Abstract
The availability of powerful non-invasive neuroimaging techniques has given rise to various studies that aim to map the human brain. These studies focus on not only finding brain activation signatures but also on understanding the overall organization of functional communication in the brain network. Based on the principle that distinct brain regions are functionally connected and continuously share information with each other, various approaches to finding these functional networks have been proposed in the literature. In this paper, we present an overview of the most common methods to estimate and characterize functional connectivity in fMRI data. We illustrate these methodologies with resting-state functional MRI data from the Human Connectome Project, providing details of their implementation and insights on the interpretations of the results. We aim to guide researchers that are new to the field of neuroimaging by providing the necessary tools to estimate and characterize brain circuitry.
Highlights
Functional magnetic resonance imaging techniques have emerged as a powerful tool for the characterization of human brain connectivity and its relationship to health, behavior, and lifestyle [1]
We have reviewed the most common methods to estimate functional connectivity in Functional magnetic resonance imaging (fMRI) data
For single-subject data, estimation can be done by directly quantifying correlations across regions of interest and/or seed regions, or by finding a set of latent components that represent simultaneous activity, and while interpretation is straightforward for the former approach, it is not as clear for the later
Summary
Functional magnetic resonance imaging (fMRI) techniques have emerged as a powerful tool for the characterization of human brain connectivity and its relationship to health, behavior, and lifestyle [1]. Compared to alternative brain imaging modalities such as positron emission tomography (PET) and eletroencephalography (EEG), fMRIs are non-invasive and have a high spatial resolution, which makes them a popular choice in large brain imaging studies An example of such studies is the Human Connectome Project that aims at understanding the underlying function of the brain by describing the patterns of connectivity in the healthy adult human brain [3]. There are mainly two goals in such studies: first, to identify location signatures in the brain that respond to external stimuli, and second, to identify brain space–time association patterns that emerge when the brain is either at rest or performing a task These association patterns are measures of co-activation in functionally connected time series of anatomically different brain regions, known as functional connectivity [4,5]. Understanding these patterns can play an important role in predicting the early onset of neurodegenerative diseases and in monitoring disease care and treatment [6,7]
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