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Multiple Functional Networks Modeling for Autism Spectrum Disorder Diagnosis

Authors
Kam, Tae-EuiSuk, Heung-IlLee, Seong-Whan
Issue Date
11월-2017
Publisher
WILEY
Keywords
autism spectrum disorder; functional magnetic resonance imaging; discriminative restricted Boltzmann machine; multiple clusters; hierarchical clustering; functional network analysis
Citation
HUMAN BRAIN MAPPING, v.38, no.11, pp.5804 - 5821
Indexed
SCIE
SCOPUS
Journal Title
HUMAN BRAIN MAPPING
Volume
38
Number
11
Start Page
5804
End Page
5821
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/81731
DOI
10.1002/hbm.23769
ISSN
1065-9471
Abstract
Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. (c) 2017 Wiley Periodicals, Inc.
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