First,the state space tree method for finding communication network overall re-liability is presented.It directly generates one disjoint tree multilevel polynomial of a networkgraph.Its advantages are smaller computat...First,the state space tree method for finding communication network overall re-liability is presented.It directly generates one disjoint tree multilevel polynomial of a networkgraph.Its advantages are smaller computational effort(its computing time complexity is O(en_l),where e is the number of edges and n_l is the number of leaves)and shorter resulting expression.Second,based on it an exact decomposition algorithm for finding communication network overallreliability is presented by applying the hypergraph theory.If we use it to carry out the m-timedecomposition of a network graph,the communication network scale which can be analyzed by acomputer can be extended to m-fold.展开更多
目的:运用静息态功能磁共振探讨精神分裂症患者的脑网络及其拓扑属性。方法:收集the Center of Biomedical Research Excellence提供的35名精神分裂症患者资料作为患者组以及35名健康被试者资料作为对照组,计算两组的局部一致性,并进行...目的:运用静息态功能磁共振探讨精神分裂症患者的脑网络及其拓扑属性。方法:收集the Center of Biomedical Research Excellence提供的35名精神分裂症患者资料作为患者组以及35名健康被试者资料作为对照组,计算两组的局部一致性,并进行统计学分析。然后使用Dosenbach's 160 atlas检查全脑功能网络,提取全脑网络的两个子网络:默认模式网络和躯体运动网络,构成一个新网络,计算全脑网络及新网络的拓扑属性。结果:精神分裂症患者大脑的默认模式网络与躯体运动网络之间及默认模式网络内部的功能连接存在显著减弱(P<0.05,FDR校正),全脑网络聚集系数有所下降,默认模式网络与躯体运动网络构成的网络的全局和局部效率降低(P<0.05)。结论:精神分裂症患者默认模式网络与躯体运动网络之间及默认模式网络内部功能连接存在显著异常,默认模式网络与躯体运动网络之间拓扑属性的显著改变可能成为关键因素。此外,该结论可运用于默认模式网络与躯体运动网络对应的脑神经元随机微分方程组的定性分析,从而对精神分裂症的物理治疗有所帮助。展开更多
文摘First,the state space tree method for finding communication network overall re-liability is presented.It directly generates one disjoint tree multilevel polynomial of a networkgraph.Its advantages are smaller computational effort(its computing time complexity is O(en_l),where e is the number of edges and n_l is the number of leaves)and shorter resulting expression.Second,based on it an exact decomposition algorithm for finding communication network overallreliability is presented by applying the hypergraph theory.If we use it to carry out the m-timedecomposition of a network graph,the communication network scale which can be analyzed by acomputer can be extended to m-fold.
文摘目的:运用静息态功能磁共振探讨精神分裂症患者的脑网络及其拓扑属性。方法:收集the Center of Biomedical Research Excellence提供的35名精神分裂症患者资料作为患者组以及35名健康被试者资料作为对照组,计算两组的局部一致性,并进行统计学分析。然后使用Dosenbach's 160 atlas检查全脑功能网络,提取全脑网络的两个子网络:默认模式网络和躯体运动网络,构成一个新网络,计算全脑网络及新网络的拓扑属性。结果:精神分裂症患者大脑的默认模式网络与躯体运动网络之间及默认模式网络内部的功能连接存在显著减弱(P<0.05,FDR校正),全脑网络聚集系数有所下降,默认模式网络与躯体运动网络构成的网络的全局和局部效率降低(P<0.05)。结论:精神分裂症患者默认模式网络与躯体运动网络之间及默认模式网络内部功能连接存在显著异常,默认模式网络与躯体运动网络之间拓扑属性的显著改变可能成为关键因素。此外,该结论可运用于默认模式网络与躯体运动网络对应的脑神经元随机微分方程组的定性分析,从而对精神分裂症的物理治疗有所帮助。
