Translating neurophysiological biomarkers into clinical tools: A psychometric blueprint illustrated with the error-related negativity.

Clinical neuroscience seeks reliable biomarkers for psychiatric diagnosis, prognosis, and treatment, but translation has stalled because replication is inconsistent, theory is incomplete, and links to psychological processes are unclear. These shortcomings largely stem from inadequate attention to psychometric principles. This review focuses on event-related potentials and shows how assessment of reliability and validity, as well as optimization and standardization, can support the development of actionable biomarkers. Biomarker development can falter when measures are adapted from basic research protocols that emphasize within-person contrasts and minimize between-person variance, a strategy poorly suited to examining individual differences. Many biomarkers show poor internal and test-retest reliability when used to distinguish individuals or predict clinical outcomes, especially in patient populations in which data are more variable. Furthermore, the validity of any biological measure depends on well-articulated causal models linking brain activity to psychological phenomena. A roadmap, guided by the U.S. Food and Drug Administration and the National Institutes of Health Biomarkers, EndpointS, and other Tools resource, aligns psychometric work with analytic validation, clinical validation, and context-of-use qualification. This framework is illustrated with the error-related negativity (ERN), an event-related potential that has progressed from basic cognitive research to a prognostic biomarker for anxiety. Priorities for ERN development include meeting high reliability thresholds, optimizing tasks and pipelines for clinical samples, and harmonizing acquisition and analysis to support cross-site generalization. Although the focus of the review is on ERN, the principles apply broadly to all biological measures. The proposed process for guiding biomarker evaluation through psychometrics will pave the way for better selection of biomarkers, ultimately improving their clinical utility in precision medicine. (PsycInfo Database Record (c) 2025 APA, all rights reserved).