SI Shuai-zong, LIU Xiao, ZHU Jian, ZHAO Hai. Generative Models of Human Brain Functional Networks Based on Local Community[J]. Journal of Northeastern University Natural Science, 2018, 39(11): 1566-1571.
[1]Power J D,Cohen A L,Nelson S M,et al.Functional network organization of the human brain[J].Neuron,2011,72(4):665-678. [2]Bullmore E,Sporns O.The economy of brain network organization[J].Nature Reviews Neuroscience,2012,13(5):336-349. [3]Bullmore E,Sporns O.Complex brain networks:graph theoretical analysis of structural and functional systems[J].Nature Reviews Neuroscience,2009,10(3):186-198. [4]Betzel R F,Avena-Koenigsberger A,Goni J,et al.Generative models of the human connectome[J].Neuroimage A,2016,124:1054-1064. [5]Sporns O,Betzel R F.Modular brain networks[J].Annual Review of Psychology,2016(67):613-640. [6]Meunier D,Lambiotte R,Bullmore E T.Modular and hierarchically modular organization of brain networks[J].Frontiers in Neuroscience,2010,4:200-211. [7]Gallen C L,Baniqued P L,Chapman S B,et al.Modular brain network oganization pedicts rsponse to cognitive training in older adults[J].PloS One,2016,11(12):e0169015-e0169032. [8]Ziv N E,Ahissar E.Neuroscience:new tricks and old spines[J].Nature,2009,462(7275):859-861. [9]Cannistraci C V,Alanis-Lobato G,Ravasi T.From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks[J].Scientific Reports,2013(3):1613-1626. [10]Lyu L,Zhou T.Link prediction in complex networks:a survey[J].Physica A:Statistical Mechanics and Its Applications,2011,390(6):1150-1170. [11]Hermundstad A M,Bassett D S,Brown K S,et al.Structural foundations of resting-state and task-based functional connectivity in the human brain[J].Proceedings of the National Academy of Sciences,2013,110(15):6169-6174. [12]Vértes P E,Alexander-Bloch A F,Gogtay N,et al.Simple models of human brain functional networks[J].Proceedings of the National Academy of Sciences,2012,109(15):5868-5873.(上接第1565页)复杂度同样是O(n),因此算法的整体时间复杂度为O(n).4结语在肺部分割的问题中,单一的分割算法无法快速、精确地将肺部区域分割出来,严重影响后续处理的精确性,针对这一问题,本文提出了基于多方法融合的肺部分割方法,将灰度、梯度等信息进行综合作为识别特征.实验结果表明,本文提出的方法能获得很好的肺部分割结果.