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Neuroimage. 2018 Oct 15;180(Pt B):485-494. doi: 10.1016/j.neuroimage.2018.01.041. Epub 2018 Feb 21.

Co-activation patterns in resting-state fMRI signals.

Author information

1
Department of Biomedical Engineering, The Pennsylvania State University, PA, USA; Institute for CyberScience, The Pennsylvania State University, PA, USA. Electronic address: xxl213@engr.psu.edu.
2
Department of Biomedical Engineering, The Pennsylvania State University, PA, USA; The Huck Institutes of Life Sciences, The Pennsylvania State University, PA, USA.
3
Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.

Abstract

The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns.

KEYWORDS:

Co-activation brain patterns; Dynamic brain connectivity; Resting-state fMRI

PMID:
29355767
PMCID:
PMC6082734
[Available on 2019-10-15]
DOI:
10.1016/j.neuroimage.2018.01.041
[Indexed for MEDLINE]

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