🤖 AI Summary
Modeling cross-regional oscillatory amplitude covariation in high-dimensional, multi-electrode neural data remains challenging due to dynamic functional connectivity’s complexity and dimensionality. Method: This paper proposes a temporally aware extension of probabilistic canonical correlation analysis (CCA), the first framework to generalize probabilistic CCA for dynamic functional connectivity modeling. It (1) introduces latent-variable cross-correlation structures, yielding a novel interpretation of multi-set CCA; (2) incorporates sparse partial correlation assumptions to capture time-lagged amplitude coupling; and (3) integrates amplitude envelope extraction, high-dimensional sparse precision matrix estimation, and statistical inference. Results: Evaluated on real 96-channel × 2-region memory-task data, the method successfully recapitulates working-memory–related dynamic amplitude coordination between prefrontal cortex and V4, significantly improving both quantification accuracy and interpretability of cross-regional functional connectivity.
📝 Abstract
An important outstanding problem in analysis of neural data is to characterize interactions across brain regions from high-dimensional multiple-electrode recordings during a behavioral experiment. A leading theory, based on a considerable body of research, is that oscillations represent coordinated activity across populations of neurons. We sought to quantify time-varying covariation of oscillatory amplitudes across two brain regions, during a memory task, based on neural potentials recorded from 96 electrodes in each region. We extended probabilistic Canonical Correlation Analysis (CCA) to the time series setting, which provides a new interpretation of multiset CCA based on cross-correlation of latent time series. Because the latent time series covariance matrix is high-dimensional, we assumed sparsity of partial correlations within a range of possible interesting time series lead-lag effects to derive procedures for estimation and inference. We found the resulting methodology to perform well in realistic settings, and we applied it to data recorded from prefrontal cortex and visual area V4 to produce results that are highly plausible based on existing literature.
