# 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. functional connectivity; fMRI; Alzheimer's disease; seed-based method; independent component analysis; machine learning-based methods; graph-based methods +
Footnote †: journal: Medical Imaging  
resting-state fMRI (rs-fMRI). When the modulation in the BOLD signal is analyzed during a particular activity or given task, such as eye-blinking, speaking, memorizing, finger or hand movement, etc. is called task-based fMRI (ts-fMRI). Sometimes, resting-state brain activity is far more significant than task-based activity in total brain functioning because separate task-based analyses require different protocols and experiments, such as motor function and language function analysis. Therefore, in this study, we solely focus on rs-fMRI analysis methods. The analysis of the undirected association of two or more functionally connected brain regions has become a cutting-edge research topic in the neuroimaging field. This paper focuses on methods for assessing the FC through correlations with BOLD signals. However, many other approaches utilize another additional component for assessing connectivity. AD is a kind of brain disease similar to coronary artery disease (CAD) in the heart. It is a degenerative disorder as well, which means it becomes worse with time. AD is believed to begin 20 years or more before visible symptoms develop. Symptoms arise when nerve cells (neurons) in the brain involved in thinking, reasoning, and memory (cognitive function) have been harmed or damaged. One of the known causes of AD is the accumulation of \(\beta\)-amyloid protein between neurons that disrupts cell functions. A brief description of the pathophysiology behind this process is shown in Section 2. In neuroimaging, AD pathogenesis can analyze utilizing magnetic resonance imaging (MRI) to examine structural changes, fMRI to assess functional activation patterns, and PET can investigate functional and metabolic alterations utilizing radiotracers. Multi-modal neuroimaging seems to be the most successful method, but it is also the most expensive one. In past years, non-invasive and non-irradiating fMRI has become a popular tool in investigations of mild cognitive impairment and AD [3]. The FC, temporal and spatial connections between distinct brain areas and connectivity-based brain disorders may be investigated using the fMRI technique.