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

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

1.
北京工业大学信息学部多媒体与智能软件技术北京市重点实验室北京 100124

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

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出版历程
- 收稿日期: 2018-10-19
- 录用日期: 2019-04-15
- 刊出日期: 2021-04-23

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

1.
Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124

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

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.
- 脑效应连接网络 /
- 蚁群算法 /
- 多源信息融合 /
- 搜索空间压缩 /
- 启发函数修正
Abstract: The research of brain effective connectivity (EC) networks is an important topic within the community of human brain connectome, where identifying brain EC networks from neuroimaging data has become an effective tool which can evaluate normal brain functions and their injuries associated with neurodegenerative diseases. For learning brain EC networks from functional magnetic resonance imaging data, this paper proposes an ant colony optimization algorithm merged with multiple source information, called ACOMM-EC. First, the new algorithm employs diffusion tensor imaging data to acquire anatomical constraint information among regions of interest (ROIs), and uses Pearson positive correlated information to restrict the search spaces of feasible solutions so that many unnecessary searches of ants can be avoided. And then, by combining the joint active information between two nodes of an arc and the original heuristic function, a new heuristic function with a better heuristic ability is given to induct the process of stochastic searches, which enhances the purpose of ants searching, and improves the optimization efficiency. Finally, the algorithm is tested on different data and compared with some recently proposed algorithms. The results show that the two strategies are effective, and the solution quality of the new algorithm precedes the other algorithms while the convergence speed is faster.
- Brain effective connectivity network /
- ant colony optimization /
- multiple source information fusion /
- search space compression /
- heuristic function revision
Recommended by Associate Editor ZHANG Jun-Ping
注释:

1) 本文责任编委张军平

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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	$71.2\pm13.8$	$74.9\pm8.3$	$78.4\pm13.4$

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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)

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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	$0.82\pm0.07$	$0.82\pm0.07$	$0.82\pm0.07$	$264.35\pm18.27$

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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	$0.65\pm0.07$	$0.76\pm0.07$	$0.71\pm0.07$	$382.74\pm22.85$

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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	$0.64\pm0.07$	$0.71\pm0.07$	$0.68\pm0.07$	$(1.27\pm0.12)\times10^{4}$

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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	$353.44\pm21.63$	$164.45\pm13.24$

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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	$2.43\times10^{3}$	$(1.55\pm0.15)\times10^{4}$	$(2.26\pm0.22) \times10^{3}$

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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

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参考文献(40)

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