Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes.

ABSTRACT: Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive model that simultaneously identifies oscillations by their peak frequency, damping time and network structure. We use this decomposition to define a set of Spatio-Spectral Eigenmodes (SSEs) providing a parsimonious description of oscillatory networks. We show that the multivariate system transfer function can be rewritten in these modal coordinates, and that the full transfer function is a linear superposition of all modes in the decomposition. The modal transfer function is a linear summation and therefore allows for single oscillatory signals to

SUBMITTER: Quinn AJ 

PROVIDER: S-EPMC8456753 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature