Exploring Functional Connectivity Networks in fMRI Data Using Clustering Analysis

  • Dazhong Liu
  • Ning Zhong
  • Yulin Qin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6889)


Some approaches have been proposed for exploring functional brain connectivity networks from functional magnetic resonance imaging (fMRI) data. Based on a popular algorithm K-means and an effective clustering algorithm called Affinity Propagation (AP), a combined clustering method to explore the functional brain connectivity networks is presented. In the proposed method, K-means is used for data reduction and AP is used for clustering. Without setting the seed of ROI in advance, the proposed method is especially appropriate for the analysis of fMRI data collected with a periodic experimental paradigm. The validity of the proposed method is illustrated by experiments on a simulated dataset and a human dataset. Receiver operating characteristic (ROC) analysis was performed on the simulated dataset. Results show that this method can efficiently and robustly detect the actual functional response with typical signal changes in the aspect of noise ratio, phase and amplitude. On the human dataset, the proposed method discovered brain networks which are compatible with the findings of previous studies.


Receiver Operating Characteristic Curve Functional Connectivity Default Mode Network fMRI Data Functional Connectivity Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dazhong Liu
    • 1
    • 2
  • Ning Zhong
    • 1
    • 3
  • Yulin Qin
    • 1
  1. 1.International WIC InstituteBeijing University of TechnologyBeijingChina
  2. 2.School of Mathematics and Computer ScienceHebei UniversityBaodingChina
  3. 3.Dept. of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan

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