Computer Science ›› 2022, Vol. 49 ›› Issue (8): 257-266.doi: 10.11896/jsjkx.210600094

• Artificial Intelligence • Previous Articles     Next Articles

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. 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).

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

CLC Number: 

  • TP181
[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).
[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).
[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).
[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.
[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.
[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.
[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.
[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).
[52]HSU C W,CHANG C C,LIN C J.A practical guide to support vector classification[OL]. Scholar.
[1] LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214.
[2] NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296.
[3] HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11.
[4] WEI Kai-xuan, FU Ying. Re-parameterized Multi-scale Fusion Network for Efficient Extreme Low-light Raw Denoising [J]. Computer Science, 2022, 49(8): 120-126.
[5] WANG Xin-tong, WANG Xuan, SUN Zhi-xin. Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network [J]. Computer Science, 2022, 49(8): 314-322.
[6] ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343.
[7] YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63.
[8] CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323.
[9] SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features [J]. Computer Science, 2022, 49(6A): 12-16.
[10] LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92.
[11] ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99.
[12] XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406.
[13] YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515.
[14] WANG Fei, HUANG Tao, YANG Ye. Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion [J]. Computer Science, 2022, 49(6A): 784-789.
[15] FANG Lian-hua, LIN Yu-mei, WU Wei-zhi. Optimal Scale Selection in Random Multi-scale Ordered Decision Systems [J]. Computer Science, 2022, 49(6): 172-179.
Full text



No Suggested Reading articles found!