Integrative precision-medicine approach to cognitive assessment in older adults

Brain-behavior interactions are imperative across a wide range of medical practices and are commonly probed by neuropsychological assessments. These assessments are carried out in order to support diagnosis of congenital or acquired neurological conditions or to assist in treatment planning or tracking. Recent advancements include standardization of tools and norms, the development of computer-based tests, and incorporation of test performance with neuroimaging tools for clinical prediction. However, current practices are challenged by cultural biases, time-consuming procedures, and relying on commercial testing tools. These factors may limit assessments’ quality and prevalence in clinical and research settings, widening health disparities and hindering scientific advancement. In the era of precision medicine, optimizing neuropsychological assessments is a necessity. The current chapter will suggest how and why a precision medicine approach should be applied in the field of neuropsychological assessment, and how applying this framework will increase sensitivity and specificity of existing diagnoses and improve treatment selection and outcome. By using an illustrative case study, we describe current discrepancies and suggest ways to implement and embrace the following changes: (1) explore behavior using a dimensional cross-diagnostic approach integrating different information levels (e.g., biology, cognition, and behavior); (2) study brain atrophy and brain connectivity as potent markers of brain-behavior interactions; (3) adopt an open-science approach by creating open databases with neuroanatomical and behavioral records of different patient populations; and (4) incorporate technology in the assessment by using computerized tests and ecological momentary assessment tools to complement the traditional tests and measures. Combining novel neurocognitive indexes with advanced genomics and state-of-the-art neuroimaging will allow creating individual profiles spanning the brain-behavior interaction. These profiles would guide personalized diagnostic and prognostic treatment strategies.