Comparison of Functional Connectivity Analysis Methods in Alzheimer’s Disease

Abstract

This paper intends to present a comparative review of functional connectivity (FC) analysis methods and their computational methodologies measured through functional magnetic resonance imaging (fMRI). The fMRI technique has been established as a powerful tool for identifying and visualizing the active brain areas in response to certain stimuli and tasks. FC is a metric for the interaction between various brain regions. The synchronization of the functional activity between non-adjacent brain regions is reflected in FC, and changes in FC occur earlier than changes in the physical brain structure. The functionally active brain area can be identified by detecting signal changes caused by blood oxygen levels during the corresponding neuronal activity. The fMRI technique can assess these physiological signals, which can be utilized for further study and research. FC is therefore crucial in identifying a variety of brain disorders, including Alzheimer’s (AD). AD is a neurodegenerative disease that primarily affects the elderly, and previous studies have reported that patients with AD seem to have impaired FC between different brain areas. Henceforth, AD patients’ clinical diagnosis and prediction depend significantly on the practical and precise classification of symptoms using fMRI. We have first reviewed the existing FC analysis methods, such as model/seed-based methods and data-driven methods, and further compared them based on the reduced FC observed in AD patients compared to normal controls (NC). The goal is to provide an overview of the benefits, challenges, and limitations of FC analysis methods in the context of medical imaging for AD.

Keywords

• functional connectivity
• fMRI
• Alzheimer’s disease
• seed-based method
• independent component analysis
• machine learning-based methods