Unraveling Alzheimer’s disease: Investigating dynamic functional connectivity in the default mode network through DCC-GARCH modeling

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