The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.

中文翻译：

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NHSMM-MAR-sdNC：一种新颖的数据驱动计算框架，用于状态相关的有效连接分析


大脑表现出内在的动力学特征，其特征是神经活动的自发时空重组或亚稳态，这与功能整合和分离密切相关。与动态功能连接相比，状态依赖的有效连接（即动态有效连接）更适合探索亚稳态，因为它能够推断大脑区域之间的因果关系。然而，依赖于国家的有效连接方法却很少且迫切需要。在本研究中，提出了一种新的数据驱动计算框架，名为 NHSMM-MAR-sdNC，将非参数隐半马尔可夫模型与多元自回归模型和状态相关的新因果关系相结合，以研究状态相关的有效连通性。该框架不受任何生物学假设的约束。此外，可以直接从观测数据中推断出状态数，并且可以明确地估计状态持续时间分布，而不是受几何形式的限制，这克服了隐马尔可夫模型的局限性。合成数据的实验结果表明，该框架能够自适应地识别状态数和准确的状态相关因果网络。这种新方法还通过现实世界静息态功能磁共振成像数据揭示了状态相关因果网络的动态。我们的方法提供了一个新的数据驱动的计算框架，用于识别状态相关的有效连接，这将有助于识别和评估大脑的亚稳定性和流动动力学。