Dynamic functional connectivity (DFC) analysis has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal time-varying dynamic changes of brain states. The sliding window method is by far the most popular DFC analysis method due to its simplicity. However, the sliding window method comes with some assumptions, namely the typically approach uses a single window which captures dynamics only within a specific frequency range. In this study, we propose a generalized approach to dynamics via a multi-dimensional random convolution (RandCon) DFC method that is able to effectively capture time-varying DFC at arbitrary time scales by extracting different local features from fMRI time series using a number of multi-dimensional random convolution kernels without the need for learning kernel weights. Compared to a standard sliding window method, multiplication of temporal derivatives (MTD) and phase synchrony methods, RandCon with the smallest kernel size (3 time points) showed notable improvements in performance on simulated data, particularly in terms of DFC temporal and spatial estimation in very short window/kernel size under different noise levels. Results from real fMRI data indicated that RandCon was more sensitive to gender differences than competing methods. Furthermore, we show that the sliding window method can be considered a special case of the proposed multi-dimensional convolution framework. The proposed method is simple and efficient significantly broadens the scope of dynamic functional connectivity research and offer theoretical and practical potential.

翻译：动态功能连接（DFC）分析已广泛应用于功能磁共振成像（fMRI）数据，以揭示脑状态的时变动态变化。滑动窗口方法因其简便性而成为目前最流行的DFC分析方法。然而，滑动窗口方法存在一些假设，即典型方法使用单一窗口，仅能捕捉特定频率范围内的动态变化。本研究提出了一种通过多维随机卷积（RandCon）DFC方法实现动态分析的广义框架，该方法能够通过使用多个多维随机卷积核从fMRI时间序列中提取不同的局部特征，从而在任意时间尺度上有效捕捉时变DFC，且无需学习卷积核权重。与标准滑动窗口方法、时间导数乘积（MTD）方法及相位同步方法相比，采用最小卷积核尺寸（3个时间点）的RandCon在模拟数据上表现出显著的性能提升，尤其是在不同噪声水平下极短窗口/卷积核尺寸的DFC时间与空间估计方面。真实fMRI数据结果表明，RandCon对性别差异的敏感性优于其他对比方法。此外，我们证明滑动窗口方法可视为所提多维卷积框架的一个特例。该方法简洁高效，显著拓宽了动态功能连接研究的范畴，具有理论与应用潜力。