Especially notable are impairments in identifying and responding to stimuli that are either salient or novel.The P300 event-related potential is a physiological index of the cognitive processes elicited by such task-relevant or novel deviant stimuli . Generators of the P300 are widely distributed across both cortex and subcortex. However, the scalp-recorded P300 response to an auditory target stimulus – also referred to as P3b to differentiate it from the P3a orienting response to novelty – arises primarily from inferior parietal and supramarginal cortical regions . Reduced amplitude of this response is one of the most robust physiological abnormalities associated with schizophrenia, being replicated across hundreds of studies with a relatively large effect size . Although P300 amplitude varies to a certain extent with fluctuations in clinical symptomatology , it also exhibits characteristics of a trait-like disease marker. The measure has relatively high test–retest reliability , and the patient deficit persists regardless of the level of acute symptomatology or psychotropic medication . There is also strong evidence implicating a genetic contribution to P300 amplitude in both healthy subjects and patients . Thus, P300 amplitude meets the essential criteria for a schizophrenia endophenotype . Historically, an impediment to the utility of the P300 as a schizophrenia biomarker has been its lack of specificity. P300 is disrupted in a variety of neuropsychiatric disorders associated with cognitive impairment.
These include, among others, Alzheimer’s disease , alcoholism and affective illness . Additionally, the P300 response is influenced by a variety of factors,cannabis grow equipment independent of clinical diagnosis. Most notable among these are age and sex; P300 amplitude is smaller, overall, in men and exhibits both reduced amplitude and prolonged latency in the elderly.P300 amplitude is smaller in active smokers compared to nonsmokers, and this decrement reflects both the current number of cigarettes smoked per day and the number of years of continuous smoking . It is similarly reduced following acute administration of marijuana , alcohol , and cocaine , and among chronic users of these substances . In contrast, the effect of antipsychotic medications, among schizophrenia patients, appears to be relatively inconsequential . Another potentially important factor that has only rarely been considered is race or ethnicity, and the little evidence that exists concerning this has been inconclusive . Given the frequent co-occurrence of schizophrenia with other co-morbid neuropsychiatric and substance use disorders, as well as the differences in smoking prevalence and racial stratification that is often found in schizophrenia patient vs. healthy control samples, a comprehensive understanding of the impact of such modulating factors is critical to enhancing the utility of P300 as a disease-specific schizophrenia biomarker. Recently, with the advent of studies of the schizophrenia prodrome, the importance of the P300 as a putative biomarker has taken on added significance. There is now substantial evidence that P300 amplitude is reduced in “high-risk” individuals with prodromal symptoms, prior to illness onset . Importantly, within a high-risk cohort, P300 appears to be a sensitive predictor of which individuals will, in fact, transition to frank psychosis.
Moreover, the degree of impairment indicates the proximity of illness onset . The greater the magnitude of the amplitude reduction, the more likely that psychosis onset is imminent. P300 amplitude assessment may, therefore, play an important part in the clinical evaluation of at-risk individuals. However P300 studies have thus far been confined primarily to academic neurophysiology laboratories, and data analyses have been limited primarily to between-group comparisons of measures acquired in one laboratory under identical conditions. It is thus unclear whether specific values obtained in one experimental setting can be compared or co-mingled with values obtained in another setting under less-than-identical conditions. The ability to aggregate quantitative data across multiple sites is critical to the strategy employed by the Consortium on the Genetics of Schizophrenia to identify the genetic substrates of disease endophenotypes. It is also critical to the utility of this measure as a specific predictive biomarker of impending psychosis. The purpose of the current analysis was therefore, first, to determine if a standardized P300 data acquisition system could be successfully deployed to multiple settings without on-site electrophysiology expertise. We assessed the overall usability of the ERP data and the ability to detect known schizophrenia deficits. We also considered the consistency of measurements across five COGS-2 sites, and examined various socio-demographic modulating factors that can contribute to measurement variability. As noted, a careful understanding of the quantitative impact of these modulating factors is an important prerequisite to any interpretation of a specific set of measurements.Healthy control subjects and schizophrenia patients were enrolled in the COGS-2 endophenotype study at 5 sites, as detailed in the introductory article of this Special Issue . Briefly, all participants were assessed using a modified version of the Structured Clinical Interview for DSM-IV-TR , Modules A–E .
All patients met criteria for either SZ or schizo affective disorder, depressed sub-type. HCS were excluded for any history of a psychotic disorder in either themselves or a 1stdegree relative, a current Axis I mood disorder, a Cluster A Axis II disorder, or current psychoactive medication use. Subjects were also excluded for any medical or neurological condition that could interfere with endophenotype assessment, history of substance abuse in the past month or substance dependence in the past 6 months, or a positive toxicology screen at the time of testing. Clinical and demographic characteristics of the P300 sample are presented, by site, in Table 1. Specific past substance related diagnoses are detailed in Table 2. Adjunctive psychoactive medications used by patients are listed in Table 3. In addition to the Structured Clinical Interview and the various endophenotype measures, all subjects were assessed with the Mini Mental Status Examination and the Global Assessment of Functioning Scale . Patients were further assessed with two measures of functional capacity: the 15-item clinician rated Scale of Functioning , and the UCSD Performance-based Skills Assessment-Brief , which directly assesses an individual’s capacity in multiple domains of everyday functioning through the use of props and standardized skill performance tests. There were significant group differences in age , sex and racial composition . There were also significant site differences for each of these measures, and group × site interactions for age and sex. As expected, patients and controls differed on education, GAF, and MMSE scores. There were also significant main effects of site and group × site interactions for both GAF and MMSE. Site differences were also evident for rates of past substance use, major depressive disorder, and nicotine use. Among patients, site differences were observed for duration of illness, age of onset, and current symptomatology . This variability indicates that different sites drew their samples from different socio-demographic and clinical pools.limitations of the 2-channel recording system and the need to dedicate one of these to the EOG. Subjects were seated in front of a computer monitor and directed to fixate their gaze on the center screen. A hearing test was conducted to ensure N40 dB hearing threshold bilaterally at 1000 Hz. Subjects were instructed to press the button on a hand-held counter whenever they heard a 1500 Hz target tone amid a stream of 1000 Hz tones. They were given a brief practice period to ensure initial task comprehension and compliance. Unfortunately,cannabis plant growing the experimental hardware did not allow further real-time monitoring of button-press responses over the course of the experiment. Subjects were then presented a series of 400 tones, including 62 random targets. The ERP system digitally sampled the EEG at 1000 Hz and wrote 1400 ms of data for each stimulus trial, beginning at 100 ms prior to stimulus onset.
At the conclusion of the experiment, the number of button presses was recorded from the counter.Raw EEG data from all 5 COGS-2 sites were uploaded to a centralized database. Quality assurance data review and analysis was then conducted by a single investigator who was blind to all demographic and diagnostic information. EEG data were processed using Brain Vision Analyzer 1.5 . Data were digitally filtered between 0.1 and 30 Hz and eye movement artifact was removed using an automated algorithm . Intervals with additional EEG artifact were excluded from further analysis. Remaining trials were then sorted and combined to form separate average ERP wave forms for the target and frequent tone conditions. These were baseline corrected relative to the 100 ms pre-stim interval and visually inspected to determine the presence or absence of reliably identifiable ERP components. A highly conservative, stringent, approach to data inclusion was employed. Data without an unambiguous N100/P200 response to the frequent tone, or a reliably identifiable P300 response to the target were excluded. Subjects were also excluded if their target count deviated by more than 20 from the actual number of 62, regardless of the quality of EEG data, since appropriate task engagement could not be documented. For the remaining subjects, P300 amplitude and latency were measured from the peak target response between 250 and 400 ms. .P300 amplitude and latency differences were analyzed in two separate general linear models , with diagnosis, sex and test site as categorical factors and age as a continuous predictor. Significant main effects and interactions were parsed with post-hoc paired contrasts. The effects of various modulating factors were then initially assessed by adding these individually as separate additional factors to the original model. To rule out potential false positive results from multiple tests of different modulators, all significant modulating variables were then included in one omnibus regression model to evaluate the collective and individual residual effects of these multiple factors. Associations between P300 and clinical factors, in the patient sample, were examined via Pearson correlation coefficients, with a significance threshold of p b 0.01.We examined the effects of several potential modulating variables, to determine how they independently influenced P300 amplitude and whether they contributed to observed site differences. Each variable was added as a single additional factor to the original multi-factorial model. MMSE score [F = 4.79. p b 0.05] was positively associated with P300 amplitude independent of diagnosis, but had no impact on observed site differences. Other measures of cognitive and functional status – GAF score and education – were not significant predictors of P300. Smoking, however, was found to be a robust modulator of P300 amplitude [F = 10.34, p b 0.01]. Although the interaction between smoking and diagnosis was not significant [F = 2.44, p = 0.12], separate within-group analyses of smokers and nonsmokers revealed a significant patient-control difference only among nonsmokers [F = 29.69, p b 0.00001]. Smoking differentially reduced P300 amplitude in healthy control subjects while having little effect in patients , which eliminated all diagnostic differences [F = 1.09, p = 0.30]. However, site differences remained robust even after controlling for smoking status. It should be noted, though, that only 70 control subjects were classified as smokers, compared to 50% of patients. The observed site differences appeared to primarily reflect racial stratification differences. Inclusion of race as an additional predictor produced a significant race effect [F = 16.29, p b 0.000001], which eliminated the site effect while leaving the effects of both diagnosis [F = 20.64, p b 0.00001] and age [F = 15.56, p b 0.0001] intact. P300 amplitude was lower, overall, in the African American sample than in either the Caucasian or “Other/Mixed” racial groupings. There was a clear trend towards an interaction of race × diagnosis, but it did not reach statistical significance [F = 2.85, p = .06]. In separate analyses, significant patient-control differences were observed within each racial subgroup, although the effect size was noticeably smaller within the African American sample . The differential impact of race on the association between schizophrenia and P300 was manifested primarily as an amplitude reduction among African American controls, rather than patients. Further consideration of potential modulating variables revealed that this apparent racial difference was due, in part, to the differential impact across the racial groupings of prior substance use disorders. When the sample was restricted to subjects with no history of substance use, the interaction of race × diagnosis was insignificant [F = 1.56, p = 0.21] and the magnitude of the patient-control difference was similar across racial categories .