Neural oscillations have long been considered important markers of interaction across brain regions, yet identifying coordinated oscillatory activity from high-dimensional multiple-electrode recordings remains challenging. We sought to quantify time-varying covariation of oscillatory amplitudes across two brain regions, during a memory task, based on local field potentials recorded from 96 electrodes in each region. We extended Canonical Correlation Analysis (CCA) to multiple time series through the cross-correlation of latent time series. This, however, introduces a large number of possible lead-lag cross-correlations across the two regions. To manage that high dimensionality we developed rigorous statistical procedures aimed at finding a small number of dominant lead-lag effects. The method correctly identified ground truth structure in realistic simulation-based settings. When we used it to analyze local field potentials recorded from prefrontal cortex and visual area V4 we obtained highly plausible results. The new statistical methodology could also be applied to other slowly-varying high-dimensional time series.

翻译：长期以来，神经振荡一直被认为是大脑区域间相互作用的重要标志，但从高维多电极记录中识别协调的振荡活动仍然具有挑战性。我们试图基于两个大脑区域各96个电极记录的局部场电位，在记忆任务期间量化两个脑区之间振荡振幅的时变协变关系。我们通过潜在时间序列的互相关，将典型相关分析（CCA）扩展到多时间序列。然而，这引入了两个区域之间大量可能的超前-滞后互相关。为了处理这种高维性，我们开发了严格的统计程序，旨在找出少数主导的超前-滞后效应。该方法在基于现实模拟的设置中正确识别了真实结构。当我们将其用于分析从前额叶皮层和视觉区域V4记录的局部场电位时，获得了高度可信的结果。这种新的统计方法也可应用于其他缓慢变化的高维时间序列。