Abstract

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.

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This Issue

Philosophical Transactions of the Royal Society B: Biological Sciences cover image

29 May 2005

Volume 360Issue 1457

Theme Issue ‘Multimodal neuroimaging of brain connectivity’ compiled by P. A. Valdés-Sosa, K. J. Friston and R. Kötter

Article Information

DOI:https://doi.org/10.1098/rstb.2005.1634
PubMed:16087444
Published by:Royal Society
Print ISSN:0962-8436
Online ISSN:1471-2970

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Published online29/05/2005
Published in print29/05/2005

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Investigations into resting-state connectivity using independent component analysis

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