Virtual Adversarial Training-Based Deep Feature Aggregation Network From Dynamic Effective Connectivity for MCI Identification

• Published in: IEEE transactions on medical imaging Vol. 41; no. 1; pp. 237 - 251
• Main Authors: Li, Yang, Liu, Jingyu, Jiang, Yiqiao, Liu, Yu, Lei, Baiying
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity patterns; Algorithms; Alzheimer Disease; Artificial neural networks; Brain; Brain - diagnostic imaging; Brain mapping; Brain modeling; Classification; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive tasks; Computer-aided analysis; convolutional neural networks; Deep learning; Diseases; dynamic effective connectivity; Feature extraction; Functional magnetic resonance imaging; graph attention networks; Graph theory; Heuristic algorithms; Humans; Kalman filters; Machine learning; Magnetic Resonance Imaging; Neural networks; Neural Networks, Computer; Perturbation; Representations; Time series analysis; Training; virtual adversarial training
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2021.3110829

Dynamic functional connectivity (dFC) network inferred from resting-state fMRI reveals macroscopic dynamic neural activity patterns for brain disease identification. However, dFC methods ignore the causal influence between the brain regions. Furthermore, due to the complex non-Euclidean structure of brain networks, advanced deep neural networks are difficult to be applied for learning high-dimensional representations from brain networks. In this paper, a group constrained Kalman filter (gKF) algorithm is proposed to construct dynamic effective connectivity (dEC), where the gKF provides a more comprehensive understanding of the directional interaction within the dynamic brain networks than the dFC methods. Then, a novel virtual adversarial training convolutional neural network (VAT-CNN) is employed to extract the local features of dEC. The VAT strategy improves the robustness of the model to adversarial perturbations, and therefore avoids the overfitting problem effectively. Finally, we propose the high-order connectivity weight-guided graph attention networks (cwGAT) to aggregate features of dEC. By injecting the weight information of high-order connectivity into the attention mechanism, the cwGAT provides more effective high-level feature representations than the conventional GAT. The high-level features generated from the cwGAT are applied for binary classification and multiclass classification tasks of mild cognitive impairment (MCI). Experimental results indicate that the proposed framework achieves the classification accuracy of 90.9%, 89.8%, and 82.7% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification respectively, outperforming the state-of-the-art methods significantly.