In recent years, the field of psychiatric neuroscience has generated numerous biological markers that have contributed to our understanding of central nervous system disorders. Despite the abundance of available “biomarkers” [1] and improved understanding of pathophysiology, drugs for the treatment of Alzheimer’s Disease, schizophrenia, and other CNS disorders continue to fail at high rates and at substantial cost (e.g., aducanab and encenicline). In the preclinical stages of development, many assays, including those that purport to measure the same underlying cognitive constructs across species, have limited evidence for predicting responses in humans. Promising drugs graduate from preclinical to later clinical stages of development and are tested on broad diagnosis-based cohorts, without consideration of individual variations in the brain functions that govern treatment sensitivity. This “one-size-fits-all” approach limits the ability to differentiate treatment-sensitive individuals who may be hidden among non-responders in conventional group level analyses [2].

To address these and other limitations, NIH and/or FDA have established future research frameworks (e.g., Research Domain Criteria) and called for translational biomarkers that can rapidly detect treatment sensitivity and/or early response to interventions and thereby accelerate the development of novel therapeutics [1]. Based on prior review processes for selecting cognitive tests for clinical trials [3], we propose an expanded set of criteria (Table 1) for neurophysiologic measures that can be broadly used across multiple categories of biomarkers and surrogate endpoints including pharmacodynamic/response, predictive, prognostic, monitoring, and susceptibility/risk [1].

Table 1 Proposed criteria for neurophysiologic biomarkers
Full size table

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