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

Neuroimaging Clin N Am. 2017 Nov;27(4):561-579. doi: 10.1016/j.nic.2017.06.012. Epub 2017 Aug 18.

Authors

Vince D Calhoun¹, Nina de Lacy²

Affiliations

¹ The Mind Research Network, 1101 Yale Boulevard Northeast, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, 1 University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: [email protected].
² Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA 98195, USA.

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.

Keywords: Brain; Connectivity; Dynamics; Function; Group ICA; Independent component analysis; fMR imaging.

Publication types

Review

MeSH terms

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

Abstract

Publication types

MeSH terms

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