Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia
- 1Dartmouth
Abstract
Understanding how receptor-level modulation reorganizes large-scale brain circuits remains a central challenge in neuropharmacology. Here we introduce a multiscale mechanistic model with explicit core-matrix thalamocortical architecture to examine how propofol reorganizes brainwide activity from individual receptors to systems-level circuits. Crucially, the model did not involve machine learning or statistical fitting to pre-existing anesthesia data. We use the model to simulate the multi-scale effects of propofol, which range from modulation of individual synaptic conductances to widespread alterations of neural spiking, field potentials, and coherence. Without requiring any training on task-specific data, our simulation of sensory processing in a standard auditory oddball paradigm matches independent data from anesthetized and awake behaving macaques. 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 previously unknown biomarker of propofol concentration: elevated residual inter-stimulus cortical activity. This dose-dependent biomarker 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, serves as an illustrative example of a broader generative framework that can translate receptor-level modulation into circuit-scale biomarkers with potential applications across predictive neuropharmacology.
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BibTeX
@misc{pathak2026mechanistic,
title = {{Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia}},
author = {A. Pathak and S. L. Brincat and Y. Xiong and H. Organtzidis and M. Protter and V. Du and H. H. Strey and L. R. Mujica-Parodi and E. K. Miller and R. Granger},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.10.731411},
url = {https://www.biorxiv.org/content/10.64898/2026.06.10.731411v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia
AU - A. Pathak
AU - S. L. Brincat
AU - Y. Xiong
AU - H. Organtzidis
AU - M. Protter
AU - V. Du
AU - H. H. Strey
AU - L. R. Mujica-Parodi
AU - E. K. Miller
AU - R. Granger
PY - 2026
DA - 2026/06/10
PB - bioRxiv
DO - 10.64898/2026.06.10.731411
UR - https://www.biorxiv.org/content/10.64898/2026.06.10.731411v2
AB - Understanding how receptor-level modulation reorganizes large-scale brain circuits remains a central challenge in neuropharmacology. Here we introduce a multiscale mechanistic model with explicit core-matrix thalamocortical architecture to examine how propofol reorganizes brainwide activity from individual receptors to systems-level circuits. Crucially, the model did not involve machine learning or statistical fitting to pre-existing anesthesia data. We use the model to simulate the multi-scale effects of propofol, which range from modulation of individual synaptic conductances to widespread alterations of neural spiking, field potentials, and coherence. Without requiring any training on task-specific data, our simulation of sensory processing in a standard auditory oddball paradigm matches independent data from anesthetized and awake behaving macaques. 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 previously unknown biomarker of propofol concentration: elevated residual inter-stimulus cortical activity. This dose-dependent biomarker 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, serves as an illustrative example of a broader generative framework that can translate receptor-level modulation into circuit-scale biomarkers with potential applications across predictive neuropharmacology.
ER -