An improved spectral clustering method for accurate detection of brain resting-state networks
Jason Barrett,
Haomiao Meng,
Zongpai Zhang,
Song M. Chen,
Li Zhao,
David C. Alsop,
Xingye Qiao,
Weiying Dai
Affiliations
Jason Barrett
Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA
Haomiao Meng
Department of Mathematics and Statistics, State University of New York at Binghamton, Binghamton, NY, USA
Zongpai Zhang
Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA
Song M. Chen
Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA
Li Zhao
Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
David C. Alsop
Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
Xingye Qiao
Department of Mathematics and Statistics, State University of New York at Binghamton, Binghamton, NY, USA
Weiying Dai
Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA; Corresponding author.
This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.
