Dynamic thermodynamic-informational entropic relationship (TIER) models of selective vulnerability to neurodegeneration

Abstract

BACKGROUNDNeurodegenerative diseases share selective vulnerability patterns suggesting common physical mechanisms. We apply unified mechanics theory to neural systems, predicting that brain regions accumulate structural damage proportional to computational workload. METHODSWe simulated a hierarchical neural network implementing relationships between mechanical work (W = F x D), proportional thermodynamic entropy accumulation ({Delta}s {propto} W), and structural failure thresholds. Neural architectures at three hierarchical levels employed Hebbian learning across 2000 simulation sets, tracking thermodynamic entropy generation and dynamic stability. A coupled "siphon" model simulated cortical and subcortical support populations under constant cognitive demand. RESULTSHeteromodal integration nodes consistently exhibited elevated work, accelerated entropy accumulation, and dynamic instability across architectures. Support systems reached 50% population loss before cortical systems despite lower absolute work, demonstrating accelerated compensatory failure. DISCUSSIONThese thermodynamic-informational entropic relationship (TIER) models depict mechanisms underlying selective vulnerability across neurodegeneration, reframing neurodegeneration as the physical consequence of evolutionary trade-offs optimizing cognitive performance over longevity.