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Article: Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network

TitleIdentifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network
Authors
KeywordsComputational modeling
Autism
Functional magnetic resonance imaging
Brain modeling
Data models
Issue Date2020
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2020, Epub 2020-07-21 How to Cite?
AbstractWith the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anomalies in the ASD. However, the diagnosis of ASD still adopts the symptom-based criteria by clinical observation. The existing computational models tend to achieve unreliable diagnostic classification on the large-scale aggregated data sets. In this work, we propose a novel graph-based classification model using the deep belief network (DBN) and the Autism Brain Imaging Data Exchange (ABIDE) database, which is a worldwide multisite functional and structural brain imaging data aggregation. The remarkable connectivity features are selected through a graph extension of K-nearest neighbors and then refined by a restricted path-based depth-first search algorithm. Thanks to the feature reduction, lower computational complexity could contribute to the shortening of the training time. The automatic hyperparameter-tuning technique is introduced to optimize the hyperparameters of the DBN by exploring the potential parameter space. The simulation experiments demonstrate the superior performance of our model, which is 6.4% higher than the best result reported on the ABIDE database. We also propose to use the data augmentation and the oversampling technique to identify further the possible subtypes within the ASD. The interpretability of our model enables the identification of the most remarkable autistic neural correlation patterns from the data-driven outcomes.
Persistent Identifierhttp://hdl.handle.net/10722/285382
ISSN
2019 Impact Factor: 8.793
2015 SCImago Journal Rankings: 3.181

 

DC FieldValueLanguage
dc.contributor.authorHuang, ZA-
dc.contributor.authorZhu, Z-
dc.contributor.authorYau, CH-
dc.contributor.authorTan, KC-
dc.date.accessioned2020-08-18T03:52:56Z-
dc.date.available2020-08-18T03:52:56Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2020, Epub 2020-07-21-
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/285382-
dc.description.abstractWith the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anomalies in the ASD. However, the diagnosis of ASD still adopts the symptom-based criteria by clinical observation. The existing computational models tend to achieve unreliable diagnostic classification on the large-scale aggregated data sets. In this work, we propose a novel graph-based classification model using the deep belief network (DBN) and the Autism Brain Imaging Data Exchange (ABIDE) database, which is a worldwide multisite functional and structural brain imaging data aggregation. The remarkable connectivity features are selected through a graph extension of K-nearest neighbors and then refined by a restricted path-based depth-first search algorithm. Thanks to the feature reduction, lower computational complexity could contribute to the shortening of the training time. The automatic hyperparameter-tuning technique is introduced to optimize the hyperparameters of the DBN by exploring the potential parameter space. The simulation experiments demonstrate the superior performance of our model, which is 6.4% higher than the best result reported on the ABIDE database. We also propose to use the data augmentation and the oversampling technique to identify further the possible subtypes within the ASD. The interpretability of our model enables the identification of the most remarkable autistic neural correlation patterns from the data-driven outcomes.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72-
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems-
dc.rightsIEEE Transactions on Neural Networks and Learning Systems. Copyright © IEEE.-
dc.rights©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectComputational modeling-
dc.subjectAutism-
dc.subjectFunctional magnetic resonance imaging-
dc.subjectBrain modeling-
dc.subjectData models-
dc.titleIdentifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network-
dc.typeArticle-
dc.identifier.emailYau, CH: annyau@hku.hk-
dc.description.naturepostprint-
dc.identifier.doi10.1109/TNNLS.2020.3007943-
dc.identifier.pmid32692687-
dc.identifier.hkuros313000-
dc.publisher.placeUnited States-

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