Boolean Network Modeling Identifies Cognitive Resilience in the First Murine Model of Asymptomatic Alzheimer's Disease.

ABSTRACT: Alzheimer's disease (AD) is a progressive neurodegenerative disorder defined by amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs), yet approximately 20-30% of aged individuals exhibit these hallmark lesions without developing cognitive impairment-a clinically silent condition termed asymptomatic AD (AsymAD). The molecular basis of this cognitive resilience remains poorly understood due to a lack of mechanistic models. Here, we integrate systems-level Boolean network modeling with in vivo validation to define the transcriptomic logic of AsymAD and uncover a novel preclinical model. Using Boolean implication networks trained on large-scale human cortical RNA-seq datasets, we identified a robust and invariant AD gene signature that accurately stratifies disease states across

SUBMITTER: Jati S 

PROVIDER: S-EPMC12259141 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature