Leef: environmental neuroscience modeling for cognitive resilience

Environmental exposures, including noise, air pollution, and heat, produce measurable alterations in neural dynamics, yet these effects remain difficult to monitor outside laboratory settings. Building on recent advances in environmental neuroscience and the Neuropredictive Exposome, we introduce Leef (Local Environmental Exposure Framework), a proposed framework designed to estimate cognitive strain and adaptive capacity from real-world environmental and physiological signals. Leef integrates continuous sensing of ambient exposures (e.g., PM 2.5 , VOCs, temperature, noise) with neural signatures of environmental stress derived from controlled EEG studies, and is intended to help surface “hidden” cognitive costs that may arise even when behavioral performance appears stable. The framework formalizes how neuro-environmental relationships can be embedded into predictive modeling pipelines designed for edge-based, privacy preserving deployment. Initial implementation and prototyping are underway through ENRI Tech Inc., supporting translation of the framework into scalable research and digital health tools while remaining distinct from clinical validation at this stage. While this work does not present a validated system, it outlines the theoretical, computational, and ethical foundations required to enable real-time inference of environmentally driven cognitive strain. By linking mechanistic neuroscience to deployable sensing architectures, Leef provides a scientifically grounded pathway for studying and mitigating environmental impacts on brain function in naturalistic contexts.