基于随机分块模型的结构脑网络连接优化

发布时间：2018-05-28 22:52

本文选题：结构脑网络 + 标记神经元比例　；参考：《计算机工程与设计》2017年08期

【摘要】：针对传统的结构脑网络构建以及分析方法缺乏对网络中连接的可信程度的验证,且在现有的研究中对结构脑网络进行优化的方法存在缺陷,介绍一种基于随机分块模型的算法,对结构脑网络中的连接可靠性进行量化评价,作为基础对网络结构进行优化。利用网络中节点间的模块化结构,以及节点间是否存在连接依赖于节点在网络中起到的作用这两个属性,将节点分为模块,迭代多次,直到节点发挥其最大作用为止。通过与真实网络相比较,验证该方法是否可以应用在结构脑网络。结果表明,在不同的网络稀疏度情况下,使用该算法优化后的网络的正确边的比率,均远高于原有的方法说明该方法对结构脑网络的优化有显著效果。
[Abstract]:In view of the fact that the traditional methods of structural-brain network construction and analysis lack the verification of the credibility of the connections in the network, and the shortcomings of the existing research methods of optimizing the structural brain network, an algorithm based on random block model is introduced. The connection reliability of structural brain network is evaluated quantitatively, and the network structure is optimized based on it. Using the modularization structure between nodes and whether there is a connection between nodes depending on the role of nodes in the network, the nodes are divided into modules and iterated many times until the nodes play their most important role. Compared with the real network, the proposed method can be applied to the structural brain network. The results show that under different network sparsity, the ratio of the correct edges of the optimized network using this algorithm is much higher than that of the original method, which shows that the method has a significant effect on the optimization of the structural brain network.
【作者单位】：太原理工大学计算机科学与技术学院;
【基金】：国家自然科学基金项目(61373101、61472270、61402318) 教育部高等学校博士学科点专项科研基金项目(20131402110006) 太原理工大学青年基金项目(2012L014、2013T047)
【分类号】：O157.5;R318

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