Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis

doi:10.3778/j.issn.1673-9418.1908067

Abstract

Abstract:

Autism is a neurodevelopmental disorder with great uncertainty in its diagnosis. However, the existing modeling methods for autism diagnosis have not been effectively studied for the uncertainty of the diagnosis process so far. In this paper, based on TSK (Takagi-Sugeno-Kang) fuzzy system and combining the association information between functional connections, a new sparse modeling method JGSL-TSK (joint-group-sparse-learning Takagi-Sugeno-Kang) for uncertain joint group is proposed and applied to the auxiliary diagnosis of autism. Firstly, the original rs-fMRI (resting-state functional magnetic resonance imaging) data are preprocessed and extracted to obtain the reduced dimension feature matrix. Secondly, based on the TSK fuzzy system framework, the joint sparse regulari-zation term is introduced to the consequent parameter learning process from the correlation between features, so as to guide the joint selection of features within the same rule and between rules. Finally, the alternating optimization method is used to solve the model. Compared with the existing methods, this method has the advantages of strong interpretability and good classification performance. Experimental results show that this method is conducive to the auxiliary diagnosis of autism.

Key words: autism spectrum disorder (ASD), resting-state functional magnetic resonance imaging (rs-fMRI), TSK fuzzy system, joint group sparse

摘要：

自闭症是一种神经发育障碍类疾病，其诊断过程存在着很大的不确定性。目前已有的面向自闭症诊断的建模方法没有针对诊断过程的不确定性进行有效研究。为此，以TSK模糊系统为基础，结合功能连接之间的关联信息，提出一种新型的不确定性联合组稀疏建模方法JGSL-TSK，并将其用于自闭症的辅助诊断。首先，对原始rs-fMRI数据进行预处理和特征提取，得到低维特征数据；然后，基于TSK模糊系统框架，从特征之间的相关性出发，在后件参数学习过程中引入联合组稀疏正则化项，从而引导同一规则内特征和规则之间特征的联合选择；最后，采用交替优化方法求解模型。与已有方法相比，该方法具有可解释性强、分类准确率高等优点，实验结果证明了该方法有利于自闭症的辅助诊断。

关键词: 自闭症谱系障碍, 静息态功能磁共振成像, TSK模糊系统, 联合组稀疏

ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong. Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(12): 2083-2093.

张春香，王骏，张嘉旭，邓赵红，潘祥，王士同. 面向自闭症辅助诊断的联合组稀疏TSK建模方法[J]. 计算机科学与探索, 2020, 14(12): 2083-2093.

References

[1] Thakkar K N, Polli F E, Joseph R M, et al. Response moni-toring, repetitive behaviour and anterior cingulate abnorma-lities in autism spectrum disorders (ASD)[J]. Brain, 2008, 131(9): 2464-2478.
[2] Hazlett H C, Gu H, Munsell B C, et al. Early brain develop-ment in infants at high risk for autism spectrum disorder[J]. Nature, 2017, 542(7641): 348-351.
[3] Tamura R, Kitamura H, Endo T, et al. Reduced thalamic vo-lume observed across different subgroups of autism spectrum disorders[J]. Psychiatry Research: Neuroimaging, 2010, 184(3): 186-188.
[4] Amaral D G, Schumann C M, Nordahl C W. Neuroanatomy of autism[J]. Trends in Neurosciences, 2007, 31(3): 137-145.
[5] Maximo J O, Keown C L, Nair A, et al. Approaches to local connectivity in autism using resting state functional connec-tivity MRI[J]. Frontiers in Human Neuroscience, 2013, 7: 605.
[6] Shukla D K, Keehn B, Müller R A. Regional homogeneity of fMRI time series in autism spectrum disorders[J]. Neuro-science Letters, 2010, 476(1): 46-51.
[7] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control[J]. Readings in Fuzzy Sets for Intelligent Systems, 1993, 15(1): 387-403.
[8] Jiang Y, Chung F L, Ishibuchi H, et al. Multitask TSK fuzzy system modeling by mining intertask common hidden structure[J]. IEEE Transactions on Cybernetics, 2015, 45(3): 548-561.
[9] Deng Z, Choi K S, Chung F L, et al. Scalable TSK fuzzy modeling for very large datasets using minimal-enclosing-ball approximation[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(2): 210-226.
[10] Deng Z, Jiang Y, Choi K S, et al. Knowledge-leverage-based TSK fuzzy system modeling[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(8): 1200-1212.
[11] Ecker C, Rocha-Rego V, Johnston P, et al. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach[J]. Neuroimage, 2010, 49(1): 44-56.
[12] Gori I, Giuliano A, Muratori F, et al. Gray matter alterations in young children with autism spectrum disorders: compar-ing morphometry at the voxel and regional level[J]. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging, 2015, 25(6): 866-874.
[13] Zhang Y, Wang J, Bao G Q, et al. A novel unsupervised fuzzy feature learning method for computer-aided diagnosis of autism[J]. CAAI Transactions on Intelligent Systems, 2019, 14(5): 882-888.张英, 王骏, 鲍国强, 等. 面向自闭症辅助诊断的无监督模糊特征学习新方法[J]. 智能系统学报, 2019, 14(5): 882-888.
[14] Chiu S L. Fuzzy model identification based on cluster estim-ation[J]. Journal of Intelligent and Fuzzy Systems, 1994, 2(3): 267-278.
[15] Yu G, Liu Y, Shen D. Graph-guided joint prediction of class label and clinical scores for the Alzheimer??s disease[J]. Brain Structure and Function, 2015, 221(7): 1-15.
[16] Thompson R. Graphical models in applied multivariate stati-stics[J]. Journal of Classification, 1992, 9(1): 159-160.
[17] Friedman J, Hastie T, Tibshirani R. Sparse inverse covari-ance estimation with the graphical lasso[J]. Biostatistics, 2008, 9(3): 432-441.
[18] Zou H. The adaptive lasso and its oracle properties[J]. Jour-nal of Industrial and Management Optimization, 2006, 101(476): 1418-1429.
[19] Li H Y. The diagnostic value of serum cystatin C by the receiver operating characteristic curve in early diagnosis of nephropathy[J]. Chinese Journal of Laboratory Diagnosis, 2017, 21(6): 958-960.李红艳. ROC曲线评价血清胱抑素C对早期肾功能损伤的临床诊断价值[J]. 中国实验诊断学, 2017, 21(6): 958-960.
[20] Tibshirani R. The lasso method for variable selection in the Cox model[J]. Statistics in Medicine, 1997, 16(4): 385-395.
[21] Juang C F, Chiu S H, Shiu S J. Fuzzy system learned thro-ugh fuzzy clustering and support vector machine for human skin color segmentation[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2007, 37(6): 1077-1087.
[22] Wang J, Wang Q, Peng J, et al. Multi-task diagnosis for autism spectrum disorders using multi-modality features: a multi-center study[J]. Human Brain Mapping, 2017, 38(6): 3081-3097.