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.

中文翻译：

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基于虚拟对抗训练的深度特征聚合网络来自动态有效连接用于 MCI 识别

从静息状态 fMRI 推断出的动态功能连接 (dFC) 网络揭示了用于脑部疾病识别的宏观动态神经活动模式。然而，dFC 方法忽略了大脑区域之间的因果影响。此外，由于脑网络复杂的非欧式结构，先进的深度神经网络难以应用于从脑网络中学习高维表示。在本文中，提出了一种群约束卡尔曼滤波器 (gKF) 算法来构建动态有效连接 (dEC)，其中 gKF 比 dFC 方法更全面地理解了动态大脑网络中的方向交互。然后，采用一种新颖的虚拟对抗训练卷积神经网络（VAT-CNN）来提取 dEC 的局部特征。VAT 策略提高了模型对对抗性扰动的鲁棒性，从而有效地避免了过拟合问题。最后，我们提出了高阶连接权重引导图注意网络（cwGAT）来聚合 dEC 的特征。通过将高阶连通性的权重信息注入注意力机制，cwGAT 提供了比传统 GAT 更有效的高级特征表示。从 cwGAT 生成的高级特征应用于轻度认知障碍 (MCI) 的二元分类和多类分类任务。实验结果表明，该框架在正常控制（NC）与早期 MCI（EMCI）、EMCI 与晚期 MCI（LMCI）以及 NC 与 EMCI 与.