Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder
Autism Spectrum Disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2024-09-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832572917438742528 |
|---|---|
| author | Ruimin Ma Ruitao Xie Yanlin Wang Jintao Meng Yanjie Wei Yunpeng Cai Wenhui Xi Yi Pan |
| author_facet | Ruimin Ma Ruitao Xie Yanlin Wang Jintao Meng Yanjie Wei Yunpeng Cai Wenhui Xi Yi Pan |
| author_sort | Ruimin Ma |
| collection | DOAJ |
| description | Autism Spectrum Disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging (s-MRI). However, most studies involve with datasets where participants’ age are above 5 and lack interpretability. In this paper, we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years, based on s-MRI features extracted using Contrastive Variational AutoEncoder (CVAE). 78 s-MRIs, collected from Shenzhen Children’s Hospital, are used for training CVAE, which consists of both ASD-specific feature channel and common-shared feature channel. The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control (TC) participants represented by the common-shared features. In case of degraded predictive accuracy when data size is extremely small, a transfer learning strategy is proposed here as a potential solution. Finally, we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions, which discloses potential biomarkers that could help target treatments of ASD in the future. |
| format | Article |
| id | doaj-art-f6b9248fc5f34a758e6154165052503c |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-f6b9248fc5f34a758e6154165052503c2025-02-02T06:29:07ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-09-017378179310.26599/BDMA.2024.9020004Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational AutoencoderRuimin Ma0Ruitao Xie1Yanlin Wang2Jintao Meng3Yanjie Wei4Yunpeng Cai5Wenhui Xi6Yi Pan7Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with University of Chinese Academy of Sciences, Beijing 100049, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Key Laboratory of Intelligent Bioinformatics, and with Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaAutism Spectrum Disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging (s-MRI). However, most studies involve with datasets where participants’ age are above 5 and lack interpretability. In this paper, we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years, based on s-MRI features extracted using Contrastive Variational AutoEncoder (CVAE). 78 s-MRIs, collected from Shenzhen Children’s Hospital, are used for training CVAE, which consists of both ASD-specific feature channel and common-shared feature channel. The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control (TC) participants represented by the common-shared features. In case of degraded predictive accuracy when data size is extremely small, a transfer learning strategy is proposed here as a potential solution. Finally, we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions, which discloses potential biomarkers that could help target treatments of ASD in the future.https://www.sciopen.com/article/10.26599/BDMA.2024.9020004autism spectrum disorder (asd) classificationcontrastive variational autoencoder (cvae)transfer learningneuroanatomical interpretation |
| spellingShingle | Ruimin Ma Ruitao Xie Yanlin Wang Jintao Meng Yanjie Wei Yunpeng Cai Wenhui Xi Yi Pan Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder Big Data Mining and Analytics autism spectrum disorder (asd) classification contrastive variational autoencoder (cvae) transfer learning neuroanatomical interpretation |
| title | Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder |
| title_full | Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder |
| title_fullStr | Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder |
| title_full_unstemmed | Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder |
| title_short | Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder |
| title_sort | autism spectrum disorder classification with interpretability in children based on structural mri features extracted using contrastive variational autoencoder |
| topic | autism spectrum disorder (asd) classification contrastive variational autoencoder (cvae) transfer learning neuroanatomical interpretation |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020004 |
| work_keys_str_mv | AT ruiminma autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder AT ruitaoxie autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder AT yanlinwang autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder AT jintaomeng autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder AT yanjiewei autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder AT yunpengcai autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder AT wenhuixi autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder AT yipan autismspectrumdisorderclassificationwithinterpretabilityinchildrenbasedonstructuralmrifeaturesextractedusingcontrastivevariationalautoencoder |