Journal
FRONTIERS IN SYSTEMS NEUROSCIENCE
Volume 7, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnsys.2013.00101
Keywords
resting-state network; non-stationary connectivity; network dynamics; clustering; dynamic connectivity
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Funding
- Intramural Research Program of the NIH, NINDS
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [ZIANS002990] Funding Source: NIH RePORTER
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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.
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