3252 Neuroclinical fingerprint of high-risk psychosis
Authors: Novak Keisha , Kotov Roman , Foti Dan 
Published: March 2019

OBJECTIVES/SPECIFIC AIMS: The study aims to utilize event-related potentials (ERPs) coupled with observable reports of symptoms to comprehensively understand neurological and symptomatic profile of individuals at risk for developing psychosis. The study is a short-term longitudinal design which allows for examination of course as well as structure of illness. The primary outcome is to map known neuroclinical deficits among individuals with schizophrenia onto a high-risk, non-clinical sample. A secondary aim of the study is to demonstrate prediction of symptom severity over time measured by a combination of ERPs and clinical symptom scores. METHODS/STUDY POPULATION: Recruited participants are pre-screened for eligibility via telephone interview. This process includes administration of Community Assessment of Psychotic Experiences (CAPE), and the Mini International Neuropsychiatric Interview (MINI). During in-person lab assessment, participants provide written informed consent and complete a battery of ERP tasks, semi-structured clinical interviews, and self-report questionnaires that assess for presence and severity of sub-threshold psychotic-like experiences. Six months following the laboratory visit, participants will be provided a link to online questionnaires that were completed during laboratory visit in order to reassess presence and severity. RESULTS/ANTICIPATED RESULTS: The target number of participants included in this study is 60. We hope to recruit individuals who range in symptom severity as measured by CAPE. It is of interest to determine relationship among known deficits in individuals with schizophrenia and individuals exhibiting sub-clinical symptoms of psychosis. Additionally, we plan to examine ERPs and symptoms together as a “profile” of high risk psychosis, yielding more robust information about this population than any one ERP or symptom measure alone. The within subjects design of this study allows for examination of symptom progression and potential prediction of symptoms based on brain activity. Many studies examine only single ERP components thus limiting the ability to draw broader conclusions regarding general cognitive frameworks among populations. We use a combination of well-validated ERPs (i.e. P300, N400, ERN) with behavioral and symptom data in order to predict variation in symptoms over the course of 6 months. The project aims to take a novel approach at identifying high-risk profiles based on neurophysiological and behavioral data and using this as a basis for predicting symptom severity across time. DISCUSSION/SIGNIFICANCE OF IMPACT: Individuals endorsing psychotic-like experiences are at heightened risk for developing a psychotic disorder in the future, and have been linked with similar social, behavioral, and emotional risk factors similar to those of schizophrenia. Subjective data (e.g. self-report, interview) sheds light on important information regarding observable symptom manifestation; however, neural measures can detect relatively subtle deficits in information processing that precede and predict overt symptom onset, which necessitates other important methodological considerations. Specifically, extant literature has shown that quantifiable indices of cognitive deficits may represent a vulnerability to psychosis in high-risk populations, and can be measured using event-related potentials (ERPs). This study integrates a psychophysiological approach by mapping neural deficits from schizophrenia onto a high-risk sample. Many studies examine only single ERP components thus limiting the ability to draw broader conclusions regarding general cognitive frameworks among populations. We use a combination of well-validated ERPs (i.e. P300, N400, ERN) with behavioral and symptom data in order to predict variation in symptoms over the course of 6 months. The project aims to take a novel approach at identifying high-risk profiles based on neurophysiological and behavioral data and using this as a basis for predicting symptom severity across time. We will parse heterogeneity within a high-risk group in order to create innovative profiles and potentially predict variation in course of symptoms. In other words, a “fingerprint” physiologic aberration may be exhibited within high-risk individuals and can be used as biomarkers to identify those at risk even before onset of observable symptoms.