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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This research was supported by the National Natural Science Foundation of China (No. 62076059) and the Science Project of Liaoning Province (2021-MS-105).
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Cao, P., Wen, G., Liu, X. et al. Modeling the dynamic brain network representation for autism spectrum disorder diagnosis. Med Biol Eng Comput 60, 1897–1913 (2022). https://doi.org/10.1007/s11517-022-02558-4
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DOI: https://doi.org/10.1007/s11517-022-02558-4