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一种融合多源信息的脑效应连接网络蚁群学习算法

冀俊忠 刘金铎 邹爱笑 杨翠翠

冀俊忠, 刘金铎, 邹爱笑, 杨翠翠. 一种融合多源信息的脑效应连接网络蚁群学习算法.自动化学报, 2021, 47(4): 864-881 doi: 10.16383/j.aas.c180680
引用本文: 冀俊忠, 刘金铎, 邹爱笑, 杨翠翠. 一种融合多源信息的脑效应连接网络蚁群学习算法.自动化学报, 2021, 47(4): 864-881 doi: 10.16383/j.aas.c180680
Ji Jun-Zhong, Liu Jin-Duo, Zou Ai-Xiao, Yang Cui-Cui. An ant colony optimization algorithm merged with multiple source information for learning brain effective connectivity networks. Acta Automatica Sinica, 2021, 47(4): 864-881 doi: 10.16383/j.aas.c180680
Citation: Ji Jun-Zhong, Liu Jin-Duo, Zou Ai-Xiao, Yang Cui-Cui. An ant colony optimization algorithm merged with multiple source information for learning brain effective connectivity networks. Acta Automatica Sinica, 2021, 47(4): 864-881 doi: 10.16383/j.aas.c180680

一种融合多源信息的脑效应连接网络蚁群学习算法

doi: 10.16383/j.aas.c180680
基金项目: 

国家自然科学基金 61672065

详细信息
    作者简介:

    刘金铎  北京工业大学信息学部博士研究生. 2013年获得北京工业大学计算机应用技术专业学士学位. 2018年和2019年分别于纽约州立大学布法罗分校、弗吉尼亚大学做访问学者. 主要研究方向为数据挖掘, 生物信息学, 脑科学. E-mail: jinduo@bjut.edu.cn

    邹爱笑  北京工业大学信息学部博士研究生. 2017年获得北方工业大学工学硕士学位. 主要研究方向为机器学习, 计算智能, 脑科学.E-mail: zouaixiao@emails.bjut.edu.cn

    杨翠翠  北京工业大学讲师. 2017年获得北京工业大学计算机应用技术专业博士学位. 主要研究方向为机器学习, 计算智能, 生物信息学, 脑科学.E-mail: yangcc@bjut.edu.cn

    通讯作者:

    冀俊忠  北京工业大学教授.2004年获得北京工业大学计算机应用技术专业博 士学位,2005年和2010年分别于挪威科技大学、纽约州立大学布法罗分校做访问学者.主要研究方向为机器学习,计算 智能, 生物信息学, 脑科学. 本文通信作者.E-mail: jjz01@bjut.edu.cn

An Ant Colony Optimization Algorithm Merged With Multiple Source Information for Learning Brain Effective Connectivity Networks

Funds: 

National Natural Science Foundation of China 61672065

More Information
    Author Bio:

    LIU Jin-Duo  Ph. D. candidate at the Beijing University of Technology. He received his bachelor degree in computer science and application technology from Beijing University of Technology, China in 2013. He was a visiting scholar at State University of New York, USA at Buffalo in 2018 and University of Virginia in 2019. His research interest covers data mining, bioinformatics and brain science

    ZOU Ai-Xiao  Ph. D. candidate at the Beijing University of Technology. She received her master degree from North China University of Technology in 2017. Her research interest covers machine learning, computational intelligence, and brain science

    YANG Cui-Cui  Lecturer at Beijing University of Technology. She received her Ph. D. degree in computer science and application technology from Beijing University of Technology in 2017. Her research interest covers machine learning, computational intelligence, bioinformatics, and brain science

    Corresponding author: JI Jun-Zhong  Professor at Beijing University of Technology. He received his Ph. D. degree in computer science and application technology from Beijing University of Technology, China in 2004. He was a visiting scholar at Norwegian University of Science and Technology, Norwegian in 2005 and State University of New York at Bufialo, USA in 2010, respectively. His research interest covers machine learning, computational intelligence, bioinformatics, and brain science. Corresponding author of this paper
  • 摘要: 脑效应连接(Effective connectivity, EC)网络是人脑连接组研究中一项重要的研究课题, 识别脑效应连接网络已成为评价正常脑功能及其与神经退化疾病相关损伤的一种有效手段. 针对从功能性磁共振成像数据中进行脑效应连接网络的学习问题, 提出了一种将多源信息与蚁群优化过程相融合的学习方法. 新方法首先利用弥散张量成像数据获取感兴趣区域的结构约束信息, 并利用正相关的皮尔森信息来压缩蚁群搜索的空间, 以避免蚁群的许多不必要的搜索; 然后在蚁群随机搜索中通过将体素联合激活信息融合于启发函数中, 以增强蚂蚁搜索的目的性, 改进算法的优化效率. 实验结果验证了所提策略的有效性, 与最新的同类算法相比, 新算法在保持较快收敛速度的前提下, 具有更好的求解质量.
    Recommended by Associate Editor ZHANG Jun-Ping
    1)  本文责任编委 张军平
  • 图  1  脑效应连接网络与相应候选解的对应关系

    Fig.  1  The mapping relationship between EC and its corresponding candidate solution

    图  2  ACOMM-EC算法学习脑效应连接网络的流程图

    Fig.  2  The main process of learning an effective connectivity network by ACOMM-EC

    图  3  带有4个节点的初始候选连接图的变化事例

    Fig.  3  An example of the change of initial candidate connection diagram with 4 nodes

    图  4  节点$X_4$的侯选父母节点集组成的搜索空间的变化示例

    Fig.  4  An example of the change of initial candidate connection diagram with 4 nodes

    图  5  5个节点网络的仿真数据生成样例

    Fig.  5  Example simulated data for a simple 5-node network

    图  6  在Sim1-1上标准网络和7种算法学习到的脑效应连接网络对比图

    Fig.  6  The comparisons of ground-truth and effective connectivity network learned by 7 algorithms on Sim1-1

    图  7  7种算法在所有仿真数据集上的平均实验结果

    Fig.  7  The average results of 7 methods on all simulated dataset

    图  8  ACOMM-EC算法学习到的脑效应连接网络连接图

    Fig.  8  The connectograms of effective connectivity learned by ACOMM-EC

    表  1  仿真数据集参数

    Table  1  The parameters of Smith's simulation datasets

    Simulation Nodes Session (s) TR (s) Noise (%) HRF (s) Subjects
    Sim1-1 5 600 3.00 1.0 0.5 50
    Sim1-2 10 600 3.00 1.0 0.5 50
    Sim1-3 15 600 3.00 1.0 0.5 50
    Sim1-4 50 600 3.00 1.0 0.5 50
    下载: 导出CSV

    表  2  DCM模型生成仿真数据集参数

    Table  2  The parameters of DCM model generates simulated datasets

    Simulation Nodes Session (s) TR (s) Noise (%) HRF (s) Subjects
    Sim2-1 5 600 3.00 3.0 0.5 50
    Sim2-2 10 600 3.00 3.0 0.5 50
    Sim2-3 15 600 3.00 3.0 0.5 50
    Sim2-4 50 600 3.00 3.0 0.5 50
    Sim2-5 100 600 3.00 3.0 0.5 50
    Sim2-6 200 600 3.00 3.0 0.5 50
    下载: 导出CSV

    表  3  HC, EMCI和LMCI的统计特性

    Table  3  The demographic Information of the HC, EMCI and LMCI

    HC EMCI LMCI
    人数 20 15 15
    性别(男/女) 4/16 10/5 7/8
    年龄 $71.2\pm13.8$ $74.9\pm8.3$ $78.4\pm13.4$
    下载: 导出CSV

    表  4  感兴趣区域名称

    Table  4  The names of the ROIs

    编号 名称 编号 名称
    额叶 1 左背外侧额上回(Frontal_Sup_L) 7 左框内额上回(Frontal_Med_Orb_L)
    2 右背外侧额上回(Frontal_Sup_R) 8 右框内额上回(Frontal_Med_Orb_R)
    3 左额中回(Frontal_Mid_L) 9 左回直肌(Rectus_L)
    4 右额中回(Frontal_Mid_R) 10 右回直肌(Rectus_R)
    5 左内侧额上回(Frontal_Sup_Medial_L) 11 左前扣带和旁扣带脑回(Cingulum_Ant_L)
    6 右内侧额上回(Frontal_Sup_Medial_R) 12 右前扣带和旁扣带脑回(Cingulum_Ant_R)
    顶叶 13 左顶上回(Parietal_Sup_L) 17 左楔前叶(Precuneus_L)
    14 右顶上回(Parietal_Sup_R) 18 右楔前叶(Precuneus_R)
    15 左顶下缘角回(Parietal_Inf_L) 19 左后扣带回(Cingulum_Post_L)
    16 右顶下缘角回(Parietal_Inf_R) 20 右后扣带回(Cingulum_Post_R)
    枕叶 21 左枕上回(Occipital_Sup_L) 24 右枕中回(Occipital_Mid_R)
    22 右枕上回(Occipital_Sup_R) 25 左枕下回(Occipital_Inf_L)
    23 左枕中回(Occipital_Mid_L) 26 右枕下回(Occipital_Inf_R)
    颞叶 27 左颞上回(Temporal_Sup_L) 35 左颞下回(Temporal_Inf_L)
    28 右颞上回(Temporal_Sup_R) 36 右颞下回(Temporal_Inf_R)
    29 左颞上回颞极(Temporal_Pole_Sup_L) 37 左梭状回(Fusiform_L)
    30 右颞上回颞极(Temporal_Pole_Sup_R) 38 右梭状回(Fusiform_R)
    31 左颞中回(Temporal_Mid_L) 39 左海马(Hippocampus_L)
    32 右颞中回(Temporal_Mid_R) 40 右海马(Hippocampus_R)
    33 左颞中回颞极(Temporal_Pole_Mid_L) 41 左海马旁回(ParaHippocampal_L)
    34 右颞中回颞极(Temporal_Pole_Mid_R) 42 右海马旁回(ParaHippocampal_R)
    下载: 导出CSV

    表  5  在Smith仿真数据集上两种新策略的效果

    Table  5  The effectiveness of the two new strategies on Smith's simulation datasets

    数据集 算法 精度 召回率 F度量 时间(s)
    Sim1-1 ACO-EC 1 1 1 $0.36\pm0.02$
    ACO-EC1 1 1 1 $0.36\pm0.02$
    ACO-EC2 1 1 1 $0.38\pm0.02$
    Sim1-2 ACO-EC $0.89\pm0.06$ $0.89\pm0.06$ $0.89\pm0.06$ $2.81\pm0.09$
    ACO-EC1 $0.89\pm0.06$ $0.89\pm0.06$ $0.89\pm0.06$ $1.54\pm0.08$
    ACO-EC2 $0.91\pm0.06$ $0.91\pm0.06$ $0.91\pm0.06$ $2.63\pm0.09$
    Sim1-3 ACO-EC $0 .86\pm0.07$ $0.86\pm0.07$ $0.86\pm0.07$ $10.11\pm0.32$
    ACO-EC1 $0.86\pm0.07$ $0.86\pm0.07$ $0.86\pm0.07$ $6.78\pm0.27$
    ACO-EC2 $0.87\pm0.07$ $0.87\pm0.07$ $0.87\pm0.07$ $9.86\pm0.25$
    Sim1-4 ACO-EC $0.80\pm0.07$ $0.80\pm0.07$ $0.80\pm0.07$ $353.44\pm21.63$
    ACO-EC1 $0.80\pm0.07$ $0.80\pm0.07$ $0.80\pm0.07$ $181.84\pm16.19$
    ACO-EC2 $0.82\pm0.07$ $0.82\pm0.07$ $0.82\pm0.07$ $264.35\pm18.27$
    下载: 导出CSV

    表  6  在生成的高噪声仿真数据集上两种新策略的效果

    Table  6  The effectiveness of the two new strategies on generated simulated datasets with higher noises

    数据集 算法 精度 召回率 F度量 时间(s)
    Sim2-1 ACO-EC 1 1 1 $0.38\pm0.02$
    ACO-EC1 1 1 1 $0.38\pm0.02$
    ACO-EC2 1 1 1 $0.38\pm0.02$
    Sim2-2 ACO-EC $0.77\pm0.08$ $0.77\pm0.08$ $0.77\pm0.08$ $2.97\pm0.12$
    ACO-EC1 $0.77\pm0.08$ $0.77\pm0.08$ $0.77\pm0.08$ $1.69\pm0.09$
    ACO-EC2 $0.81\pm0.07$ $0.81\pm0.07$ $0.81\pm0.07$ $2.87\pm0.13$
    Sim2-3 ACO-EC $0.72\pm0.08$ $0.74\pm0.08$ $0.73\pm0.08$ $12.24\pm0.41$
    ACO-EC1 $0.75\pm0.08$ $0.75\pm0.08$ $0.75\pm0.08$ $7.43\pm0.29$
    ACO-EC2 $0.75\pm0.07$ $0.78\pm0.07$ $0.76\pm0.07$ $11.01\pm0.35$
    Sim2-4 ACO-EC $0.63\pm0.08$ $0.70\pm0.08$ $0.67\pm0.08$ $431.29\pm24.80$
    ACO-EC1 $0.70\pm0.08$ $0.70\pm0.08$ $0.70\pm0.08$ $197.38\pm18.14$
    ACO-EC2 $0.65\pm0.07$ $0.76\pm0.07$ $0.71\pm0.07$ $382.74\pm22.85$
    下载: 导出CSV

    表  7  在更大规模脑网络生成数据集上两种新策略的效果

    Table  7  The effectiveness of the two new strategies on generated simulated datasets with larger scale networks

    数据集 算法 精度 召回率 F度量 时间(s)
    Sim2-5 ACO-EC $0.61\pm0.08$ $0.69\pm0.08$ $0.65\pm0.08$ $(1.79\pm0.13)\times10^{3}$
    ACO-EC1 $0.70\pm0.08$ $0.70\pm0.08$ $0.70\pm0.08$ $(8.68\pm0.76)\times10^{2}$
    ACO-EC2 $0.66\pm0.07$ $0.73\pm0.07$ $0.70\pm0.07$ $(1.54\pm0.10)\times10^{3}$
    Sim2-6 ACO-EC $0.59\pm0.09$ $0.69\pm0.09$ $0.64\pm0.09$ $(1.55\pm0.15)\times10^{4}$
    ACO-EC1 $0.68\pm0.09$ $0.68\pm0.09$ $0.68\pm0.09$ $(4.72\pm0.38)\times10^{3}$
    ACO-EC2 $0.64\pm0.07$ $0.71\pm0.07$ $0.68\pm0.07$ $(1.27\pm0.12)\times10^{4}$
    下载: 导出CSV

    表  8  ACOMM-EC算法和其他6种算法在Smith仿真数据上的实验对比

    Table  8  The comparisons of ACOMM-EC and other six algorithms on Smith's simulated datasets

    数据集 评价指标 CGBN GC GS Patel P-corr ACO-EC ACOMM-EC
    Sim1-1 精度 0.33 0.83 0.60 0.80 0.80 1 1
    召回率 0.4 1 0.60 0.80 0.80 1 1
    F度量 0.36 0.91 0.60 0.80 0.80 1 1
    时间(s) 0.04 1.98 125.56 0.04 72.38 $0.36\pm0.02$ $0.36\pm0.02$
    Sim1-2 精度 0.27 0.64 0.82 0.82 0.82 $0.89\pm0.06$ $0.92\pm0.06$
    召回率 0.29 0.64 0.82 0.82 0.82 $0.89\pm0.06$ $0.92\pm0.06$
    F度量 0.28 0.64 0.82 0.82 0.82 $0.89\pm0.06$ $0.92\pm0.06$
    时间(s) 0.09 7.87 543.78 0.18 86.99 $2.81\pm0.09$ $1.47\pm0.08$
    Sim1-3 精度 0.33 0.63 0.89 0.80 0.56 $0.86\pm0.07$ $0.87\pm0.06$
    召回率 0.33 0.67 0.89 0.89 0.56 $0.86\pm0.07$ $0.87\pm0.06$
    F度量 0.33 0.65 0.89 0.84 0.56 $0.86\pm0.07$ $0.87\pm0.06$
    时间(s) 1.35 17.79 $1.30\times10^{3}$ 0.39 131.41 $10.11\pm0.32$ $6.15\pm0.26$
    Sim1-4 精度 0.44 0.55 0.79 0.77 0.56 $0.80\pm0.07$ $0.82\pm0.06$
    召回率 0.44 0.61 0.79 0.77 0.57 $0.80\pm0.07$ $0.82\pm0.06$
    F度量 0.44 0.58 0.79 0.77 0.56 $0.80\pm0.07$ $0.82\pm0.06$
    时间(s) 4.03 200.34 $1.48\times10^{4}$ 4.55 466.29 $353.44\pm21.63$ $164.45\pm13.24$
    下载: 导出CSV

    表  9  ACOMM-EC算法和其他6种算法在生成仿真数据上的实验对比

    Table  9  The comparisons of ACOMM-EC and other six algorithms on generated simulated datasets

    数据集 评价指标 CGBN GC GS Patel P-corr ACO-EC ACOMM-EC
    Sim2-1 精度 0.50 0.60 0.50 1 1 1 1
    召回率 0.60 1 0.60 1 0.6 1 1
    F度量 0.55 0.75 0.55 1 0.75 1 1
    时间(s) 0.09 2.17 123.48 0.04 85.01 $0.38\pm0.02$ $0.38\pm0.02$
    Sim2-2 精度 0.46 0.59 0.55 0.64 0.63 $0.77\pm0.08$ $0.81\pm0.07$
    召回率 0.60 0.91 0.55 0.64 0.91 $0.77\pm0.08$ $0.81\pm0.07$
    F度量 0.52 0.71 0.55 0.64 0.74 $0.77\pm0.08$ $0.81\pm0.07$
    时间(s) 0.11 4.05 539.42 0.21 94.40 $2.97\pm0.12$ $1.63\pm0.08$
    Sim2-3 精度 0.55 0.59 0.50 0.71 0.48 $0.72\pm0.08$ $0.78\pm0.06$
    召回率 0.64 0.74 0.59 0.71 0.59 $0.74\pm0.08$ $0.78\pm0.06$
    F度量 0.59 0.67 0.54 0.71 0.53 $0.73\pm0.08$ $0.78\pm0.06$
    时间(s) 1.26 11.97 $1.26\times10^{3}$ 0.50 208.92 $12.24\pm0.41$ $6.86\pm0.25$
    Sim2-4 精度 0.41 0.42 0.41 0.56 0.51 $0.63\pm0.08$ $0.76\pm0.06$
    召回率 0.41 0.53 0.71 0.59 0.53 $0.70\pm0.08$ $0.76\pm0.06$
    F度量 0.41 0.47 0.51 0.57 0.52 $0.67\pm0.08$ $0.76\pm0.06$
    时间(s) 3.93 134.78 $1.54\times10^{4}$ 3.66 490.57 $431.29\pm24.80$ $125.47\pm16.122$
    Sim2-5 精度 0.50 0.52 0.45 0.60 0.53 $0.61\pm0.08$ $0.74\pm0.07$
    召回率 0.53 0.54 0.65 0.63 0.54 $0.70\pm0.08$ $0.74\pm0.07$
    F度量 0.51 0.53 0.53 0.61 0.56 $0.66\pm0.08$ $0.74\pm0.07$
    时间(s) 21.59 568.04 $6.28\times10^{4}$ 15.22 $1.06\times10^{3}$ $(1.79\pm0.13)\times10^{3}$ $545.27\pm62.19$
    Sim2-6 精度 0.48 0.32 0.36 0.63 0.53 $0.59\pm0.09$ $0.71\pm0.07$
    召回率 0.53 0.54 0.65 0.63 0.54 $0.69\pm0.09$ $0.71\pm0.07$
    F度量 0.48 0.41 0.45 0.63 0.54 $0.64\pm0.09$ $0.71\pm0.07$
    时间(s) 150.18 $2.39\times10^{3}$ $2.48\times10^{5}$ 58.82 $2.43\times10^{3}$ $(1.55\pm0.15)\times10^{4}$ $(2.26\pm0.22) \times10^{3}$
    下载: 导出CSV

    表  10  HC、EMCI和LMCI三组脑叶内与脑叶间脑效应连接的数量统计

    Table  10  The intra and interlobe effective connectivity statistics for HC、EMCI and LMCI groups

    分组 脑区 额叶 顶叶 枕叶 颞叶
    HC 额叶 30 10 1 13
    顶叶 15 5 12
    枕叶 14 14
    颞叶 45
    EMCI 额叶 29 9 2 12
    顶叶 14 4 9
    枕叶 13 10
    颞叶 43
    LMCI 额叶 29 8 2 9
    顶叶 13 5 10
    枕叶 10 8
    颞叶 39
    下载: 导出CSV
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  • 收稿日期:  2018-10-19
  • 录用日期:  2019-04-15
  • 刊出日期:  2021-04-23

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