基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究

doi:10.11896/jsjkx.210600094

计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 257-266.doi: 10.11896/jsjkx.210600094

基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究

李瑶¹, 李涛¹, 李埼钒¹, 梁家瑞², Ibegbu Nnamdi JULIAN¹, 陈俊杰¹, 郭浩¹

1 太原理工大学信息与计算机学院山西晋中 030600
2 太原理工大学软件学院山西晋中 030600

收稿日期:2021-06-10 修回日期:2021-10-13 发布日期:2022-08-02
通讯作者: 郭浩(feiyu_guo@sina.com)
作者简介:(1813672649@qq.com)
基金资助:
国家自然科学基金(61472270,61672374,61741212,61876124,61976150,61873178);山西省研究生教育创新项目(2020BY131);山西省重点研发计划(201803D31043);山西省科技厅应用基础研究项目青年面上项目(201801D121135,201803D31043)

Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network

LI Yao¹, LI Tao¹, LI Qi-fan¹, LIANG Jia-rui², Ibegbu Nnamdi JULIAN¹, CHEN Jun-jie¹, GUO Hao¹

1 College of Information and Computer,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
2 College of Software,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China

Received:2021-06-10 Revised:2021-10-13 Published:2022-08-02
About author:LI Yao,born in 1996,postgraduate.Her main research interests include artificial intelligence,intelligent information processing and brain imaging.
GUO Hao,Ph.D,professor,is a senior member of China Computer Federation.His main research interests include artificial intelligence,brain imaging and medical big data research.
Supported by:
National Natural Science Foundation of China(61472270,61672374,61741212,61876124,61976150,61873178),Shanxi Province Graduate Education Innovation Project(2020BY131),Key Research and Development Project of Shanxi Province(201803D31043) and Youth General Project of Shanxi Provincial Department of Science and Technology(201801D121135,201803D31043).

摘要/Abstract

摘要： 脑功能超网络已成功应用于脑疾病的诊断。在之前的研究中,集中通过改变超边的方法来改善超网络的构建,忽略了不同尺度的节点定义对脑功能超网络拓扑的影响。考虑到该问题,提出了基于不同尺度的脑区划分来进行脑功能超网络的创建,从而分析其对脑功能超网络拓扑和分类性能的影响。具体来说,首先,基于自动解剖标记模板,利用聚类算法和随机动态种子点的方法对大脑进行细分割;其次,基于每种节点规模下所得的平均时间序列,利用LASSO方法分别进行脑功能超网络的构建;接着分别提取功能超网络的多组局部特征(节点度、最短路径长度、聚类系数),并利用非参数检验和基于相关的方法选取每种节点规模下的差异特征;最后,分别利用支持向量机构建分类模型。分类结果显示,随着节点规模的增大,所构建的脑功能超网络分类准确率增高,在节点尺度1501下,准确率高达95.45%。同时,多尺度融合的分类准确率优于任一尺度下的分类准确率,这表明不同尺度的节点定义会影响脑功能超网络的拓扑,在未来的脑功能超网络研究中,除了关注超边的构建方法外,应更加关注大脑划分方案的选择,而且多种基于大脑划分的尺度融合特征可以补充更多的分类信息,提高抑郁症与正常人的分类性能。此外,无论是在哪种节点规模下,多组局部属性特征的分类性能均优于单一属性的分类性能。该结果表明,多组局部属性特征可以弥补单一特征的缺失信息,从而发现更多的脑疾病生物学标志物,在有效表征脑功能超网络模型的同时,还需要多角度地量化脑功能超网络拓扑信息,这样才可增强组间差异表征能力,提高脑疾病诊断的预测能力。

关键词: 多尺度, 机器学习, 静息态功能磁共振成像, 局部属性特征, 脑功能超网络, 抑郁症

Abstract: Brain functional hypernetworks have been successfully utilized in the diagnosis of brain diseases.In the previous study,the different hyper-edge generation method was mainly used to improve the construction of the hyper-network,which ignored the influence of different nodes definitions on the brain functional hyper-network topology.Therefore,in light of this problem,it is proposed to construct a brain functional hyper-network based on parcellation of different scales,so as to analyze its impact on brain functional hyper-network topology and classification performance.Specifically,firstly,based on the anatomical automatic labeling atlas,the brain was segmented by the method of clustering algorithm and the random dynamic seed point;secondly,based on the average time series obtained under each node scale,the brain functional hyper-network was constructed by the LASSO method respectively;then multiple sets of local features (node degree,shortest path,clustering coefficient) were extracted,and non-parametric tests and correlation-based methods were used to select features with difference;finally,support vector machine was adopted to build classification model.The classification results show that as the size of nodes increases,the classification accuracy of the constructed brain functional hyper-network is higher.When the node scale is 1501,the classification accuracy can reach 95.45%.Meanwhile,the classification accuracy of multi-scale fusion is better than that of any scale,which indicate different node definitions will affect the topology of the brain functional hyper-network.In future research,besides focusing to the construction method of the hyper-edge,the choice of brain parcellation scheme needs more attention in hyper-network.Moreover,combining multi-scale features can supplement more classification information to enhance the classification performance of depression and normal control.In addition,regardless of the size of the node,the classification performance of multiple sets of local properties is better than that of a single type of properties,which illustrates multiple sets of local property can make up for the missing information of a type of single feature,thereby discovering more brain disease biological markers.While effectively representing the brain functional hyper-network,it is also necessary to quantify the brain functional hyper-network topology information from multiple angles,so that the ability to characterize differences between groups can be enhanced,and the ability to diagnose and predict brain diseases can be improved.

Key words: Brain functional hyper-network, Depression, Local property feature, Machine learning, Multi-scale, Resting state functional magnetic resonance imaging

中图分类号:

TP181

李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究[J]. 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094

LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network[J]. Computer Science, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094

参考文献

[1]MORGAN S E,ACHARD S,TERMENON M,et al.Low-di-mensional morpho-space of topological motifs in human fMRI brain networks[J].Network Neuroscience,2018,2(2):285-302.
[2]NI H,LI W,CHEN X.Brain network evolution modeling based on Alzheimer’s disease[J].Chinese Journal of Intelligent Science and Technology,2019,1(4):369-378.
[3]ZHANG Q,WANG B.Progress in the study of functional magnetic resonance imaging (fMRI) brain networks in the depression[J].Chinese Journal of Magnetic Resonance Imaging,2018,9(4):289-293.
[4]WU X J.Research on the Neural Mechanism of Face Recognition Based on the Analysis of the Functional Connectivity of the Resting Brain[D].Beijing:Beijing Jiaotong University,2018.
[5]ZHANG D Q,ZHU Q,HAO X K,et al.Intelligent analysis of brain images[J].Scientia Sinica(Informationis),2018,48(5):109-122.
[6]LI Y,LIU J Y,GAO X Q,et al.Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification[J].Medical Image Analysis,2018,52:80-96.
[7]BARAVALLE R,MONTANI F.Higher-Order CumulantsDrive Neuronal Activity Patterns,Inducing UP-DOWN States in Neural Populations[J].Entropy,2020,22(4):477.
[8]GLICKFELD L L,OLSEN S R.Higher-Order Areas of theMouse Visual Cortex[J].Annual Review of Vision Science,2017,3(1):251-273.
[9]MONTANGIE L,MONTANI F.Higher-order correlations incommon input shapes the output spiking activity of a neural population[J].Physica A:Statistical Mechanics and its Applications,2017,471:845-861.
[10]JIE B,WEE C Y,SHEN D,et al.Hyper-connectivity of functional networks for brain disease diagnosis[J].Medical Image Analysis,2016,32:84.
[11]BATTISTON F,CENCETTI G,C I I,et al.Networks beyond pairwise interactions:Structure and dynamics[J].Physics Reports,2020,874:1-92.
[12]LI Y,GAO X Q,JIE B,et al.Multimodal Hyper-connectivityNetworks for MCI Classification[C]//Medical Image Computing and Computer Assisted Intervention(MICCAI).2017:433-441.
[13]LI Y,SUN C,LI P Z,et al.Hypernetwork Construction andFeature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset[J/OL].Frontiers in Neuroscience,2020,14(60).https://doi.org/10.3389/fnins.2020.00060.
[14]GUO H,LI Y,XU Y,et al.Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods[J].Frontiers in Neuroinformatics,2018,12:25.
[15]YOUSRA A,BASIT R,KAMRAN M A,et al.A multi-modal,multi-atlas-based approach for Alzheimer detection via machine learning[J].International Journal of Imaging Systems and Technology,2018,28(2):113-123.
[16]JING B,LONG Z Q,LIU H,et al.Identifying current and remitted major depressive disorder with the Hurst exponent:a comparative study on two automated anatomical labeling atlases[J].Oncotarget,2017,8(52):90452-90464.
[17]LONG Z,HUANG J,LI B,et al.A Comparative Atlas-Based Recognition of Mild Cognitive Impairment with Voxel-Based Morphometry[J/OL].Frontiers in Neuroscience,2018,12(916).https://doi.org/10.3389/fnins.2018.00916.
[18]HU Y,WANG L J,NIE S D.Review on brain functional parcellation based on resting-state functional magnetic resonance imaging data[J].Journal of Image and Graphics,2017,22(10):1325-1334.
[19]XIAO Q.A Method for Measuring Node Importance in Hypernetwork Model[J].Research Journal of Applied Sciences,Engineering and Technology,2013,5(2):568-573.
[20]MA T,SUO Q.Review of hypernetwork based on Hypergraph[J].Operations Research and Management Science,2021,30(2):232-239.
[21]HEINTZ B,HONG R,SINGH S,et al.MESH:A Flexible Distributed Hypergraph Processing System[C]//IEEE Internatio-nal Conference on Cloud Engineering (IC2E).Prague:IEEE Press,2019:12-22.
[22]DUAN T,LI X B,ZHOU X,et al.Review on the research progress of super-network:theory,application and tools[C]//The Conference of China System Simulation Technology and Application (CCSSTA).Xinjiang,2019.
[23]KAUFMANN M,KREVELD M,SPECKMANN B.Subdivision Drawings of Hypergraphs[C]//International Symposium on Graph Drawing.2016:396-407.
[24]MAZOYER N T,LANDEAU B,PAPATHANASSIOU D,et al.Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J].Neuroimage,2002,15(1):273-289.
[25]PARK H S,JUN C H.A simple and fast algorithm for K-medoids clustering[J].Expert Systems with Applications,2009,36(2):3336-3341.
[26]GUO H.Machine learning classifier using abnormal resting state functional brain netwrok topological metrics in major depressive disorder[D].Taiyuan:TaiYuan University of Technology,2013.
[27]LI Y,GAO X,JIE B,et al.Multimodal Hyper-connectivity Networks for MCI Classification[C]//Medical Image Computing and Computer Assisted Intervention(MICCAI 2017).2017:433-441.
[28]ZU C,GAO Y,MUNSELL B,et al.Identifying disease-relatedsubnetwork connectome biomarkers by sparse hypergraph lear-ning[J].Brain Imaging and Behavior,2018,13:879-892.
[29]PENG Y,ZU C,ZHANG D Q.Hypergraph Based Multi-Modal Feature Selection and Its Application[J].Journal of Frontiers of Computer Science and Technology,2018,12(1):112-119.
[30]LI C X,HAO X K,ZHANG D Q.Hyper-Network Guided Correlation Analysis on Imaging Genetics[J].Pattern Recognition and Artificial Intelligence,2017,30(9):841-849.
[31]LI Y,LIU J Y,GAO X Q,et al.Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification[J].Medical Image Analysis,2019,52:80-96.
[32]ZHANG Z Z,LIN H J,GAO Y.Dynamic Hypergraph Structure Learning[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.2018:3162-3169.
[33]LIU Q S,SUN Y B,WANG C T,et al.Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification[J].IEEE Transactions on Image Processing,2016,26(1):452-463.
[34]GUO H,ZHANG F,CHEN J J,et al.Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer’s Disease[J/OL].Frontiers in neuroscience,2017,11(615).https://doi.org/10.3389/fnins.2017.00615.
[35]WANG M L,HAO X K,HUANG J S,et al.Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzhei-mer’s disease[J].Bioinformatics,2018,35(11):1948-1957.
[36]LIU J,JI S W,YE J P.SLEP:Sparse Learning with Efficient Projections[J/OL].Arizona State University,2013.http://www.public.asu.edu/~jye02/Software/SLEP.
[37]BRIER M R,THOMAS J B,FAGAN A M,et al.Functionalconnectivity and graph theory in preclinical Alzheimer’s disease[J].Neurobiology of Aging,2014,35(4):757-768.
[38]XIAO Q,YANG F L,LUO S,et al.A Node Importance Measu-ring Method based on Hypernetwork[C]//International Confe-rence on Future Generation Communication and Networking.2016:187-196.
[39]ZHANG Z K,LIU C.A hypergraph model of social tagging networks[J/OL].Journal of Statistical Mechanics:Theory and Experiment,2010,10:10005.https://iopscience.iop.org/article/10.1088/1742-5468/2010/10/P10005/meta.
[40]GALLAGHER S R,GOLDBERG D S.Clustering Coefficients in Protein Interaction Hypernetworks[C]//Proceedings of the International Conference on Bioinformatics,Computational Biology and Biomedical Informatics.2013:552-560.
[41]FASANO G,FRANCESCHINI A.A multidimensional version of the Kolmogorov-Smirnov test[J].Monthly Notices of the Royal Astronomical Society,1987,50(1):9-20.
[42]BENJAMINI Y,HOCHBERG Y.Controlling The False Disco-very Rate－A Practical And Powerful Approach To Multiple Testing[J].Journal of the Royal Statistical Society,1995,57(57):289-300.
[43]YANG J,YANG J Y,ZHANG D,et al.Feature fusion:parallel strategy vs.serial strategy[J].Pattern Recognition,2003,36(6):1369-1381.
[44]YU W,LIU T,VALDEZ R,et al.Application of support vector machine modeling for prediction of common diseases:the case of diabetes and pre-diabetes[J/OL].BMC Medical Informatics and Decision Making,2010,10(1):16.https://doi.org/10.1186/1472-6947-10-16.
[45]DASGUPTA S,GOLDBERG Y,KOSOROK M.Feature elimination in kernel machines in moderately high dimensions[J].Annals of Statistics,2019,47:497-526.
[46]ZHAI J Q,YANG X X,CHENG Y Q,et al.Overview of Application of Fault Detection and Diagnosis Based on Machine Learning[J].Computer Measurement and Control,2021,29(3):1-9.
[47]WANG X,DONG Y Q,YU Q,et al.Review of Structural Support Vector Machines[J].Computer Engineering and Applications,2020,56(17):24-32.
[48]GUO H,LI Y,MENSAH G K,et al.Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification[J/OL].Computational and Mathematical Methods in Medicine,2019.https://doi.org/10.1155/2019/9108108.
[49]LV J L,JIANG X,LI X,et al.Sparse representation of whole-brain fMRI signals for identification of functional networks[J].Medical Image Analysis,2015,20(1):112-134.
[50]QIAO L,ZHANG H,KIM M,et al.Estimating functional brain networks by incorporating a modularity prior[J].NeuroImage,2016,141:399-407.
[51]LI X,WANG H.Identification of functional networks in restingstate fMRI data using adaptive sparse representation and affinity propagation clustering[J/OL].Frontiers in Neuroscience,2015,9(383).https://doi.org/10.3389/fnins.2015.00383.
[52]HSU C W,CHANG C C,LIN C J.A practical guide to support vector classification[OL].http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.Google Scholar.

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基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究

Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network

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