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.
Original language | English (US) |
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Article number | 101 |
Journal | Frontiers in Systems Neuroscience |
Volume | 7 |
Issue number | DEC |
DOIs | |
State | Published - Dec 4 2013 |
All Science Journal Classification (ASJC) codes
- Neuroscience (miscellaneous)
- Developmental Neuroscience
- Cognitive Neuroscience
- Cellular and Molecular Neuroscience