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首页> 外文期刊>IEEE Transactions on Medical Imaging >Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification
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Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification

机译：基于自适应动态功能连通性的深空间特征融合

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摘要

Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic changes of neural activities in brain disease identification. Most existing dFC modeling methods extract dynamic interaction information by using the sliding window-based correlation, whose performance is very sensitive to window parameters. Because few studies can convincingly identify the optimal combination of window parameters, sliding window-based correlation may not be the optimal way to capture the temporal variability of brain activity. In this paper, we propose a novel adaptive dFC model, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification. Specifically, we adopt an adaptive Ultra-weighted-lasso recursive least squares algorithm to estimate the adaptive dFC, which effectively alleviates the problem of parameter optimization. Then, we extract temporal and spatial features from the adaptive dFC. In order to generate coarser multi-domain representations for subsequent classification, the temporal and spatial features are further mapped into comprehensive fused features with a deep feature fusion method. Experimental results show that the classification accuracy of our proposed method is reached to 87.7%, which is at least 5.5% improvement than the state-of-the-art methods. These results elucidate the superiority of the proposed method for MCI classification, indicating its effectiveness in the early identification of brain abnormalities.
机译：使用静态功能磁共振成像（RS-FMRI）的动态功能连接（DFC）分析是目前一种先进的技术，用于捕获脑病鉴定中神经活动的动态变化。大多数现有DFC建模方法通过使用基于滑动窗口的相关性提取动态交互信息，其性能对窗口参数非常敏感。因为少量研究可以令人信服地识别窗口参数的最佳组合，所以滑动窗口的相关性可能不是捕获大脑活动的时间变异性的最佳方式。在本文中，我们提出了一种新型自适应DFC模型，通过深度空间 - 时间特征融合方法，用于轻度认知障碍（MCI）识别。具体地，我们采用自适应超加权-Lasso递归最小二乘算法来估计自适应DFC，这有效地减轻了参数优化问题。然后，我们从Adaptive DFC中提取时间和空间特征。为了生成用于后续分类的较粗糙的多域表示，时间和空间特征进一步映射到具有深度特征融合方法的综合融合特征。实验结果表明，我们提出的方法的分类准确性达到87.7％，而不是最先进的方法至少为5.5％。这些结果阐明了所提出的MCI分类方法的优越性，表明其在早期识别脑异常的有效性。

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