A Mini Review on Different Methods of Functional-MRI Data Analysis

Author(s): Karunanithi Rajamanickam

Physiological changes due to blood oxygen level dependent (BOLD) signals from the brain can be probed by functional MRI (fMRI). Especially, several resting state fMRI (rs-fMRI) studies have evidenced the alterations in the default mode network (DMN), which is a fundamental network among the resting state networks (RSNs) with respect to progressing diminished brain function due to various disease conditions such as Alzheimer’s disease (AD). Recently, there are several techniques developed to analyze the rs-fMRI data such as voxel based morphometry (VBM), i.e., seed based analysis, independent component analysis (ICA), clustering algorithm, graph method, neural networks, pattern classification method and statistical parametric mapping. Though these techniques are promising, its application on routine clinical practice is not yet developed. However, it may play a vital role in future for diagnostic and prognosticating various dementia conditions. In this review, fundamentals of rs-fMRI, different data analysis techniques such as seed-based, independent component analysis and graph theory analysis, regional homogeneity analysis and amplitudes of low-frequency fluctuations of the rs-fMRI BOLD signal are discussed.