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Abstract #2746

Resting-state Brain Networks using Spectral Clustering Analysis

Jason Barrett1, Haomiao Meng2, Song Chen1, Li Zhao3, David Alsop3, Xingye Qiao2, and Weiying Dai1

1Computer Science, State University of New York at Binghamton, Vestal, NY, United States, 2Mathematical Sciences, State University of New York at Binghamton, Vestal, NY, United States, 3Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States

Seed-based correlation method and independent component analysis (ICA)-based method have been used to extract the resting-state brain networks from fMRI data. Both methods require either prior knowledge of brain anatomy or selection of unordered spatial sources. Here, we investigate a data-driven spectral clustering algorithm to study brain networks for resting-state arterial spin labeling (ASL) and blood-oxygen-level dependent (BOLD) fMRI data. The spectral clustering algorithm successfully separates the brain resting-state networks and rank the non-neural noises at last. It is of great benefit to use ASL to study brain resting-state networks because of the largely reduced non-neural noise sources.

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