标题： 基于时空变换器利用动态功能连接对轻度认知障碍的分类 显示英文标题

标题： Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer

摘要： 使用静息态功能磁共振成像（rs-fMRI）的动态功能连接（dFC）是一种先进的技术，用于捕捉神经活动的动态变化，并在阿尔茨海默病（AD）等脑疾病的研究中非常有用。 然而，现有的研究尚未充分利用嵌入在 dFC 中的序列信息，这些信息在识别脑状态时可能提供有价值的信息。 在本文中，我们提出了一种新颖的框架，基于 transformer 架构联合学习 dFC 中的空间和时间信息嵌入。 具体而言，我们首先通过滑动窗口策略从 rs-fMRI 数据中构建 dFC 网络。 然后，我们同时采用时间块和空间块来捕获更高阶的动态时空依赖表示，通过将其映射到一个高效的融合特征表示中。 为了进一步增强这些特征表示的鲁棒性，减少对标记数据的依赖，我们还引入了对比学习策略来处理不同的脑状态。 在来自阿尔茨海默病神经影像学倡议（ADNI）的 345 名受试者、570 次扫描的实验结果表明，我们提出的方法在轻度认知障碍（MCI，AD 的前驱阶段）预测方面具有优越性，展示了其在早期识别 AD 方面的潜力。 显示英文摘要

摘要： Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities, and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture. Specifically, we first construct dFC networks from rs-fMRI data through a sliding window strategy. Then, we simultaneously employ a temporal block and a spatial block to capture higher-order representations of dynamic spatio-temporal dependencies, via mapping them into an efficient fused feature representation. To further enhance the robustness of these feature representations by reducing the dependency on labeled data, we also introduce a contrastive learning strategy to manipulate different brain states. Experimental results on 345 subjects with 570 scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the superiority of our proposed method for MCI (Mild Cognitive Impairment, the prodromal stage of AD) prediction, highlighting its potential for early identification of AD.