Cross-Population Amplitude Coupling in High-Dimensional Oscillatory Neural Time Series

TL;DR

This paper introduces a novel statistical method to analyze high-dimensional neural oscillation data, effectively identifying dominant cross-region amplitude coupling and lead-lag effects during a memory task.

Contribution

It extends Canonical Correlation Analysis to high-dimensional neural data, providing a rigorous way to detect significant oscillatory interactions across brain regions.

Findings

Successfully identified ground truth structures in simulations

Detected plausible amplitude coupling in prefrontal cortex and V4

Method applicable to other high-dimensional time series

Abstract

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.