Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption

Area of Science:

• Biochemistry
• Neuroscience
• Computational Biology

Background:

• Alzheimer's disease (AD) is characterized by amyloid-beta (Aβ), tau, and phosphorylated tau (P-tau) biomarkers.
• The complex interplay and disruption of these biomarkers are central to AD pathogenesis.

Purpose of the Study:

• To develop a novel mathematical model for evaluating AD progression.
• To quantify network disruption using integral parameters and network disruption probability.
• To assess the model's efficacy in classifying AD and mild cognitive impairment (MCI).

Main Methods:

• Development of a simplified crosstalk network model for AD biomarkers.
• Quantification of network integral variation using three disruption parameters.
• Evaluation of network robustness via network disruption probability.
• Integration of the model with Support Vector Machine (SVM) for classification.

Main Results:

• Network disruption probability demonstrated a strong linear correlation with the Mini Mental State Examination (MMSE) scores.
• The SVM model achieved high accuracy (95%), sensitivity (95%), and specificity (95%) in classifying AD vs. normal.
• The model also showed high performance in classifying MCI vs. normal (90% accuracy, 94% sensitivity, 83% specificity).

Conclusions:

• The proposed mathematical model effectively evaluates Alzheimer's disease progression.
• The model shows significant potential for facilitating early diagnosis of AD and MCI.
• Network disruption probability serves as a valuable metric for assessing disease severity and cognitive decline.