The past two decades have witnessed the popularity of functional magnetic resonance imaging (fMRI) as a tool for mapping human brain functions. By measuring blood oxygen level dependent (BOLD) single changes [1
], fMRI detects the active parts of brain. Recently, instead of identifying the isolated activated brain regions under certain conditions, increasing attention has been paid to how different parts of the brain connect, interact and coordinate with each other to perform certain kind of cognitive function [7
Functional connectivity is defined as the “temporal correlations between spatially remote neurophysiological events” [10
]. Unlike anatomical connectivity that describes the physical connections between two brain sites [12
] and effective connectivity which characterizes the influence that a neural system may exert over another [10
], functional connectivity examines regional interactions in the brain at a macro level, using datasets from electroencephalographic (EEG), magnetoencephalographic (MEG), local field potentials (LFP), positron emission tomography (PET) or functional MRI [14
]. Compared with other imaging modalities, functional MRI provides investigators with a non-invasive yet in-vivo representation of brain state with high spatial resolution, and thus has drawn a lot of interest from researchers worldwide.
To stay close with the definition of functional connectivity, we here make a distinct discrimination between two types of fMRI studies. One type tries to find the spatial activation patterns of human brain. These studies are often done under a well-designed cognitive task, aiming to find the brain regions involved in this task. The other type, functional connectivity study with fMRI (fcMRI), tries to find the temporal correlations of spatially remote neurophysiological events. Unlike fMRI study, fcMRI study is often conducted under resting-state condition although there are methods using paradigm fMRI data.
From our perspective, methods used for functional connectivity analysis via fMRI are generally grouped into two types: model-based methods and data-driven methods. Model-based methods such as cross-correlation analysis (CCA) are based on prior knowledge. Since they are easy to implement and interpret, model-based methods are wildly used. Data-driven methods (either based on clustering or decomposition), however, need no prior knowledge. Thus, it is quite useful for resting-state fMRI studies where no prior information about the spatial or temporal pattern is known.
Although interesting research findings about functional connectivity detection with fMRI have been extensively reported in the literature, as far as we know, there are few dedicated reviews on methods for functional connectivity investigation with fMRI. Therefore, this paper aims to provide such a review. We will focus on the methods for detecting functional connectivity with fMRI, while review on methods for study of anatomical connectivity and effective connectivity is beyond the scope of this paper.
This paper is divided into four sections as follows: the first section opens with an introduction of fcMRI study and classification of the study methods. The second section explains these methods in detail, including their principles, main contributors, as well as their advantages and drawbacks. Discussion of problems for fcMRI study is provided in the third section. Finally, the fourth section concludes the paper, and points out the significance of fcMRI study meanwhile.