Suboptimal phenotypic reliability impedes reproducible human neuroscience

Abstract

Summary Paragraph Biomarkers of behavior and psychiatric illness for cognitive and clinical neuroscience remain out of reach 1–4 . Suboptimal reliability of biological measurements, such as functional magnetic resonance imaging (fMRI), is increasingly cited as a primary culprit for discouragingly large sample size requirements and poor reproducibility of brain-based biomarker discovery 1,5–7 . In response, steps are being taken towards optimizing MRI reliability and increasing sample sizes 8–11 , though this will not be enough. Optimizing biological measurement reliability and increasing sample sizes are necessary but insufficient steps for biomarker discovery; this focus has overlooked the ‘other side of the equation’ - the reliability of clinical and cognitive assessments - which are often suboptimal or unassessed. Through a combination of simulation analysis and empirical studies using neuroimaging data, we demonstrate that the joint reliability of both biological and clinical/cognitive phenotypic measurements must be optimized in order to ensure biomarkers are reproducible and accurate. Even with best-case scenario high reliability neuroimaging measurements and large sample sizes, we show that suboptimal reliability of phenotypic data (i.e., clinical diagnosis, behavioral and cognitive measurements) will continue to impede meaningful biomarker discovery for the field. Improving reliability through development of novel assessments of phenotypic variation is needed, but it is not the sole solution. We emphasize the potential to improve the reliability of established phenotypic methods through aggregation across multiple raters and/or measurements 12–15 , which is becoming increasingly feasible with recent innovations in data acquisition (e.g., web- and smart-phone-based administration, ecological momentary assessment, burst sampling, wearable devices, multimodal recordings) 16–20 . We demonstrate that such aggregation can achieve better biomarker discovery for a fraction of the cost engendered by large-scale samples. Although the current study has been motivated by ongoing developments in neuroimaging, the prioritization of reliable phenotyping will revolutionize neurobiological and clinical endeavors that are focused on brain and behavior.