Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and adva...

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Bibliographic Details
Published inNeuroimaging clinics of North America Vol. 27; no. 4; p. 561
Main Authors Calhoun, Vince D, de Lacy, Nina
Format Journal Article
LanguageEnglish
Published United States 01.11.2017
Subjects
Brain - diagnostic imaging
Brain - physiology
Brain Mapping - methods
Humans
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Neural Pathways - diagnostic imaging
Neural Pathways - physiology
Rest
Brain
Function
Group ICA
Dynamics
Independent component analysis
Connectivity
fMR imaging
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ISSN1557-9867
1052-5149
1557-9867
DOI10.1016/j.nic.2017.06.012

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Summary:For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.
ISSN:1557-9867
1052-5149
1557-9867
DOI:10.1016/j.nic.2017.06.012