Decoding Alzheimer’s onset: entropic dynamics, topological knots, and hybrid Markov-LSTM models

Alzheimer's Disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline, synaptic dysfunction, and widespread neuronal loss. Although amyloid-beta (Aβ) plaques and hyperphosphorylated tau proteins are widely recognized as the pathological hallmarks of the disease, the mechanisms linking molecular pathology to cognitive impairment remain only partially understood. This work proposes a unified framework in which AD is interpreted as a progressive failure of information processing occurring across multiple biological scales. Concepts from information theory, stochastic processes, entropy analysis, network science, topology, and quantum-inspired biology are integrated to describe how molecular abnormalities may eventually compromise large-scale neural communication. Furthermore, a hybrid Markov-LSTM (Long Short-Term Memory) predictive framework is introduced to model the transition from Mild Cognitive Impairment (MCI) to AD using multimodal biomarkers. The resulting perspective provides an interdisciplinary approach capable of linking molecular aggregation, synaptic dysfunction, entropy reduction, and network degradation into a coherent explanatory model.