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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Dates
Type | When |
---|---|
Created | 20 years, 2 months ago (June 7, 2005, 3:22 p.m.) |
Deposited | 8 months ago (Jan. 1, 2025, 9:51 a.m.) |
Indexed | 1 hour, 3 minutes ago (Sept. 3, 2025, 6:15 a.m.) |
Issued | 20 years, 3 months ago (May 29, 2005) |
Published | 20 years, 3 months ago (May 29, 2005) |
Published Online | 20 years, 3 months ago (May 29, 2005) |
Published Print | 20 years, 3 months ago (May 29, 2005) |
@article{Beckmann_2005, title={Investigations into resting-state connectivity using independent component analysis}, volume={360}, ISSN={1471-2970}, url={http://dx.doi.org/10.1098/rstb.2005.1634}, DOI={10.1098/rstb.2005.1634}, number={1457}, journal={Philosophical Transactions of the Royal Society B: Biological Sciences}, publisher={The Royal Society}, author={Beckmann, Christian F and DeLuca, Marilena and Devlin, Joseph T and Smith, Stephen M}, year={2005}, month=may, pages={1001–1013} }