An integrated neighborhood correlation and hierarchical clustering approach of functional MRI.
Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) time series, however, the huge computation load makes it difficult for practical use. In this paper, neighborhood correlation (NC) and hierarchical clustering (HC) methods are integrated as a new approach where fMRI data are processed first by NC to get a preliminary image of brain activations, and then by HC to remove some noises. In HC, to better use spatial and temporal information in fMRI data, a new spatio-temporal measure is introduced. A simulation study and an application to visual fMRI data show that the brain activations can be effectively detected and that different response patterns can be discriminated. These results suggest that the proposed new integrated approach could be useful in detecting weak fMRI signals.
Chen
Huafu
H
Center of Neuroinformatics, School of Life Science and Technology, School of Applied Math, University of Electronic Science and Technology of China, Chengdu 610054, China. chenhf@uestc.edu.cn
Yuan
Hong
H
Yao
Dezhong
D
Chen
Lin
L
Chen
Wufan
W
D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013223:Statistics as Topic; D018511:Systems Integration