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Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Huang, Huifang ; Liu, Xingdan ; Jin, Yan ; Lee, Seong‐Whan ; Wee, Chong‐Yaw ; Shen, Dinggang

Human brain mapping, 2019-02, Vol.40 (3), p.833-854 [Periódico revisado por pares]

Hoboken, USA: John Wiley & Sons, Inc

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Título:
Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis
Autor: Huang, Huifang ; Liu, Xingdan ; Jin, Yan ; Lee, Seong‐Whan ; Wee, Chong‐Yaw ; Shen, Dinggang
Assuntos: Autism ; Autism Spectrum Disorder - diagnostic imaging ; Autism Spectrum Disorder - physiopathology ; Biomarkers ; Brain ; Brain - diagnostic imaging ; Brain - physiopathology ; Brain architecture ; Brain Mapping - methods ; Child ; Clustering ; computer‐aided diagnosis ; Constraint modelling ; Diagnosis ; Disease detection ; Disorders ; Feature extraction ; Female ; functional connectivity network ; Functional morphology ; Humans ; Image Interpretation, Computer-Assisted - methods ; Information processing ; Magnetic Resonance Imaging - methods ; Male ; Mental disorders ; multi‐kernel fusion ; multi‐view group‐sparse network ; multi‐view learning ; Network analysis ; Networks ; Neural networks ; Neural Pathways - diagnostic imaging ; Neural Pathways - physiopathology ; Regression analysis ; Regression models ; Regularization ; Representations ; resting‐state functional magnetic resonance imaging (R‐fMRI) ; Support Vector Machine ; Support vector machines
É parte de: Human brain mapping, 2019-02, Vol.40 (3), p.833-854
Notas: Funding information
National Natural Science Foundation of China, Grant/Award Numbers: 61300073, 61773048, 61272356, 61463035; National Institutes of Health, Grant/Award Numbers: EB006733, EB008374, MH100217, MH108914, AG041721, AG049371, AG042599, DE022676, CA206100, AG053867, EB022880; Institute for Information & Communications Technology Promotion (IITP) grant, Grant/Award Number: 2017‐0‐00451
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SourceType-Scholarly Journals-1
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Funding information National Natural Science Foundation of China, Grant/Award Numbers: 61300073, 61773048, 61272356, 61463035; National Institutes of Health, Grant/Award Numbers: EB006733, EB008374, MH100217, MH108914, AG041721, AG049371, AG042599, DE022676, CA206100, AG053867, EB022880; Institute for Information & Communications Technology Promotion (IITP) grant, Grant/Award Number: 2017‐0‐00451
Descrição: Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi‐view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l2,1‐norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group‐sparse network at local level. We then linearly fuse the selected features from each individual network via a multi‐kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.
Editor: Hoboken, USA: John Wiley & Sons, Inc
Idioma: Inglês

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Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Huang, Huifang ; Liu, Xingdan ; Jin, Yan ; Lee, Seong‐Whan ; Wee, Chong‐Yaw ; Shen, Dinggang

Hoboken, USA: John Wiley & Sons, Inc

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