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

Proposed criteria for candidate translational neurophysiologic biomarkers have admittedly high standards for established psychometric properties and functional characteristics, applicable to both infra-human and human versions of the measures. Early “target engagement” identified in single-dose or limited-dose experimental medicine designs is both feasible [2, 4, 5] and particularly valuable but may not generalize across settings. Since the type and calibration of equipment, as well as data processing and analysis methods all can have a substantial impact on psychometric properties, criteria established on one testing platform may not be applicable to another. For example, results obtained from high-density EEG recordings with advanced signal processing algorithms that leverage spatiotemporal relationships for sophisticated artifact reduction and analysis may not generalize to lower-fidelity recording systems with much more limited signal processing options.

While preclinical assays are often used in specialized laboratories, validation of human response homology tested in less controlled, real-world clinical trial environments is an important next-step for validation. Some translational neurophysiologic measures already fulfill all or many of the Table 1 criteria, including mismatch negativity (MMN), P3a, auditory steady state response (ASSR) and prepulse inhibition of startle (PPI), and have been used effectively in experimental medicine designs [2, 4, 5] and multi-site consortia [6]. Notably, even high-density EEG assessments can be feasibly scaled up for valid use in multi-center trials with proper training and centralized data processing and management.

We propose an initial set of criteria to guide development of neurophysiologic biomarkers for predicting psychotherapeutic sensitivity. These criteria offer the potential to advance treatments for major brain disorders beyond the “one-size-fits-all” approach based on fuzzy diagnostic categories, towards a more precise, personalized, and biologically informed strategy for matching CNS interventions with sensitive patient subgroups.

Funding and Disclosure

Research reported in this publication was supported by the Sidney R. Baer, Jr. Foundation, the Brain and Behavior Research Foundation, Department of Veterans Affairs VISN-22 Desert-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), National Institute of Mental Health (MH59803, MH94320) and National Institute of Aging (AG059640). G.A.L. reports having been a consultant to Astellas, Boehringer-Ingelheim, Heptares, NeuroSig, Neuroverse, and the National Aeronautics and Space Administration (NASA). The funding organizations had no role in the preparation, review, or approval of the paper; and decision to submit the paper for publication.