Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings
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发表时间：
2020-12
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通讯作者：
Heejong Bong;Zongge Liu;V. Ventura
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作者：
Heejong Bong;Zongge Liu;V. Ventura
跨多个大脑区域的高维神经记录可用于建立具有良好空间和时间分辨率的功能连接。我们设计并实现了一种新的方法，高维时间序列的潜在动态因子分析（LDFA-H），它结合了（a）一种新的方法来估计高维时间序列（观测变量）之间的协方差结构和（B）一个新的扩展的概率CCA动态时间序列（潜在变量）。我们感兴趣的是潜在变量之间的互相关，在神经记录中，这些变量可以捕获从一个大脑区域到另一个大脑区域的信息流。仿真结果表明，LDFA-H优于现有的方法，在这个意义上说，它捕获的目标因素，即使在区域内的相关性，由于噪声占主导地位的跨区域相关。我们应用我们的方法，从192个电极在前额叶皮层（PFC）和视觉区V4在记忆引导的扫视任务的局部场电位（LFP）记录。结果捕获PFC和V4之间随时间变化的超前滞后依赖性，并显示相关的信号空间分布。
High-dimensional neural recordings across multiple brain regions can be used to establish functional connectivity with good spatial and temporal resolution. We designed and implemented a novel method, Latent Dynamic Factor Analysis of High-dimensional time series (LDFA-H), which combines (a) a new approach to estimating the covariance structure among high-dimensional time series (for the observed variables) and (b) a new extension of probabilistic CCA to dynamic time series (for the latent variables). Our interest is in the cross-correlations among the latent variables which, in neural recordings, may capture the flow of information from one brain region to another. Simulations show that LDFA-H outperforms existing methods in the sense that it captures target factors even when within-region correlation due to noise dominates cross-region correlation. We applied our method to local field potential (LFP) recordings from 192 electrodes in Prefrontal Cortex (PFC) and visual area V4 during a memory-guided saccade task. The results capture time-varying lead-lag dependencies between PFC and V4, and display the associated spatial distribution of the signals.