Article

Investigations into resting-state connectivity using Independent Component Analysis

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
Philosophical Transactions of The Royal Society B Biological Sciences (Impact Factor: 7.06). 06/2005; 360(1457):1001-13. DOI: 10.1098/rstb.2005.1634
Source: PubMed

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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    • "Functional MR images acquired while subjects are at rest (not performing any task) show low frequency (<0.1 Hz) BOLD signal changes in several spatially distinct brain networks (Damoiseaux et al., 2006;Fox and Raichle, 2007;Fox and Greicius, 2010). By using correlation or blind-source separation of these signals, well-known functional networks can be extracted from resting-state fMRI data, such as the auditory, the visual and the sensory-motor networks (Beckmann et al., 2005;Smith et al., 2009). The rationale is that brain regions that are intrinsically and functionally connected share similar time-courses and can, therefore, be separated from others and proven statistically independent (Beckmann and Smith, 2004;Deco et al., 2011). "
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    • "Voxels with a BOLD time course that mapped onto the component's time course, using a Z-score threshold of 4, were designated as members of this component. Verification of candidate networks of interest was obtained through visual comparison with the published literature on neonate and adult ICA networks (Beckmann et al., 2005;Doria et al., 2010;Smith et al., 2009) and through consulting the Automated Anatomical Labeling segmentation map provided with the UNC infant atlas. A multivariate general linear modeling (MVM) framework was employed to assess the effect of PAE on connectivity between the time courses for 6 networks identified through ICA as being of interest (see Results section). "
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