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

Andrew J Quinn,Gary G.R Green,Mark Hymers

doi:10.1016/j.neuroimage.2021.118330

Andrew J Quinn, Gary G.R Green + Show 1 more

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https://doi.org/10.1016/j.neuroimage.2021.118330

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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 be isolated and analysed in terms of their spectral content, spatial distribution and network structure. We validate the method on simulated data and explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide between participant variability in peak frequency and network structure of alpha oscillations and show a distinction between occipital ’high-frequency alpha’ and parietal ’low-frequency alpha’. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person’s behavioural, cognitive or clinical state.

Highlights

The wide variety of oscillatory phenomena in electrophysiological recordings of brain function reflect thee synchronised activity of underlying neuronal networks [1, 2]
Two modes with distinct spectral structures 450 were defined by direct pole placement and used to generate time-courses which were projected into a 10 node network structure. 20 realisations of 300 seconds in duration were simulated from this network structure
We have shown that a modal decomposition of multivariate autoregressive (MVAR) parameters can be used to simultaneously estimate spatial and frequency structure within human resting state MEG data

Summary

Introduction

The wide variety of oscillatory phenomena in electrophysiological recordings of brain function reflect thee synchronised activity of underlying neuronal networks [1, 2] These oscillatory signatures have a rich frequency spectrum that 5 shows meaningful between subject variability across cortex and between participants. We present a data driven modal decomposition analysis approach which identifies oscillations in multivariate time-series and characterises their peak frequency, damping time, spatial topography and network structure. We apply this method to explore the macro-structure of alpha oscillations in Human neocortex and how this varies between subjects

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