SD-CNN: A static-dynamic convolutional neural network for functional brain networks

• Published in: Medical image analysis Vol. 83; p. 102679
• Main Authors: Huang, Jiashuang, Wang, Mingliang, Ju, Hengrong, Shi, Zhenquan, Ding, Weiping, Zhang, Daoqiang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-01-2023
• Subjects: Brain - diagnostic imaging; Brain Diseases; Diffusion connections; Dynamic functional connections; Humans; Neural Networks, Computer; Resting-state fMRI; Static functional connections; Resting-state fMRI; Static functional connections; Dynamic functional connections; Diffusion connections
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2022.102679

Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time-varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.
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