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.

翻译：在动态功能连接性领域，滑动窗口方法被广泛使用，其稳定性已得到普遍认可。然而，滑动窗口方法在窗口内的数据处理过于简单，这在一定程度上限制了其效果。本研究提出一种基于随机卷积的特征扩展方法，该方法无需训练即可获得比滑动窗口方法更好、更抗噪的结果。在模拟数据上的实验表明，使用随机卷积方法获得的动态功能连接矩阵和时间序列，在更短的时间窗口内与标准答案的拟合度（95.59%）高于滑动窗口方法（45.99%）。在真实数据上的性别差异研究也显示，随机卷积方法比滑动窗口方法揭示了更多的性别差异。通过理论分析，我们提出了一个更全面的卷积功能连接计算模型，滑动窗口方法可作为该模型的一个特例，从而为动态功能连接的研究方法开辟了广阔潜力。