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

Vince D. Calhoun, Nina de Lacy

Research output: Contribution to journalReview articlepeer-review

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 languageEnglish (US)
Pages (from-to)561-579
Number of pages19
JournalNeuroimaging Clinics of North America
Volume27
Issue number4
DOIs
StatePublished - 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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