title: Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns.
abstract: Recent fMRI studies have shown that analysis of the human brain's spontaneous activity may provide a powerful approach to reveal its functional organization. Dedicated methods have been proposed to investigate co-variation of signals from different brain regions, with the goal of revealing neuronal networks (NNs) that may serve specialized functions. However, these analysis methods generally do not take into account a potential non-stationary (variable) interaction between brain regions, and as a result have limited effectiveness. To address this, we propose a novel analysis method that uses clustering analysis to sort and selectively average fMRI activity time frames to produce a set of co-activation patterns. Compared to the established networks extracted with conventional analysis methods, these co-activation patterns demonstrate novel network features with apparent relevance to the brain's functional organization.
authors.affiliation: Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda, MD, USA.
authors.forename: Xiao
authors.initials: X
authors.lastname: Liu
authors.affiliation: Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda, MD, USA.
authors.forename: Catie
authors.initials: C
authors.lastname: Chang
authors.affiliation: Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda, MD, USA.
authors.forename: Jeff H
authors.initials: JH
authors.lastname: Duyn
keywords: clustering; dynamic connectivity; network dynamics; non-stationary connectivity; resting-state network