We suggest a time-varying partial correlation as a statistical measure of dynamic functional connectivity (dFC) in the human brain. Traditional statistical models often assume specific distributions on the measured data such as the Gaussian distribution, which prohibits their application to neuroimaging data analysis. First, we use the copula-based dynamic conditional correlation (DCC), which does not rely on a specific distribution assumption, for estimating time-varying correlation between regions-of-interest (ROIs) of the human brain. Then, we suggest a time-varying partial correlation based on the Gaussian copula-DCC-GARCH model as an effective method for measuring dFC in the human brain. A recursive algorithm is explained for computation of the time-varying partial correlation. Numerical simulation results demonstrate effectiveness of the partial correlation-based methods against pairwise correlation-based methods. In addition, a two-step procedure is described for the inference of sparse dFC structure using functional magnetic resonance imaging (fMRI) data. We illustrate the proposed method by analyzing an fMRI data set of human participants watching a Pixar animated movie. Based on twelve a priori selected brain regions in the cortex, we demonstrate that the proposed method is effective for inferring sparse dFC network structures and robust to noise distribution and a preprocessing step of fMRI data.

我们建议将时变偏相关作为人脑中动态功能连接 (dFC) 的统计量度。传统的统计模型通常假设测量数据的特定分布，例如高斯分布，这阻碍了它们在神经影像数据分析中的应用。首先，我们使用不依赖于特定分布假设的基于 copula 的动态条件相关性 (DCC) 来估计人脑感兴趣区域 (ROI) 之间的时变相关性。然后，我们建议基于 Gaussian copula-DCC-GARCH 模型的时变偏相关作为测量人脑 dFC 的有效方法。解释了一种用于计算时变偏相关的递归算法。数值模拟结果证明了基于部分相关的方法对基于成对相关的方法的有效性。此外，描述了使用功能磁共振成像 (fMRI) 数据推断稀疏 dFC 结构的两步过程。我们通过分析观看皮克斯动画电影的人类参与者的 fMRI 数据集来说明所提出的方法。基于皮层中十二个先验选择的大脑区域，我们证明了所提出的方法对于推断稀疏 dFC 网络结构是有效的，并且对噪声分布和 fMRI 数据的预处理步骤具有鲁棒性。我们通过分析观看皮克斯动画电影的人类参与者的 fMRI 数据集来说明所提出的方法。基于皮层中十二个先验选择的大脑区域，我们证明了所提出的方法对于推断稀疏 dFC 网络结构是有效的，并且对噪声分布和 fMRI 数据的预处理步骤具有鲁棒性。我们通过分析观看皮克斯动画电影的人类参与者的 fMRI 数据集来说明所提出的方法。基于皮层中十二个先验选择的大脑区域，我们证明了所提出的方法对于推断稀疏 dFC 网络结构是有效的，并且对噪声分布和 fMRI 数据的预处理步骤具有鲁棒性。