A roadmap for the development and validation of event-related potential biomarkers in schizophrenia research

Abstract

New efforts to develop treatments for cognitive dysfunction in mental illnesses would benefit enormously from biomarkers that provide sensitive and reliable measures of the neural events underlying cognition. Here, we evaluate the promise of event-related potentials (ERPs) as biomarkers of cognitive dysfunction in schizophrenia. We conclude that ERPs have several desirable properties: (1) they provide a direct measure of electrical activity during neurotransmission; (2) their high temporal resolutions make it possible to measure neural synchrony and oscillations; (3) they are relatively inexpensive and convenient to record; (4) animal models are readily available for several ERP components; (5) decades of research has established the sensitivity and reliability of ERP measures in psychiatric illnesses; and 6) feasibility of large N (>500) multisite studies has been demonstrated for key measures. Consequently, ERPs may be useful for identifying endophenotypes and defining treatment targets, for evaluating new compounds in animals and in humans, and for identifying individuals who are good candidates for early interventions or for specific treatments. However, several challenges must be overcome before ERPs gain widespread use as biomarkers in schizophrenia research, and we make several recommendations for the research that is necessary to develop and validate ERP-based biomarkers that can have a real impact on treatment development.

Figure 1

Example mismatch negativity results. While the subjects read a book, brief tones were presented at a rate of approximately 1 Hz, with 80% standard tones (1000 Hz) and 20% deviant tones (1004, 1008, 1016, or 1032 Hz). The mismatch negativity is isolated by differences waves (right column) in which the ERP elicited by the standard stimulus is subtracted from the ERP elicited by the deviant stimulus. (Reprinted with permission from reference 69, copyright 2010 by Risto Näätänen and Kairi Kreegipuu, all rights reserved).

Figure 2

Example of individual differences in ERPs. Waveforms are shown from 9 healthy control subjects (HC1 – HC9), along with the average across the subjects (AVG). These waveforms were elicited by the oddballs in a visual oddball paradigm (data from 70). Note especially the differences in the P2 latency range (shaded area).