Abstract |
The dynamic functional connectivity analysis provides valuable information for understanding functional brain activity underlying different cognitive processes. Modeling spatio-temporal dynamics in functional brain networks is critical for underlying the functional mechanism of autism spectrum disorder (ASD). In our study, we propose a machine learning approach for the classification of neurological disorders while providing an interpretable framework, which thoroughly captures spatio-temporal features in resting-state functional magnetic resonance imaging (rs-fMRI) data. Specifically, we first transform rs-fMRI time-series into temporal multi-graph using the sliding window technique. A temporal multi-graph clustering is then designed to eliminate the inconsistency of the temporal multi-graph series. Then, a graph structure-aware LSTM (GSA-LSTM) is further proposed to capture the spatio-temporal embedding for temporal graphs. Furthermore, The proposed GSA-LSTM can not only capture discriminative features for prediction but also impute the incomplete graphs for the temporal multi-graph series. Extensive experiments on the autism brain imaging data exchange (ABIDE) dataset demonstrate that the proposed dynamic brain network embedding learning outperforms the state-of-the-art brain network classification models. Furthermore, the obtained clustering results are consistent with the previous neuroimaging-derived evidence of biomarkers for autism spectrum disorder (ASD).
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Authors | Peng Cao, Guangqi Wen, Xiaoli Liu, Jinzhu Yang, Osmar R Zaiane |
Journal | Medical & biological engineering & computing
(Med Biol Eng Comput)
Vol. 60
Issue 7
Pg. 1897-1913
(Jul 2022)
ISSN: 1741-0444 [Electronic] United States |
PMID | 35522357
(Publication Type: Journal Article)
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Copyright | © 2022. International Federation for Medical and Biological Engineering. |
Topics |
- Autism Spectrum Disorder
(diagnostic imaging)
- Brain
(diagnostic imaging)
- Brain Mapping
(methods)
- Humans
- Magnetic Resonance Imaging
(methods)
- Neuroimaging
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