Identification of autism spectrum disorder using multi-regional resting-state data through an attention learning approach
•Deriving potential biomarkers of ASD from the multi-regional rsfMRI data.•Data augmentation by segmenting the rsfMRI data with a sliding window strategy.•Identifying ASD with long-short term memory network and autoencoder network.•Heterogeneous data from ABIDE demonstrated classification accuracy o...
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| Vydáno v: | Biomedical signal processing and control Ročník 69; s. 102833 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.08.2021
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| ISSN: | 1746-8094, 1746-8108 |
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| Abstract | •Deriving potential biomarkers of ASD from the multi-regional rsfMRI data.•Data augmentation by segmenting the rsfMRI data with a sliding window strategy.•Identifying ASD with long-short term memory network and autoencoder network.•Heterogeneous data from ABIDE demonstrated classification accuracy of 74.7%.
Resting-state functional magnetic resonance imaging (rsfMRI) holds the promise to produce objective biomarkers of autism spectrum disorder (ASD). However, recent imaging efforts have focused on the functional connectivity measures and the resting-state data independently at different regions of interest (ROIs). In the present study, we investigated the multi-regional resting-state data for discovering potential biomarkers of ASD.
For better understanding of the results, we considered the brain activities at ROIs derived from the CC200 atlas. An attention learning approach, stacking a long short-term memory (LSTM) recurrent neural network and an autoencoder network, was proposed to explore the atypical features of brain activities for ASD. And we demonstrated the feasibility of proposed method with an application to the Autism Brain Imaging Data Exchange (ABIDE) database.
Based on the augmented data from 674 subjects, experiments achieved good classification accuracy of 74.7% under the intra-site cross-validation and 71.3% under the inter-site. The results outperform those from traditional machine learning classifiers (including support vector machine and random forest) and previously reported single LSTM network. Analysis on the weights of our optimal model highlighted the brain regions that are known to be implicated in ASD.
This study demonstrates that the attention learning with multi-regional resting-state data has the potential for screening autistic patients. |
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| AbstractList | •Deriving potential biomarkers of ASD from the multi-regional rsfMRI data.•Data augmentation by segmenting the rsfMRI data with a sliding window strategy.•Identifying ASD with long-short term memory network and autoencoder network.•Heterogeneous data from ABIDE demonstrated classification accuracy of 74.7%.
Resting-state functional magnetic resonance imaging (rsfMRI) holds the promise to produce objective biomarkers of autism spectrum disorder (ASD). However, recent imaging efforts have focused on the functional connectivity measures and the resting-state data independently at different regions of interest (ROIs). In the present study, we investigated the multi-regional resting-state data for discovering potential biomarkers of ASD.
For better understanding of the results, we considered the brain activities at ROIs derived from the CC200 atlas. An attention learning approach, stacking a long short-term memory (LSTM) recurrent neural network and an autoencoder network, was proposed to explore the atypical features of brain activities for ASD. And we demonstrated the feasibility of proposed method with an application to the Autism Brain Imaging Data Exchange (ABIDE) database.
Based on the augmented data from 674 subjects, experiments achieved good classification accuracy of 74.7% under the intra-site cross-validation and 71.3% under the inter-site. The results outperform those from traditional machine learning classifiers (including support vector machine and random forest) and previously reported single LSTM network. Analysis on the weights of our optimal model highlighted the brain regions that are known to be implicated in ASD.
This study demonstrates that the attention learning with multi-regional resting-state data has the potential for screening autistic patients. |
| ArticleNumber | 102833 |
| Author | Li, Jun Yu, Xuan Liu, Yaya Yu, Jie Xu, Lingyu |
| Author_xml | – sequence: 1 givenname: Yaya surname: Liu fullname: Liu, Yaya organization: School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China – sequence: 2 givenname: Lingyu surname: Xu fullname: Xu, Lingyu email: frht_sh@163.com organization: School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China – sequence: 3 givenname: Jie surname: Yu fullname: Yu, Jie organization: School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China – sequence: 4 givenname: Jun surname: Li fullname: Li, Jun email: jun.li@coer-scnu.org organization: South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China – sequence: 5 givenname: Xuan surname: Yu fullname: Yu, Xuan organization: School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China |
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