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

BACKGROUND: Neurodegenerative 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. METHODS: We simulated a hierarchical neural network implementing relationships between mechanical work (W = F × D), proportional thermodynamic entropy accumulation (Δs ∝ 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. RESULTS: Heteromodal 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. DISCUSSION: These 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.