J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2015 , Vol. 20 >Issue 6: 758 - 762

DOI: https://doi.org/10.1007/s12204-015-1687-7

Research article

Research on Modeling Approach of Brain Function Network Based on Anatomical Distance

  • Ming-hui ZHANG ,
  • Shu-hui LI ,
  • Li-na ZHANG ,
  • Xiao-yang HE ,
  • Shen-yuan DU ,
  • Hua WANG ,
  • Qie-gen* LIU ,
  • Guang-hua HAN ,
  • Yi-sheng ZHAO ,
  • Hao ZHANG ,
  • Chen HE ,
  • Xu-jin PU ,
  • Kai-yong HUANG ,
  • Bing-quan ZHU ,
  • Hong JI ,
  • Qiu-shi CHEN ,
  • Feng FENG ,
  • Zhan-cheng PAN ,
  • Xiang-lian ZHOU ,
  • Chen HE ,
  • Yin-jie SU ,
  • Geng-xi DAI ,
  • Guo-zheng YAN ,
  • Chuan-jing LU ,
  • Gang LIU ,
  • Jian-hua WANG ,
  • Jin-jian CHEN ,
  • Ying CHEN ,
  • Zhong-hui CHEN ,
  • Jian ZHANG ,
  • Wen-ming XU ,
  • Zheng-qiang WANG ,
  • Jun-liang CAO ,
  • Ling-ge JIANG ,
  • Lin QUAN ,
  • Wen-hua CHU ,
  • Jian-hua WANG ,
  • Hua-dong LI ,
  • Xi ZHU ,
  • Qi-cai LI ,
  • Ling-ge2 JIANG ,
  • Bao-heng YAO ,
  • Zhi-yuan MEI ,
  • Ying-jun ZHANG ,
  • Feng WU ,
  • Lian LIAN ,
  • Yan-li YANG ,
  • Jing-hai GONG ,
  • Hao GUO ,
  • Kun FU ,
  • Jun-jie CHEN ,
  • Hai-fang LI
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  • College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China

Received date: 2015-09-30

  Online published: 2020-10-09

Supported by

Foundation item: the National Natural Science Foundation of China (Nos. 61170136, 61373101, 61472270 and 61402318);The Natural Science Foundation of Shanxi (No. 2014021022-5);The Special/Youth Foundation of Taiyuan University of Technology (No.2012L014);The Youth Team Fund of Taiyuan University of Technology (Nos.2013T047 and 2013T048)

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Abstract

The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging (fMRI) network. Finally, the result shows that the best model only is based on anatomical distance.

Key words： laterally loaded piles; hydraulic head; land deformation; pumping-recovery; $m$-method; back analysis; horizontal displacement; outage performance; heterogeneous circumstance; magnetic resonance imaging (MRI); sparse representation; non-convex; generalized thresholding; amplify-and-forward (AF); beamforming; channel state information (CSI); power control; cognitive radio; monotone optimization; price; Stackelberg game; fairness; supply chain coordination; dictionary updating; alternating direction method; two-level Bregman method with dictionary updating (TBMDU); admission control scheme; circular excavation; heterogeneity; substitution; service parts; last stock; handover service; high-speed train communication; S-clay1 model; undrained compression test; functionally graded materials; low-velocity water entry; cylinder structure; cylindrical sandwich panel; rectangular sandwich plate; simply supported; free vibration; resting-state brain function network; supercavitating; ventilated; dynamic mesh; pitching; wall effect; model network; connection distance minimization; topological property; anatomical distance; underwater glider; nonlinear control; adaptive backstepping; Lyapunov function; cylinder radius; initial velocity; entry angle; soft soil; strain-dependent modulus; common neighbor; video capsule endoscopy (VCE); frame rate; working hours; in vivo experiment

Cite this article

Ming-hui ZHANG , Shu-hui LI , Li-na ZHANG , Xiao-yang HE , Shen-yuan DU , Hua WANG , Qie-gen* LIU , Guang-hua HAN , Yi-sheng ZHAO , Hao ZHANG , Chen HE , Xu-jin PU , Kai-yong HUANG , Bing-quan ZHU , Hong JI , Qiu-shi CHEN , Feng FENG , Zhan-cheng PAN , Xiang-lian ZHOU , Chen HE , Yin-jie SU , Geng-xi DAI , Guo-zheng YAN , Chuan-jing LU , Gang LIU , Jian-hua WANG , Jin-jian CHEN , Ying CHEN , Zhong-hui CHEN , Jian ZHANG , Wen-ming XU , Zheng-qiang WANG , Jun-liang CAO , Ling-ge JIANG , Lin QUAN , Wen-hua CHU , Jian-hua WANG , Hua-dong LI , Xi ZHU , Qi-cai LI , Ling-ge2 JIANG , Bao-heng YAO , Zhi-yuan MEI , Ying-jun ZHANG , Feng WU , Lian LIAN , Yan-li YANG , Jing-hai GONG , Hao GUO , Kun FU , Jun-jie CHEN , Hai-fang LI . Research on Modeling Approach of Brain Function Network Based on Anatomical Distance[J]. Journal of Shanghai Jiaotong University(Science), 2015 , 20(6) : 758 -762 . DOI: 10.1007/s12204-015-1687-7

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