Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling

Abstract

Alzheimers disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta (A{beta}), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A{beta} biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimers process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A{beta} detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A{beta} status, and offer key insights into dynamic functional connectivity analysis in AD.