In the field of dynamic functional connectivity, the sliding-window method is
widely used and its stability is generally recognized. However, the
sliding-window method's data processing within the window is overly simplistic,
which to some extent limits its effectiveness. This study proposes a feature
expansion method based on random convolution, which achieves better and more
noise-resistant results than the sliding-window method without requiring
training. Experiments on simulated data show that the dynamic functional
connectivity matrix and time series obtained using the random convolution
method have a higher degree of fit (95.59\%) with the standard answers within
shorter time windows, compared to the sliding-window method (45.99\%). Gender
difference studies on real data also reveal that the random convolution method
uncovers more gender differences than the sliding-window method. Through
theoretical analysis, we propose a more comprehensive convolutional functional
connectivity computation model, with the sliding-window method being a special
case of this model, thereby opening up vast potential for research methods in
dynamic functional connectivity.

基于随机卷积的特征扩展方法在动态功能连接领域比滑动窗口方法更有效，在模拟数据和实际性别差异研究中均取得更好的结果。该研究提出了更全面的卷积功能连接计算模型，其中滑动窗口方法是该模型的特例，从而为动态功能连接的研究方法打开了巨大的潜力。

动态功能连接：基于随机卷积而不学习的不再滑动窗口

No More Sliding-Windows: Dynamic Functional Connectivity Based On Random  Convolutions Without Learning

Graph Neural Networks (GNNs) have shown promise in learning dynamic
functional connectivity for distinguishing phenotypes from human brain
networks. However, obtaining extensive labeled clinical data for training is
often resource-intensive, making practical application difficult. Leveraging
unlabeled data thus becomes crucial for representation learning in a
label-scarce setting. Although generative self-supervised learning techniques,
especially masked autoencoders, have shown promising results in representation
learning in various domains, their application to dynamic graphs for dynamic
functional connectivity remains underexplored, facing challenges in capturing
high-level semantic representations. Here, we introduce the Spatio-Temporal
Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the
Joint Embedding Predictive Architecture (JEPA) in computer vision. ST-JEMA
employs a JEPA-inspired strategy for reconstructing dynamic graphs, which
enables the learning of higher-level semantic representations considering
temporal perspectives, addressing the challenges in fMRI data representation
learning. Utilizing the large-scale UK Biobank dataset for self-supervised
learning, ST-JEMA shows exceptional representation learning performance on
dynamic functional connectivity demonstrating superiority over previous methods
in predicting phenotypes and psychiatric diagnoses across eight benchmark fMRI
datasets even with limited samples and effectiveness of temporal reconstruction
on missing data scenarios. These findings highlight the potential of our
approach as a robust representation learning method for leveraging label-scarce
fMRI data.

图神经网络在学习动态功能连接方面显示出潜力，但是在训练时获取大量标记的临床数据往往资源密集，使实际应用困难。因此，借助无标签数据在标记稀缺环境中进行表征学习变得至关重要。本文介绍了一种空时联合嵌入掩码自编码器（ST-JEMA），它借鉴了计算机视觉中的联合嵌入预测架构（JEPA），并对动态图进行重构，以实现考虑时间视角的更高级语义表示的学习。利用大规模的 UK Biobank 数据集进行自监督学习，ST-JEMA 在动态功能连接中显示出卓越的表示学习性能，证明了在预测表型和精神诊断方面优于先前方法的优越性，并且对缺失数据场景下的时间重构的有效性。这些发现突出了我们的方法作为一种强大的用于利用标记稀缺 fMRI 数据的表示学习方法的潜力。