Are you Fangfeng Zhang?

Claim your profile

Publications (3)0 Total impact

  • Fangfeng Zhang, Chunhui Chen, Lu Jiang
    [Show abstract] [Hide abstract]
    ABSTRACT: This study used the complex network analysis to examine the brain functional network involved in right finger movements and compare the deferent functional network involved in left finger and right finger movements. We found that (a)the connections change exponentially as distance between nodes change, the function is Gaussian; (b) the distribution of functional connections was scale-free; (c) the typical path lengths were relatively short and comparable to those calculated for equivalent random networks, but the clustering coefficients were several orders of magnitude larger than those of equivalent random networks; and (d) central nodes were located in brain regions identified in previous cognitive neuroscience studies. These results suggest that the method of complex network analysis can be an important tool in future research in cognitive neuroscience.
    Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on; 11/2010
  • Fangfeng Zhang, Chunhui Chen
    [Show abstract] [Hide abstract]
    ABSTRACT: Evolution modeling of complex network is a fruitful field, yet few studies have modeled system with spatial information like human brain, and methods for modeling spatial system tend to get inconsistent results. The current study intends to improve it by modeling 3-dimension complex evolution model. With the assumption that probability of having edge depended on distance between nodes, we got a network follows power law.
    Sixth International Conference on Natural Computation, ICNC 2010, Yantai, Shandong, China, 10-12 August 2010; 01/2010
  • Fangfeng Zhang, Lu Jiang, Chunhui Chen, Qi Dong, Liu Yan
    [Show abstract] [Hide abstract]
    ABSTRACT: This study used the complex network analysis to examine the brain functional network involved in finger movements. We found that (a) long-range connections decreased exponentially as distance between nodes increased, whereas short-range connections increased linearly with distance; (b) the distribution of functional connections was scale-free; (c) the typical path lengths were relatively short and comparable to those calculated for equivalent random networks, but the clustering coefficients were several orders of magnitude larger than those of equivalent random networks; and (d) central nodes were located in brain regions identified in previous cognitive neuroscience studies. These results suggest that the method of complex network analysis can be an important tool in future research in cognitive neuroscience.
    Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on; 11/2009