Predictive formulae for brain health assessment in various neurological and psychological conditions

This cross-diagnostic, physiology-informed predictive modeling study investigates the integration of brain function–based physiological assessments with neuro-biomarker analyses to develop predictive models for comprehensive brain health evaluation. A total of 76 participants with diverse neurological and psychological conditions were enrolled. The primary objective was to construct and validate regression-based models incorporating cognitive performance, sensory functions (vision and hearing), emotional well-being, motor abilities, and aging as independent variables, with neuro-biomarkers serving as dependent outcomes. Participants underwent standardized evaluations of cognition, sensory perception, affective status, and motor function. Concurrently, peripheral blood samples were analyzed for serum levels of neurofilament light chain (NfL), neuron-specific enolase (NSE), and phosphorylated tau (pTau), biomarkers associated with neurodegeneration and neuronal injury. Multiple linear regression analyses yielded predictive equations demonstrating significant statistical associations and robust internal validity across all assessed domains. This approach offers a novel, non-invasive method for estimating underlying neuropathological processes, facilitating enhanced risk stratification, early diagnosis, and longitudinal monitoring of brain disorders. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.