Abstract
Dynamic functional connectivity networks (dFCN) based on rs-fMRI have demonstrated tremendous potential for brain function analysis and brain disease classification. Recently, studies have applied deep learning techniques (e.g., convolutional neural network, CNN) to dFCN classification, and achieved better performance than the traditional machine learning methods. However, previous deep learning methods usually perform successive convolutional operations on the input dFCN to obtain high-order brain network aggregation features, extracting them from each sliding window using a series split, which may neglect non-linear relations between different regions and the sequentiality of information. Important high-order sequence information of dFCN, which could further improve the classification performance, is ignored in these studies. To address these issues, we propose a self-attention-based convolutional recurrent network (SA-CRN) learning framework for brain disease classification with rs-fMRI data. The experimental results on a public dataset (i.e., ADNI) demonstrate the effectiveness of our proposed SA-CRN method.
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Acknowledgement
Z. Zhang, B. Jie, Z. Wang, J. Zhou and Y. Yang were supported in part by NSFC (Nos. 61976006, 61573023, 61902003), Anhui-NSFC (Nos. 1708085MF145, 1808085MF171) and AHNU-FOYHE (No. gxyqZD2017010).
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Zhang, Z., Jie, B., Wang, Z., Zhou, J., Yang, Y. (2022). Self-attention Based High Order Sequence Features of Dynamic Functional Connectivity Networks with rs-fMRI for Brain Disease Classification. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_51
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