SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging

This paper employs a novel-deep learning method and brain frequencies to discriminate school-aged children with autism spectrum disorders (ASD) from typically developing (TD) school-aged children with functional magnetic resonance imaging (fMRI) data of 84 subjects from the ABIDE (Autism Brain Imagi...

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Veröffentlicht in:Multimedia tools and applications Jg. 77; H. 17; S. 22809 - 22820
Hauptverfasser: Xiao, Zhiyong, Wang, Canhua, Jia, Nan, Wu, Jianhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2018
Springer Nature B.V
Schlagworte:
Autism
Autistic children
Brain
Children
Classification
Computer Communication Networks
Computer Science
Data exchange
Data Structures and Information Theory
Deep learning
Disorders
Machine learning
Magnetic resonance imaging
Multimedia Information Systems
NMR
Nuclear magnetic resonance
Special Purpose and Application-Based Systems
Subtraction
Stacked autoencoder
School-aged children
Autism spectrum disorder
Classification
Brain frequency
ISSN:1380-7501, 1573-7721
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Zusammenfassung:This paper employs a novel-deep learning method and brain frequencies to discriminate school-aged children with autism spectrum disorders (ASD) from typically developing (TD) school-aged children with functional magnetic resonance imaging (fMRI) data of 84 subjects from the ABIDE (Autism Brain Imaging Data Exchange) database. Firstly, the fMRI data were preprocessed, and then each subject’s dataset was decomposed into 30 independent components (IC). Secondly, some key ICs were selected and inputted into a stacked autoencoder (SAE). The SAE was adopted for features subtraction and dimensionality reduction. Finally, a softmax classifier was used to discriminate the school-aged children with ASD from TD school-aged children. The average accuracy of the work was as high as 87.21% (average sensitivity = 92.86%, average specificity = 84.32%). The results of classification demonstrated that the proposed method may have the potential to automatically discriminate school-aged children with ASD from TD school-aged children. Attempts to use deep learning-based algorithms and brain frequencies to discriminate school-aged children with ASD from TD school-aged children should likely be a key step forward in auxiliary clinical utility.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-5625-1