Event-Related Potentials in the Clinical High-Risk (CHR) State for Psychosis: A Systematic Review. (July 2018)

• Record Type: Journal Article
• Authors: Lepock, Jennifer R.; Mizrahi, Romina; Korostil, Michele; Bagby, R. Michael; Pang, Elizabeth W.; Kiang, Michael
• Is Part Of: Clinical EEG and neuroscience. Volume 49:Number 4(2018)
• Page Start: 215
• Page End: 225
• Publication Date: 2018-07
• Subjects: prodrome -- event-related potentials -- mismatch negativity (MMN) -- P300 -- psychosis; Electroencephalography -- Periodicals; Neurosciences -- Periodicals; 616.8047547
• DOI: 10.1177/1550059418755212
• Languages: English
• ISSNs: 1550-0594

Abstract

There is emerging evidence that identification and treatment of individuals in the prodromal or clinical high-risk (CHR) state for psychosis can reduce the probability that they will develop a psychotic disorder. Event-related brain potentials (ERPs) are a noninvasive neurophysiological technique that holds promise for improving our understanding of neurocognitive processes underlying the CHR state. We aimed to systematically review the current literature on cognitive ERP studies of the CHR population, in order to summarize and synthesize the results, and their implications for our understanding of the CHR state. Across studies, amplitudes of the auditory P300 and duration mismatch negativity (MMN) ERPs appear reliably reduced in CHR individuals, suggesting that underlying impairments in detecting changes in auditory stimuli are a sensitive early marker of the psychotic disease process. There are more limited data indicating that an earlier-latency auditory ERP response, the N100, is also reduced in amplitude, and in the degree to which it is modulated by stimulus characteristics, in the CHR population. There is also evidence that a number of auditory ERP measures (including P300, MMN and N100 amplitudes, and N100 gating in response to repeated stimuli) can further refine our ability to detect which CHR individuals are most at risk for developing psychosis. Thus, further research is warranted to optimize the predictive power of algorithms incorporating these measures, which could help efforts to target psychosis prevention interventions toward those most in need.