Investigations into resting-state connectivity using independent component analysis
John Radcliffe Hospital · University of Oxford · +1 more institution
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…
Citation impact
- FWCI
- 14.80
- Percentile
- 100%
- References
- 55
Authors
4- CFChristian F. BeckmannCorresponding
John Radcliffe Hospital, University of Oxford, Wellcome Centre for Integrative Neuroimaging
- MDMarilena DeLuca
John Radcliffe Hospital, University of Oxford, Wellcome Centre for Integrative Neuroimaging
- JTJoseph T. Devlin
John Radcliffe Hospital, University of Oxford, Wellcome Centre for Integrative Neuroimaging
- SMStephen M. Smith
John Radcliffe Hospital, University of Oxford, Wellcome Centre for Integrative Neuroimaging
Topics & keywords
- Resting state fMRI
- Functional magnetic resonance imaging
- Independent component analysis
- Computer science
- Pattern recognition (psychology)
- Artificial intelligence
- Probabilistic logic
- Functional connectivity