Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81395
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dc.contributorDepartment of Chinese and Bilingual Studiesen_US
dc.creatorHu, Jen_US
dc.creatorCao, Len_US
dc.creatorLi, Ten_US
dc.creatorLiao, Ben_US
dc.creatorDong, Sen_US
dc.creatorLi, Pen_US
dc.date.accessioned2019-09-24T00:53:19Z-
dc.date.available2019-09-24T00:53:19Z-
dc.identifier.issn1748-670Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/81395-
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.rightsCopyright © 2020 Jinlong Hu et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Hu, J., Cao, L., Li, T., Liao, B., Dong, S., & Li, P. (2020). Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder. Computational and Mathematical Methods in Medicine, 2020, 1394830, 1-12 is available at https://dx.doi.org/10.1155/2020/1394830en_US
dc.titleInterpretable learning approaches in resting-state functional connectivity analysis : the case of autism spectrum disorderen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage12en_US
dc.identifier.volume2020en_US
dc.identifier.doi10.1155/2020/1394830en_US
dcterms.abstractDeep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationComputational and mathematical methods in medicine, 2020, 1394830, p. 1-12en_US
dcterms.isPartOfComputational and mathematical methods in medicineen_US
dcterms.issued2020-
dc.identifier.isiWOS:000538138700002-
dc.identifier.scopus2-s2.0-85085926544-
dc.identifier.eissn1748-6718en_US
dc.identifier.artn1394830en_US
dc.description.validate202006 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0435-n02en_US
dc.description.pubStatusPublisheden_US
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