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

Vince D. Calhoun; Nina de Lacy

doi:10.1016/j.nic.2017.06.012

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

Vince D. Calhoun, Nina de Lacy

School of Medicine

Research output: Contribution to journal › Review article › peer-review

39 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	561-579
Number of pages	19
Journal	Neuroimaging Clinics of North America
Volume	27
Issue number	4
DOIs	https://doi.org/10.1016/j.nic.2017.06.012
State	Published - Nov 2017

Keywords

Brain
Connectivity
Dynamics
Function
Group ICA
Independent component analysis
fMR imaging

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Clinical Neurology

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10.1016/j.nic.2017.06.012

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

abstract = "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.",

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

Abstract

Keywords

ASJC Scopus subject areas

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