Sliding window method is widely used to study the functional connectivity dynamics in brain networks. A key issue of this method is how to choose the window length and number of clusters across different window length. Here, we introduced a universal method to determine the optimal window length and number of clusters and applied it to study the dynamic functional network connectivity (FNC) in major depressive disorder (MDD). Specifically, we first extracted the resting-state networks (RSNs) in 27 medication-free MDD patients and 54 healthy controls using group independent component analysis (ICA), and constructed the dynamic FNC patterns for each subject in the window range of 10–80 repetition times (TRs) using sliding window method. Then, litekmeans algorithm was utilized to cluster the FNC patterns corresponding to each window length into 2–20 clusters. The optimal number of clusters was determined by voting method and the optimal window length was determined by identifying the most representative window length. Finally, 8 recurring FNC patterns regarded as FNC states were captured for further analyzing the dynamic attributes. Our results revealed that MDD patients showed increased mean dwell time and fraction of time spent in state #5, and the mean dwell time is correlated with depression symptom load. Additionally, compared with healthy controls, MDD patients had significantly reduced FNC within FPN in state #7. Our study reported a new approach to determine the optimal window length and number of clusters, which may facilitate the future study of the functional dynamics. These findings about MDD using dynamic FNC analyses provide new evidence to better understand the neuropathology of MDD.

滑动窗口方法被广泛用于研究大脑网络中的功能连接动力学。该方法的一个关键问题是如何选择窗口长度和跨不同窗口长度的簇数。在这里，我们介绍了一种确定最佳窗口长度和簇数的通用方法，并将其应用于研究重度抑郁症 (MDD) 中的动态功能网络连接 (FNC)。具体来说，我们首先使用组独立成分分析（ICA）提取了 27 名无药物 MDD 患者和 54 名健康对照的静息状态网络（RSN），并在 10-80 的窗口范围内为每个受试者构建了动态 FNC 模式。使用滑动窗口方法的重复次数（TRs）。然后，利用litekmeans算法将每个窗口长度对应的FNC模式聚类成2-20个簇。通过投票法确定最佳聚类数，通过识别最具代表性的窗口长度确定最佳窗口长度。最后，捕获了 8 个被视为 FNC 状态的重复 FNC 模式，以进一步分析动态属性。我们的结果显示，MDD 患者的平均停留时间和处于状态#5 的时间比例增加，并且平均停留时间与抑郁症状负荷相关。此外，与健康对照相比，MDD 患者在状态#7 的 FPN 内 FNC 显着降低。我们的研究报告了一种确定最佳窗口长度和簇数的新方法，这可能有助于未来对功能动力学的研究。