Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia

Understanding how receptor-level pharmacological modulation reorganizes large-scale brain circuits remains a central challenge in neuropharmacology. We introduce a multiscale mechanistic model with explicit core-matrix thalamocortical architecture, driven solely by GABA-A modulation without parameter fitting to any anesthesia data, to examine how propofol reorganizes brainwide activity from individual receptors to systems-level circuits. The model exhibits anesthetic effects spanning individual synaptic conductances to widespread changes in spiking, field potentials, and coherence. Without training on any task-specific data, our simulation of sensory processing in a standard auditory oddball paradigm matches independent macaque datasets. The same simulation, unmodified, also reproduces changes to functional connectivity in anesthetized humans, exhibiting selective attenuation of matrix thalamocortical loops relative to core loops. Most importantly, the simulation identified a dose-dependent biomarker of propofol concentration -- elevated residual inter-stimulus cortical activity -- that was subsequently confirmed in empirical macaque data where it had previously gone unnoticed. This simulation-first discovery, arising from mechanistic circuit dynamics rather than statistical comparison of clinical populations, illustrates a generative framework for translating receptor-level modulation into circuit-scale biomarkers with potential applications across predictive neuropharmacology.