兰州大学机构库 >信息科学与工程学院
A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder
Fu, Yu1; Zhang, Jie2; Li, Yuan3; Shi, Jie1; Zou, Ying1; Guo, Hanning1; Li, Yongchao1; Yao, ZJ(姚志军)1; Wang, Yalin2; Hu, B(胡斌)1,4,5,6
2021-01-10
Source PublicationPROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY
ISSN0278-5846
Volume104
AbstractAutism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with > 80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.
KeywordAutism spectrum disorder (ASD) Surface-based morphometry (SBM) Classification High-dimensional features
DOI10.1016/j.pnpbp.2020.109989
Indexed BySCIE ; SSCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2019YFA0706200] ; National Natural Science Foundation of China[61632014][61627808][61210010] ; National Basic Research Program of China (973 Program)[2014CB744600] ; Program of Beijing Municipal Science & Technology Commission[Z171100000117005] ; Gansu Science and Technology Program[17JR7WA026] ; Fundamental Research Funds for the Central Universities[lzuxxxy2018-it70][lzujbky-2018-it67]
WOS Research AreaNeurosciences & Neurology ; Pharmacology & Pharmacy ; Psychiatry
WOS SubjectClinical Neurology ; Neurosciences ; Pharmacology & Pharmacy ; Psychiatry
WOS IDWOS:000573429100028
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.lzu.edu.cn/handle/262010/442234
Collection信息科学与工程学院
Corresponding AuthorYao, Zhijun; Wang, Yalin; Hu, Bin
Affiliation1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Gansu, Peoples R China
2.Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85287 USA
3.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China
4.Lanzhou Univ, Sch Informat Sci & Engn, Gansu Prov Key Lab Wearable Comp, Lanzhou, Gansu, Peoples R China
5.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai Inst Biol Sci, Shanghai, Peoples R China
6.Capital Med Univ, Beijing Inst Brain Disorders, Beijing, Peoples R China
First Author AffilicationSchool of Information Science and Engineering
Corresponding Author AffilicationSchool of Information Science and Engineering
Recommended Citation
GB/T 7714
Fu, Yu,Zhang, Jie,Li, Yuan,et al. A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder[J]. PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY,2021,104.
APA Fu, Yu.,Zhang, Jie.,Li, Yuan.,Shi, Jie.,Zou, Ying.,...&Hu, Bin.(2021).A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder.PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY,104.
MLA Fu, Yu,et al."A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder".PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY 104(2021).
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