面向人脑功能划分的人工水母搜索优化算法  

Artificial Jellyfish Search Optimization Algorithm for Human Brain Functional Parcellation

作　　者：赵学武 王红梅[1] 刘超慧[1] 李玲玲[1] 薄树奎[1] 冀俊忠 ZHAO Xuewu;WANG Hongmei;LIU Chaohui;LI Lingling;BO Shukui;JI Junzhong(School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China;College of Computer in Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]郑州航空工业管理学院智能工程学院,郑州450046 [2]北京工业大学信息学部计算机学院,北京100124

出　　处：《计算机科学与探索》2022年第8期1829-1841,共13页Journal of Frontiers of Computer Science and Technology

基　　金：河南省科技攻关项目(202102210164,202102210341,212102210077,202102210399,212102210518,222102210292,192102210283);国家自然科学基金(U1904119);河南省高等学校重点科研项目基础研究计划(21A520047,20A520041,20A520040);国家大学生创新创业训练计划项目(202110485023);河南省教育科学十三五规划项目(2020YB0149)。

摘　　要：人脑功能划分是揭示人脑功能分离性的重要方式。然而,现有的大多数划分方法因不能较好地处理功能磁共振影像(fMRI)数据的高维性和低信噪比性,表现出搜索能力较弱和划分结果较差的问题。为了减轻此问题,提出一种基于人工水母搜索优化(AJSO)的人脑功能划分方法。该方法首先基于预处理的fMRI数据计算功能相关矩阵,并将其映射到低维空间。然后将食物编码为由多个功能簇中心构成的聚类解,利用改进型人工水母搜索优化算法搜索更优的食物,采用融入迭代停滞的时间控制机制调控人工水母执行主动运动或被动运动,以提高全局搜索能力;针对主动运动设计适应度引导的步长确定策略,增强人工水母搜索的科学性和针对性。最后根据最小距离原则得到相关矩阵中每行数据的簇标,并将其映射到相应的体素上。在真实fMRI数据上的实验表明:与其他一些划分方法相比,新方法不仅拥有较高的搜索能力,而且可得到具有更好空间结构和更强功能一致性的划分结果。这项研究将人工水母搜索优化算法应用于人脑功能划分,提供了一种更有效的人脑功能划分方法。Human brain function parcellation is an important way to reveal the separation of brain functions.However,most of the existing parcellation methods can not deal with the high dimension and low signal-to-noise ratio of func-tional magnetic resonance imaging(fMRI)data,so they show the problem of weaker search ability and poorer par-cellation results.To alleviate this problem,a human brain functional parcellation method based on artificial jellyfish search optimization(AJSO)algorithm is proposed.Firstly,a functional correlation matrix is calculated based on the preprocessed fMRI data and mapped to a low-dimensional space.Then,a food is encoded as a cluster solution com-posed of multiple functional cluster centers and the improved AJSO is used to search for better food.The time con-trol mechanism integrated with iterative stagnation is used to control the artificial jellyfish to perform active or pas-sive motion,so as to improve the global search ability.Step size determination strategy guided by fitness is designed for active movement to enhance scientific and targeted search of artificial jellyfish.Finally,according to the principle of minimum distance,the cluster label of each row data in the correlation matrix is obtained and mapped to the cor-responding voxels.Experiments on real fMRI data show that compared with other partitioning methods,the new method not only has higher searching ability,but also can obtain better spatial structures and stronger functional con-sistency.In this study,artificial jellyfish search optimization algorithm is applied to brain functional parcellation,which provides a more effective method of brain functional parcellation.

关 键 词：人脑功能划分 人工水母搜索优化算法(AJSO) 融入迭代停滞的时间控制机制 适应度引导的步长确定策略 海马 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]