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Exploring the Structural and Strategic Bases of Autism Spectrum Disorders With Deep Learning

Authors
 FENGKAI KE  ;  SEUNGJIN CHOI  ;  YOUNG HO KANG  ;  KEUN-AH CHEON  ;  AND SANG WAN LEE 
Citation
 IEEE ACCESS, Vol.8 : 153341-153352, 2020-08 
Journal Title
 IEEE ACCESS 
Issue Date
2020-08
Keywords
Deep learning ; sMRI ; austism spectrum disorders ; neural networks
Abstract
Deep learning models are applied in clinical research in order to diagnose disease. However, diagnosing autism spectrum disorders (ASD) remains challenging due to its complex psychiatric symptoms as well as a generally insuf cient amount of neurobiological evidence. We investigated the structural and strategic bases of ASD using 14 different types of models, including convolutional and recurrent neural networks. Using an open source autism dataset consisting of more than 1000 MRI scan images and a high-resolution structural MRI dataset, we demonstrated how deep neural networks could be used as tools for diagnosing and analyzing psychiatric disorders. We trained 3D convolutional neural networks to visualize combinations of brain regions, thus representing the most referred-to regions used by the model whilst classifying the images. We also implemented recurrent neural networks to classify the sequence of brain regions ef ciently. We found emphatic structural and strategic evidence on which the model heavily relies during the classi cation process. For instance, we observed that the structural and strategic evidence tends to be associated with subcortical structures, including the basal ganglia (BG). Our work identi es the distinct brain structures that characterize a complex psychiatric disorder while streamlining the deductive reasoning that clinicians can use to ensure an economical and time-ef cient diagnosis process.
Files in This Item:
T202003387.pdf Download
DOI
10.1109/ACCESS.2020.3016734
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers
Yonsei Authors
Cheon, Keun Ah(천근아) ORCID logo https://orcid.org/0000-0001-7113-9286
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179627
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