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。