Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through...

Area of Science:

• Neuroscience
• Biomarker Discovery
• Medical Imaging

Background:

• Alzheimer's disease (AD) has a long preclinical phase, necessitating early detection biomarkers.
• Current amyloid beta (Aβ) biomarkers (CSF, PET, plasma) have limitations like cost, availability, or physiological relevance.
• Brain functional connectivity (FC) alterations are linked to AD pathology, offering a potential non-invasive detection avenue.

Purpose of the Study:

• To explore dynamic functional connectivity (FC) using resting-state functional MRI (rs-fMRI) for non-invasive Aβ detection in Alzheimer's disease.
• To introduce and evaluate the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model for analyzing dynamic FC.

Main Methods:

• Utilized resting-state functional MRI (rs-fMRI) data.
• Applied the novel Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model to analyze dynamic brain functional connectivity.
• Compared the sensitivity of the DCC-GARCH model against cerebrospinal fluid (CSF) Aβ status.

Main Results:

• The DCC-GARCH model demonstrated superior sensitivity in detecting Aβ status compared to traditional methods.
• Dynamic FC analysis using DCC-GARCH provided significant insights into AD-related brain network changes.
• This approach showed potential for single-subject level sensitivity in AD detection.

Conclusions:

• Dynamic FC analysis, particularly with the DCC-GARCH model, offers a promising non-invasive method for early Alzheimer's disease detection.
• This method can potentially identify individuals at risk by detecting Aβ pathology through brain connectivity patterns.
• Further research into dynamic FC could refine early AD diagnosis and risk assessment.