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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.

Original publication

DOI

10.1098/rstb.2005.1634

Type

Journal article

Journal

Philos Trans R Soc Lond B Biol Sci

Publication Date

29/05/2005

Volume

360

Pages

1001 - 1013

Keywords

Brain, Brain Mapping, Data Interpretation, Statistical, Humans, Magnetic Resonance Imaging, Models, Neurological, Models, Statistical