Psychiatry 156:1046–1051. Kumari V, Antonova E, Zachariah E, Galea A, Aasen I, Ettinger U, et al. 2005. Structural brain correlates of prepulse inhibition of the
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acoustic startle response in healthy humans. Neuroimage 26:1052–1058. Kumari V, Fannon D, Sumich AL, Sharma T. 2007a. Startle gating in antipsychotic-naïve first episode schizophrenia patients: one ear is better than two. Psychiatry Res 151:21–28. Kumari V, Antonova E, Geyer MA, Fytche D, Williams SC, Sharma T. 2007b. A fMRI investigation of startle gating deficits in schizophrenia patients treated with typical or atypical antipsychotics. Int J Neuropsychopharmacol 10:463–477. Kumari V, Fannon D, Geyer MA, Premkumar P, Antonova E, Simmons A, Kuipers E. 2008. Cortical grey matter volume and sensorimotor gating in schizophrenia. Cortex 44:1206–1214. Kumari V. 2011. Sex differences and hormonal influences in human sensorimotor gating: implications for schizophrenia. Curr Top Behav Neurosci 8:141–154. Maxwell ME. 1992. Family Interview for Genetic Studies (FIGS): Manual For FIGS, Clinical Neurogenetics Branch, Intramural Research Program, National Institute of Mental Health. Bethesda, MD. Mayoral M, Bombín I, Zabala A, Robles O, Moreno D, Parellada M, et al. 2008. Neurological soft signs in adolescents with first episode psychosis: two-year follow up. Psychiatry Res 161:344–348. Ludewig K, Geyer MA, Vollenweider FX. 2003. Deficits in prepulse inhibition and habituation in never-medicated, first-episode schizophrenia. Biol Psychiatry 54:121–128. Mackeprang T, Kristiansen KT, Glenthoj BY. 2002. Effects of antipsychotics on prepulse inhibition of the startle response in drug-naive schizophrenic patients. Biol Psychiatry 52:863–873. Meincke U, Mörth D,Voss T, Thelen B, Geyer MA, Gouzoulis-Mayfrank E. 2004. Prepulse inhibition of the acoustically evoked startle reflex in patients with an acute schizophrenic psychosis–a longitudinal study. Eur Arch Psychiatry Clin Neurosci 254:415–421. Minassian A, Feifel D, Perry W. 2007. The relationship between sensorimotor gating and clinical improvement in acutely ill schizophrenia patients. Schizophrenia Res 89:225–231. Mohr F, Hubmann W, Albus M, Franz U, Hecht S, Scherer J, et al. 2003. Neurological soft signs and neuropsychological performance in patients with first episode schizophrenia. Psychiatry Res 121:21–30. Mueser KT, Nishith P, Tracy JI, DeGirolamo J, Molinaro M. 1995. Expectations and motives for substance use in schizophrenia. Schizophr Bull. 21:367–378. Negash A, Kebede D, Alem A, Melaku Z, Deyessa N, Shibire T, et al. 2004. Neurological soft signs in bipolar I disorder patients. J Affect Disord 80:221–230. Nelson HE. 1991. National Adult Reading Test, 2nd edition. Windsor, UK: NFER-Nelson. Reichenberg A. 2010. The assessment of neuropsychological functioning in schizophrenia. Dialogues Clin Neurosci 12:383–392. Sanders RD, Keshavan MS, Schooler NR. 1994. Neurological examination abnormalities in neuroleptic-naive patients with first-break schizophrenia: preliminary results. Am J Psychiatry 151:1231–1233. Sartori G, Colombo L, Vallar G, Rusconi ML, Pinarello A. 1997. T.I.B.: Test di Intelligenza Breve per la valutazione del quoziente intellettivo attuale e pre-morboso. La Professione di Psicologo. 1:II–XXIV. Smieskova R, Fusar-Poli P, Allen P, Bendfeldt K, Stieglitz RD, Drewe J, et al. 2010. Neuroimaging predictors of transition to psychosis–a systematic review and meta-analysis. Neurosci Biobehav Rev 34:1207–1222. Spitzer RL, Williams JBW, Gibbon M, First MB. 1993. SCID Intervista strutturata per il DSMIII-R (Italian version by Fava M, Guaraldi GP, Mazzi F, Rigatelli M.) Organizzazioni Speciali, Firenze. Swerdlow NR, Geyer MA, Braff DL. 2001. Neural circuit regulation of prepulse inhibition of startle in the rat: current knowledge and future challenges. Psychopharmacology 156:194–215. Swerdlow NR, Talledo JA, Braff DL. 2005. Startle modulation in Caucasian-Americans and Asian-Americans: a prelude to genetic/ endophenotypic studies across the ’Pacific Rim’. Psychiatr Genet 15:61–65. Thaker G. 2008. Psychosis endophenotypes in schizophrenia and bipolar disorder. Schizophr Bull 34:720–721.
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Tosato S, Dazzan P. 2005. The psychopathology of schizophrenia and the presence of neurological soft signs: a review. Curr Opin Psychiatry 18:285–288. Venkatasubramanian G, Jayakumar PN, Gangadhar BN, Keshavan MS. 2008. Neuroanatomical correlates of neurological soft signs in antipsychotic-naive schizophrenia. Psychiatry Res 164:215–222. Venkatasubramanian G, Latha V, Gangadhar BN, Janakiramaiah N, Subbakrishna DK, Jayakumar PN, Keshavan MS. 2003. Neurological
Neurological soft signs and prepulse inhibition 9 soft signs in never-treated schizophrenia. Acta Psychiatr Scand 108:144–146. Whitty P, Clarke M, McTigue O, Browne S, Gervin M, Kamali M, et al. 2006. Diagnostic specificity and predictors of neurological soft signs in schizophrenia, bipolar disorder and other psychoses over the first 4 years of illness. Schizophr Res 86:110–117. World Health Organization. 1992. Schedules for Clinical Assessment in Neuropsychiatry (Version 1.0). Geneva, CH: WHO.
ORIGINAL ARTICLE
Neurological soft signs and prepulse inhibition of the startle reflex in psychosis: A pilot study
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Elisa Ira1, Chiara Bonetto1, Martina Zanoni1, Alessandro Bottacini1, Rodolfo Mazzoncini1, Lisa Martini1, Angela Garozzo1, Antonio Lasalvia2, Michele Tansella1, Mirella Ruggeri1 & Sarah Tosato1 1
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Department of Public Health and Community Medicine, Section of Psychiatry, University of Verona, Italy and 2Unit of Psychiatry, University Hospital of Verona, Verona, Italy
Abstract Objective. Prepulse inhibition (PPI) of the startle reflex deficit and neurological soft signs (NSS) are two markers of vulnerability to psychosis. This study investigated the possibility of a PPI–NSS relation due to a putative common biological substrate, hypothesizing that patients with higher NSS scores also show higher PPI deficits. Moreover, we examined the possibility of an association of PPI deficits and NSS with negative symptoms. Methods. Fifteen subjects with psychosis and fifteen healthy controls underwent PPI and NSS evaluations. Results. Patients did not exhibit higher PPI deficits but only higher NSS rates (p 0.01), as compared with healthy controls. Higher NSS rates were not associated with PPI deficits, and NSS sensory integration signs correlated positively with negative symptoms (p 0.01). Conclusion. Our study supported the hypothesis that NSS are trait markers whereas PPI deficits state markers and that their putative common biological substrate is not sufficient to determinate an association between them. The study hypothesis, however, needs further investigation. Key words: PPI, NSS, sensory integration signs, motor sequencing signs, schizophrenia (Received 28 April 2014; accepted 29 January 2015)
Objective Prepulse inhibition (PPI) of the acoustic startle reflex is an operational measure of sensorimotor gating (Geyer et al. 1990): PPI refers to the normal unlearned reduction in the amplitude of the startle reflex occurring when a weak stimulus (the prepulse) precedes an intense auditory startle-eliciting stimulus (pulse). Reduced PPI has long been considered a biomarker for vulnerability to schizophrenia (Thaker 2011). Recently, evidence have suggested that PPI may be influenced by the course of the illness and by the pharmacologic treatment received (Meincke et al. 2004; Minassian et al. 2007), pointing out to the importance of conducting studies on patients at early phase of illness. Relatively few studies have been conducted on early phase of schizophrenia (Aggernaes et al. 2010; Mackeprang et al. 2002; Ludewig et al. 2003; Meincke et al. 2004; Kumari et al. 2007a; Quednow et al. 2008), finding that antipsychotics may partially restore PPI deficit (Kumari et al. 2007a), which was mostly detected in drug-naïve subjects. Only one study has been conducted on individuals with first-episode psychosis and did not find a PPI deficit (Cadenehead 2011). Consequently, PPI may be considered a state marker of psychosis and since evidence are not conclusive; studies on patients with a short duration of illness and pharmacological treatment are encouraged. The brain correlate of PPI deficit in psychosis is still unclear: there is evidence of the major role played by Correpsondence: Dr Sarah Tosato, Department of Public Health and Community Medicine, Section of Psychiatry, University of Verona, Policlinico G.B. Rossi, P.le L.A. Scuro 10, 37134 Verona, Italy. Tel: 39 (045) 812 4441. Fax: 39 (045) 8124155. E-mail: [email protected]
reduced prefrontal cortex volume in contributing to this deficit (Kumari et al. 2008). The finding is significant because this brain area has been found to be reduced in psychosis (Smieskova et al. 2010) and its altered functioning and volume is thought to be linked to the emergence of negative symptoms (Goff and Evins 1998; Goghari et al. 2010). Interestingly, frontal/prefrontal cortex reduction is involved in another deficit which characterizes psychosis, neurological soft signs (NSS) (Dazzan and Murray 2002; Cuesta et al. 2002; Negash et al. 2004; Whitty et al. 2006; Chan et al. 2010). NSS are minor neurological deficits clustering in four main domains (primary neurological signs, sensory integration, motor coordination, and sequencing of complex motor acts signs), reflecting the involvement of different functional areas thereby (Buchanan and Heinrichs 1989). Specifically, sensory integration signs and motor sequencing signs have shown an association with frontal/prefrontal cortex reduction as detected by MRI studies conducted on patients with early psychosis (Dazzan et al. 2004; Venkatasubramanian et al. 2008). These results indicate a certain degree of overlapping between brain regions underlying PPI and NSS; therefore, they strongly suggest that sensory integration and motor sequencing signs may be associated with PPI deficits, perhaps due to prefrontal cortex alterations. If this association was true, this would be worth of interest since evidence suggest that NSS may be considered trait markers for psychosis (Chen et al. 2005; Boks et al. 2006). Thus, this association would indicate the presence of a disrupted biological substrate underlying PPI in psychosis irrespective of the presence of an evident PPI deficit.
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Therefore, we conducted a pilot study, in order to evaluate the presence of a PPI and neurological deficits on a sample of patients with early psychosis, hypothesizing that PPI deficits would be associated with high levels of sensory integration and motor sequencing signs. Moreover, as prefrontal alteration has been associated with negative symptoms, we investigated the potential association between these symptoms and both NSS and PPI deficits.
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Methods Participants The sample consisted of a cohort of psychotic patients with a total duration of illness equal to or less than 5 years, in contact with the South Verona Community-based Mental Health Service, Italy. Cases identified at intake by the South Verona Psychiatric Register (Amaddeo and Tansella 2009) were assigned a research diagnosis of psychosis using the Item Group Checklist of the Schedule for Clinical Assessment in Neuropsychiatry (WHO 1992). Diagnosis was obtained by two senior psychiatrists who had previously received a formal training (S.T. and A.L.); they used all information available in the clinical records, integrated with interviews to the patients’ case managers if needed. Only patients with a confirmed ICD-10 diagnosis of psychosis, either non-affective or affective (F20–29; F30.2, F31.2, F31.5, F31.6, F32.3, F33.3) were included in the present study. Patients presenting a history of neurological disease, prior electro-convulsivant treatment, prior traumatic brain injury, or mental retardation (IQ 70) were excluded from the study. Additionally, pregnant, lactating, or menstruating women were not recruited. Moreover, for the present study, a healthy control group matched for gender, age, years of education, and ethnicity was recruited through notices posted at the University Hospital of Verona, Verona, Italy. Furthermore, since higher NSS motor sequencing scores have been found to be associated with lower cognitive ability in first-episode psychosis (Dazzan et al. 2008), we have selected a comparison group of healthy subjects matched for premorbid IQ. Exclusion criteria were the same as those adopted for the psychiatric patients. The absence of psychiatric disorders was ascertained via two schedules: the Mini International Neuropsychiatric Interview (M.I.N.I. Plus) (Bonora et al. 1995), to exclude any psychiatric disorder in Axis I; and the Structured Clinical Interview for DSM disorders (SCID-II) (Spitzer et al. 1993), to exclude any psychiatric disorder in Axis II. All participants signed informed consent forms. Clinical assessment A set of standardized instruments was used to collect clinical information. For the patient group only, a semi-structured interview and the Positive and Negative Syndrome Scale (PANSS) (Kay et al. 1987) were used to evaluate severity of psychotic symptoms; the scale yields positive symptoms, negative symptoms, and general psychopathology scale scores. In all participants, premorbid IQ was estimated with the Italian version of the National Adult Reading Test (Nelson
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1991; Sartori et al. 1997). A semi-structured questionnaire was used to collect information on tobacco consumption, and the Clinician Alcohol and Drug Use Scale (CAUS) (Mueser et al. 1995) was used to assess alcohol and drug consumption. Moreover, family history of mental illness was assessed with the Family Interview for Genetic Studies (FIGS) (Maxwell 1992). All researchers involved in the assessments received specific training. An inter-rater reliability session was held to test the consistency of evaluation among the researchers. The inter-rater reliability for PANSS was 0.90 (Cronbach’s Alpha). Startle response measurement Firstly, all participants’ hearing was tested by audiometer: anyone unable to detect tones at 45 dB at 500, 1000, or 6000 Hz was excluded. Participants were seated comfortably in an armchair during hearing test and the whole experiment. A commercially available electromyography (EMG) startle system (EMG SR-LAB, San Diego Instrument, San Diego, CA, USA) was used to examine the eyeblink component of the acoustic startle response. Participants were asked to keep their eyes open during the experiment. They were told that the experiment was to measure their reaction to a number of noise bursts, but they received no specific instructions to attend to these or to ignore them. Since nicotine has been found to modulate PPI (Hong et al. 2008), all subjects were required to refrain from smoking cigarets for at least one hour before testing. The eyeblink component of the acoustic startle response was indexed by recording the EMG activity of the right orbicularis oculi muscle after positioning two miniature electrodes (In Vivo Metric, Healdsburg, CA) filled with conductive gel. A ground electrode was placed behind the right ear over the mastoid. All electrode resistances were less than 10 kOhms. EMG activity was band-pass filtered (30–300 kHz). Rectified EMG was digitalized and stored every millisecond from the onset of the startling stimulus until 250-ms post stimulus. Acoustic startle and prepulse stimuli were presented binaurally through headphones (Model EC-9A 6cc, Coll Health, Mississauga, ON). The session began with a 5-min acclimation period of 70-dB background noise, which continued throughout the session, followed by four trial blocks. Block 1 consisted of five pulse-alone trials at an intensity of 115 B and a duration of 40 ms. Blocks 2 and 3 consisted of 40 trials each, containing 8 pulse-alone, 8 no-stimulus trials, and 24 prepulse-pulse trials presented in pseudorandom order (8 presentations of each of the four types of prepulse trials, described below). The prepulse stimuli consisted of 20 ms of 86-dB white noise, which preceded the startle by 30, 60, or 120 ms. Similarly to Block 1, Block 4 consisted of five pulse-alone trials. The EMG data were inspected on a trial-to-trial basis (to exclude person-specific erroneous trials) and were scored by the system’s analytic program for response amplitude (in arbitrary analog-to-digit units). Neurological soft signs An expanded, previously validated version of the Neurological Evaluation Scale (NES) (Buchanan and Heinrichs 1989)
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was used to assess NSS. The NES is a structured scale with 26 items presenting scores as pertaining to four main subscales reflecting different functional areas and showing good construct validity (Buchanan and Heinrichs 1989). The subscales are as follows: I. “primary neurological dysfunction,” which reflects a dysfunction that can be identified by a standard neurological examination, and includes the cranial nerves, eye movement, lateralizing limb pyramidal signs, and frontal release signs; II. “sensory integration dysfunction,” which reflects a dysfunction in the integration of sensory information and includes signs such as right/left confusion, astereognosis, agraphesthesia, and audiovisual integration; III. “motor coordination dysfunction,” which reflects signs of motor incoordination, and includes tests, such as tandem walk and the finger-to-nose test; IV. “sequencing of complex motor acts,” which reflects the ability to perform complex motor sequences and includes tests such as the fist-ring and the fist-edge-palm. The scale was administered in a standardized manner as specified for each item and according to a fixed order. Each item is rated on a scale from 0 to 2 (0 no abnormality; 1 mild, but definite impairment; and 2 marked impairment), and snout and suck reflexes are scored either 0 or 2. NSS assessment was always performed by a physician blind to diagnosis and psychopathology, and inter-rater reliability was good (from r 0.81 to 0.93). We analyzed each sub-scale score separately, as this approach better represents the diversity of neurological dysfunction versus evaluation via global score (Buchanan and Heinrichs 1989). Data processing and statistical analyses T-test for continuous measures and Fisher’s exact test for categorical variables were used to compare the patients’ versus controls’ demographic and clinical characteristics. Startle reactivity was indexed by the mean amplitude of the first block of pulse-alone trials as well as by the mean amplitude of the first (unique) acoustic pulse-alone trial (Braff et al. 1999). PPI was calculated as the percent decrement in startle magnitude in the presence of prepulse, compared with the magnitude without the prepulse, %PPI=[(1 prepulse amplitude/pulse amplitude) 100]. For each participant, all blink response variables were averaged for each trial type (pulse alone, 30, 60, and 120 ms prepulse-pulse) within each block (block 1, 2, 3, and 4, as described above). We calculated PPI for block 2 as suggested by previous reports (Braff et al. 1992; Braff et al. 1999). To examine only consistent PPI measures, each trial was transformed into z-scores by considering each participant’s score mean and standard deviation. Values of 2 standard deviation (SD) were excluded from subsequent analyses. Participants presenting mean startle amplitude of 30 μV in any of the pulse-alone trial blocks were considered non-responders and were also therefore excluded from subsequent analyses. Since PPI has been found to be affected by gender (Kumari 2011) and smoking status (Kumari and Gray 1999),
Neurological soft signs and prepulse inhibition 3 these variables were used as covariates in an analysis of covariance (ANCOVA) of PPI data. Correlations among PPI, NSS, and social and clinical variables were yielded by Pearson’s coefficient. As suggested in a previous study (Dazzan et al. 2004), participants with low versus high NSS scores were identified using the median value of the corresponding dimension. All tests were bilateral at p 0.05. Statistical analyses were performed using SPSS 17.0. Results Twenty-six patients suffering from psychosis with a total duration of illness equal to or less than 5 years satisfied the inclusion criteria and were asked to participate in the study. Two refused to participate because they were in the acute illness phase, and six stable patients refused to participate, although they were asked for a total of three times at monthly intervals. Eighteen patients accepted to undergo PPI evaluation and to complete the clinical evaluations. Three of these were not Caucasian and were excluded from the study to avoid potential bias due to ethnicity, given that ethnic differences in PPI have been reported between African-Americans and European-Americans (Hasenkamp et al. 2008) and between Caucasians-Americans and Asians-Americans (Swerdlow et al. 2005). The patients’ PPI sample therefore consisted of 15 participants. Thirteen of these also accepted to undergo NES evaluation. Eighteen healthy controls were recruited through advertisements and accepted to undergo PPI evaluation and clinical assessment. Three were excluded for family history of mental disorders. Thus, 15 healthy control participants matched for gender, age, and years of education were included in the sample; these also accepted to undergo NES evaluation (see Figure 1). The participants’ clinical and demographic characteristics are presented in Table I. No differences emerged for smoker status, alcohol/drug consumption, age, education, or IQ between patients and controls. Patients exhibited more positive family history of other mental disorders than controls but none had a family history of psychosis. Prepulse inhibition Three healthy controls were subsequently excluded from the analysis: one due to equipment failure and two were considered non-responders according to our criteria. The non-responder rate (13.3% of the subjects) is consistent with previous findings obtained using the same cutoff criteria to discriminate non-responder subjects, among healthy individuals (Evans et al. 2005). The initial reactivity to the first (unique) acoustic pulsealone trail was not significantly different between patients and controls (t-test: 95.20 sd 48.32 vs. 67.85 sd 41.41, p 0.123 for all controls; 84.70 sd 30.24, p 0.548 for responders). The mean pulse-alone amplitude of the first block of pulsealone trials did not differ between patients (78.00 sd 31.74) and normal subjects (56.60 sd 30.72 for all controls; 68.34 sd 24.26 for responders) (t-test, p 0.083 for all controls; p 0.424 for responders).
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Figure 1. Recruitment process.
Figure 2 shows PPI (%) measured in three inter-stimulus interval (ISI) conditions (i.e., ISI 30, 60, and 120 ms) in patients versus controls. Patients exhibited slightly reduced PPI% in the 60- and 120-ms conditions, and controls showed
slightly reduced PPI% in the 30-ms condition. All of these differences, however, were not statistically significant. ANCOVA adjusted for the effect of gender and smoke did not show any significant effect.
Table I. Characteristics of the study subjects (mean SD). Patients Number of subjects Males Age (years) Years of education Premorbid IQ Handedness right Current smoker CAUS abstinent Use without impact on functioning CDUS abstinent Use without impact on functioning FIGS Positive for psychosis Positive for other mental disorders Age of onset (years) Months of illness Diagnosis (non-affective psychosis) Number of medicated patients (at the moment of the evaluation) Typical antipsychotics Atypical antipsychotics Unmedicated for antipsychotics Antipsychotics PDD/DDD lifetime 1 (number of patients) 1 (number of patients) 1 (number of patients) PANSS Total score Positive dimension score Negative dimension score General psychopathology score
15 9 35.6 10.8 11.3 4.1 101.2 10.7 15 6 3 12 13 2 4 8 31.3 10.3 54.7 15.3 11
Controls 15 9 35.8 12.2 12.7 3.9 106.5 7.4 14 5 6 9 14 1 0 0
p value (t-test) p 1.000a p 0.963 p 0.374 p 0.182 p 1.000a p 1.000a p 0.427a p 1.000a p 0.100a P 0.002a*
2 10 3 7 4 4 2.63 0.73 3.09 1.06 2.22 1.30 2.60 0.79
CAUS, Clinical Alcohol Use Scale; CDUS, Clinical Drug Use Scale; FIGS, Family Interview for Genetic Studies; PANSS, Positive and Negative syndrome Scale a
Fisher’s exact test.
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Table III. Comparison between patients and controls in the level SD of NSS dimensions and total score (t-test).
Primary signs Motor coordination Sensory integration Motor sequencing Total score
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Figure 2. PPI (%) measured in three conditions (ISI 30, 60, 120 ms) in patients and controls.
Correlations between PPI and social/clinical variables As shown in Table II, no significant correlations were found between PPI% and age, age of onset, illness duration (in months), PANSS subscale scores, and medication dosage, respectively. Neurological soft signs Table III shows the mean values for each NSS subscale. In comparison with controls, individuals with psychosis had significantly higher mean scores for total (p 0.000), primary (p 0.002), motor coordination (p 0.002), sensory integration (p 0.033), and motor sequencing (p 0.025) signs. Correlations between NSS and social and clinical variables Table IV illustrates the positive correlations yielded among primary signs, sensory integration, total score, and months of illness. Correlations between NSS sensory integration and total scores were observed for negative symptoms, general psychopathology, and total PANSS score, but not for the positive dimension. Moreover, correlations were observed between NSS primary signs and positive dimension, and between NSS motor sequencing and total and general psychopathology scores, as well.
Table II. Correlations between % of PPI in each condition, age, age of onset, months of illness, dosage of antipsychotics, and dimensions of the PANSS in the patients (n 15) (Pearson’s coefficient). %PPI Age Age of onset Months of illness PDD/DDD PANSS Total Positive dimension Negative dimension General psychopathology *p 0.05 **p 0.01
30 ms
60 ms
120 ms
0.243 0.249 0.27 0.156
0.103 0.052 0.397 0.059
0.047* 0.070 0.231 0.340
0.104 0.157 0.150 0.023
0.402 0.103 0.381 0.456
0.051 0.215 0.017* 0.008**
Patients (N 13)
Controls (N 15)
p value
6.5 3.3 1.8 1.5 2.9 1.5 2.1 2.2 13.5 6.1
2.8 1.8 0.6 0.2 1.6 1.5 0.5 0.9 5.1 3.0
p 0.002 p 0.002 p 0.033 p 0.025 p 0.000
Correlations between PPI and NSS Patients with higher NSS scores (above the median) were compared, in terms of PPI%, with patients presenting lower NSS levels (below the median). No significant differences were observed (Table V). The same comparison could not be conducted for healthy controls, due to their low NSS levels. Discussion To our knowledge, this is the first study exploring the association between the presence of NSS and PPI deficit in a sample of patients with early psychosis. However, at least in our sample, this association could not be detected. Our patients (who were clinically stable and mostly treated with atypical antipsychotics) showed a slight, but not significant PPI deficit at the 60-ms and 120-ms condition compared with healthy subjects. The absence of a significant difference between the two groups is in line with a previous study conducted on patients with early psychosis (Cadenhead 2011). Moreover, the presence of a mild, even not significant, PPI deficit among patients may be explained considering that our patients were mostly treated with atypical antipsychotics. Kumari et al. (2007b) detected a similar result on a sample of patients with schizophrenia treated with atypical antipsychotics, whereas those treated with typical medications exhibited a significant PPI deficit. This pattern of graded response (i.e., progressive increase in PPI from unmedicated patients through those medicated with typical antipsychotics to those under atypical ones) is especially strong in responders to a particular drug (Kumari et al. 2007b) and, in line with this, our patients were stable and responders to the pharmacological treatment received. This result would support the hypothesis the PPI deficit is a state marker for psychosis. Moreover, it has been suggested that medication status affects the relationship between symptoms and PPI in schizophrenia, as this association has been observed in unmedicated patients with schizophrenia only (Duncan et al. 2006). Consistently, PPI was not associated with negative symptoms. Regarding NSS, as reported in previous literature, our sample of patients with a duration of psychosis equal to or less than 5 years, has significantly higher mean NSS scores in comparison with controls—a result in accordance with previous studies conducted on patients with psychosis (Cuesta et al. 2002; Mohr et al. 2003; Arango et al. 1999; Venkatasubramanian et al. 2003). Specifically, we found higher levels of sensory integration signs in people with psychosis than in controls. These results confirm findings from a previous study testing NSS in people with chronic psycho-
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Table IV. Correlations between each subscale of NSS, age, age at onset, months of illness, dosage of antipsychotics, and psychopathology dimensions of the PANSS in the patients (n 13) (Pearson’s coefficient). NES subscales Primary signs
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Age Age at onset Months of illness PDD/DDD PANSS Total Positive dimension Negative dimension General psychopathology
0.110 0.049 0.622* 0.244 0.611 0.692** 0.539 0.547
Motor coordination 0.030 0.017 0.246 0.473
Sensory integration
Motor sequencing
Total score
0.209 0.275 0.600* 0.144
0.425 0.487 0.409 0.175
0.137 0.221 0.702** 0.354
0.850** 0.521 0.803** 0.882**
0.013 0.236 0.110 0.017
0.609* 0.252 0.632 0.628*
0.771** 0.540 0.753** 0.751**
*p 0.05 **p 0.01
sis (Cuesta et al. 2002), but are in contrast with another one conducted in a FEP sample (Dazzan et al. 2008), suggesting that sensory integrative deficits could become higher during the progression of psychosis. We also detected higher rates of motor sequencing signs confirming previous findings thereby (Cuesta et al. 2002). Given that higher motor sequencing scores have been found to be associated with lower cognitive ability in first-episode psychosis (Dazzan et al. 2008), we have selected a comparison group matched for premorbid IQ, so that we could ascertain that a similar result would not represent a false positive due to the general cognitive impairment which characterizes psychosis (Reichenberg et al. 2010). Since general cognitive ability (i.e., IQ) is not associated with PPI (Csomor et al. 2008; Giakoumaki et al. 2006), the selection of a comparison group matched for premorbid IQ do not represent a confounding factor for PPI measurement. With respect to primary and motor coordination scores, patients presented higher rates than controls, confirming findings from previous studies carried on patients with chronic schizophrenia (Mohr et al. 2003; Arango et al. 1999) and first-episode psychosis (Sanders et al. 1994; Bachmann et al. 2005; Mayoral et al. 2008). These results would support the hypothesis that NSS are trait markers for psychosis.
Furthermore, NSS were associated with negative symptoms in accordance with previous studies (Tosato and Dazzan 2005), and consistently with the hypothesis that “negative symptoms are the behavioral correlates of an underlying central nervous system disorder, possibly affecting the integrity of the frontal lobe” (Tosato and Dazzan 2005). Finally, given the involvement of the prefrontal cortex in both PPI and in NSS, as detected by imaging studies, we hypothesized that patients presenting sensory integration and motor sequencing signs would also have low level of PPI scores. We did not confirm this hypothesis in our sample of patients affected by early psychosis, and unfortunately it could also not be tested in healthy controls due to their low NSS levels. Our results would indicate the absence of an altered biological substrate underlying PPI in psychosis irrespective of the presence of an evident PPI deficit. It may be argued that antipsychotic treatment has restored the neurobiological substrate of PPI. This seems to be modulated by the cortico-striato-thalamic-pallido-pontine circuitry, as detected in animal studies (for reviews see Koch and Schnitzler 1997; Swerdlow et al. 2001). Imaging studies on healthy subjects and unmedicated patients suffering from schizophrenia revealed an association between PPI deficit
Table V. Comparison between patients with high (above median) versus low (below median NSS) in the mean % PPI SD (t-test). Patients High NSS (N 5)
%PPI 30 ms
60 ms
120 ms
Primary signs Sensory integration Motor sequencing Motor coordination Primary signs Sensory integration Motor sequencing Motor coordination Primary signs Sensory integration Motor sequencing Motor coordination
42.73 19.30 14.80 26.84 31.30 29.28 28.07 19.43 64.75 9.13 43.40 23.36 46.93 24.72 44.68 20.27 52.22 20.94 57.00 14.99 44.08 13.47 55.73 18.48
Low NSS (N 8) 73.56 24.46 41.26 24.42 30.98 28.81 32.43 31.67 48.45 28.32 61.79 22.49 58.18 23.93 59.18 24.88 61.03 20.47 58.04 23.97 63.66 20.37 58.49 22.02
p value p 0.247 p 0.094 p 0.986 p 0.806 p 0.245 p 0.185 p 0.454 p 0.331 p 0.470 p 0.925 p 0.110 p 0.832
Neurological soft signs and prepulse inhibition 7
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DOI: 10.3109/13651501.2015.1016041
and gray matter volume in several brain areas involved in this circuitry (Hammer et al. 2013; Kumari et al. 2005). However, imaging studies conducted on medicated patients suffering from schizophrenia indicated an association only with reduced prefrontal cortical gray matter volume, independently by the antipsychotic treatment received (Kumari et al. 2008). These data suggest that while antipsychotic treatment may restore the neurobiological substrate of PPI, prefrontal cortex volume seems to remain altered in medicated patients. Frontal/prefrontal cortex is associated with the presence of sensory integration and motor sequencing signs as well (Dazzan et al. 2004; Venkatasubramanian et al. 2008). However, these signs were found to also be associated with other cortical region volume (e.g., temporal) and with subcortical structures volume (e.g., putamen, globus pallidus, and thalamus) both in drug-naïve and in medicated patients with psychosis (Dazzan et al. 2004; Venkatasubramanian et al. 2008). These findings suggest that other brain areas’ alterations may give rise to the presence of NSS in our sample. Thus, prefrontal cortex alteration may be a common biological substrate underlying both PPI and NSS but this may not be sufficient to determinate an association between the two biomarkers, possibly due to the involvement of other cerebral regions. The main limitation of this study is the small sample of subjects that we adopted due to difficulties in recruiting patients available to undergo both PPI and NSS examinations; on the other hand, to reduce the influence of confounding variables we selected only Caucasian patients and we matched them with control subjects for gender, age, years of education, and IQ. Conclusion The present article represents a pilot study, which interestingly explores a possible association between the presence of NSS and PPI deficit in early psychosis. Our results support the hypothesis that NSS are trait markers, whereas PPI is a state marker and that the putative common biological substrate is not sufficient to determinate an association between the two biomarkers. Although our explorative analysis failed to detect any association, in light of the considerations reported herein this hypothesis needs further investigation. Key Points • Prepulse inhibition (PPI) of the startle reflex deficit and neurological soft signs (NSS) are two markers of vulnerability to psychosis. • PPI and NSS may share a common biological substrate, consequently PPI deficits may be associated with the presence of NSS • Moreover PPI deficits and NSS may be associated with negative symptoms. • In our sample, patients did not exhibit higher PPI deficits but only higher NSS rates as compared with healthy controls. • Higher NSS rates were not associated with PPI deficits, NSS sensory integration signs correlated positively with negative symptoms.
•
Our study supported the hypothesis that NSS are trait markers whereas PPI deficits state markers, and that their putative common biological substrate is not sufficient to determine an association between them.
Ethical considerations This study is conducted according to globally accepted standards of good clinical practice, in agreement with the Declaration of Helsinki and in keeping with local regulations. Formal ethical approval for conducting the study has been sought and obtained by the Ethics Committee “Comitato Etico per la Sperimentazione, Azienda Ospedaliera Universitaria Integrata di Verona” (http://www.ospedaleuniverona.it/Istituzionale/Comitati/Sperimentazione/), which has approved the study protocol, the information sheets (patient and control versions), and the informed consent sheets (patient and control versions) on January 27th, 2010 (Prot. N. 5513/CE, Date: February 4th, 2010). Acknowledgements We are grateful to all participants who contributed to the study. We also thank Mr. Agostino Scaglia for his technical support. The project was supported by the grant “Promoting research to improve quality of care: The Verona WHO Centre for mental health research’’ from Fondazione Cariverona to Professor Tansella and Professor Ruggeri. Dr. Ira was funded by the FSE grant “Acquisizione nuove tecniche automatizzate di studio di immagini di risonanza magnetica nucleare cerebrali” from the Veneto Region. Statement of interest None to declare. References Aggernaes B, Glenthoj BY, Ebdrup BH, Rasmussen H, Lublin H, Oranje B. 2010. Sensorimotor gating and habituation in antipsychotic-naive, first-episode schizophrenia patients before and after 6 months’ treatment with quetiapine. Int J Neuropsychopharmacol 13:1383–1395. Amaddeo F, Tansella M. 2009. Information systems for mental health. Epidemiol Psichiatr Soc 18:1–4. Arango C, Bartko JJ, Gold JM, Buchanan RW. 1999. Prediction of neuropsychological performance by neurological signs in schizophrenia. Am J Psychiatry 156:1349–1357. Bachmann S, Bottmer C, Schröder J. 2005. Neurological soft signs in first-episode schizophrenia: a follow-up study. Am J Psychiatry 162:2337–2343. Boks MP, Selten JP, Leask S, Van den Bosch RJ. 2006. The 2-year stability of neurological soft signs after a first episode of non-affective psychosis. Eur Psychiatry 21:288–290. Bonora LI, Conti L, Piccinelli L, Tansella M, Cassano GB, Lecrubier Y. 1995. Mini International Neuropsychiatric Interview (Mini), Italian version, Le scale di valutazione in Psichiatria. In: Conti L, editor. Digest Neuropsichiatria Milano: Utet. Braff DL, Grillon C, Geyer MA. 1992. Gating and habituation of the startle reflex in schizophrenic patients. Arch Gen Psychiatry 49:206–215. Braff DL, Swerdlow NR, Geyer MA. 1999. Symptom correlates of prepulse inhibition deficits in male schizophrenic patients. Am J Psychiatry 156:596–602. Buchanan RW, Heinrichs DW. 1989. The Neurological Evaluation Scale (NES): a structured instrument for the assessment of neurological signs in schizophrenia. Psychiatry Res. 27:335–350.
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