Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably with other methods on networks with false positive motifs and multiscale neural simulations where the ground-truth connectivity was known. When applied to recordings of resting-state functional magnetic resonance imaging (rs-fMRI), DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising family of methods for estimating connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification is needed.

中文翻译：

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动态微分协方差恢复多尺度神经系统中的方向网络结构


研究神经相互作用对于理解行为的神经基础至关重要。许多统计方法已用于分析神经活动，但正确有效地估计网络交互的方向仍然是一个难题。在这里，我们推导了动态微分协方差（DDC），这是一种基于动态网络模型的方法，可在非平稳条件下检测具有低偏差和高噪声容限的方向相互作用。此外，DDC 可以很好地随着记录点的数量而扩展，并且所需的计算与协方差所需的计算相当。 DDC 在具有假阳性主题和已知地面实况连接的多尺度神经模拟的网络上进行了验证并与其他方法进行了比较。当应用于静息态功能磁共振成像 (rs-fMRI) 记录时，DDC 在通过扩散 MRI (dMRI) 获得的 1,000 多名个体受试者中一致检测到具有强结构连通性的区域相互作用。 DDC 是一种很有前景的连通性估计方法，可以推广到各种动态模型和记录技术以及需要系统识别的其他应用。