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001019297 1001_ $$0P:(DE-Juel1)161225$$aGenon, Sarah$$b0$$ufzj
001019297 245__ $$a3 - Brain networks atlases
001019297 260__ $$aCambridge, Massachusetts$$bAcademic Press$$c2023
001019297 29510 $$aAdvances in Resting-State Functional MRI: Methods, Interpretation, and Applications
001019297 300__ $$a59-85
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001019297 520__ $$aThe human brain consists of multiple areas and networks with distinct functions. To better understand the functional organization of human brain, methods including independent component analysis and graph theory have been applied to resting-state fMRI (rs-fMRI) data to delineate functional networks and parcellate the brain. An important discovery that motivated the study of brain networks with rs-fMRI was the so-called default mode network, referring to a set of regions that tend to deactivate in response to a wide range of goal-directed task conditions, and which was also observed by decomposing rs-fMRI data. Following upon studies that extracted additional, core brain networks from rs-fMRI, several functional atlases were developed by partitioning the brain into different numbers of regions or networks. From these predefined brain atlases, rs-fMRI features can be extracted for a range of applications, such as to study functional organization across development and aging and for predicting behavior from functional connectivity in healthy populations. In clinical applications, brain atlases have been used to facilitate the prediction of disease symptoms and treatment outcomes, as well as to investigate dysfunctions in patients. Nevertheless, several challenges remain in building and applying brain atlases, in particular with regard to interindividual variability, a topic that will likely remain under investigation in the future.
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