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Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes
Andrew J Quinn1, Gary G R Green2, Mark Hymers2
1Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University Department of Psychiatry, Warneford Hospital, Oxford OX3 7JX, UK.
Neuroimage
|July 8, 2021
Summary
This study introduces Spatio-Spectral Eigenmodes (SSEs) to analyze brain oscillations, revealing significant individual differences in alpha frequency and network structure. This method enhances understanding of neural dynamics and individual phenotypes.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Signal Processing
Background:
- Individual variability in brain oscillatory networks is informative but analytically challenging.
- Existing methods struggle to simultaneously capture spatial, spectral, and network properties of oscillations.
Purpose of the Study:
- To develop a data-driven method for decomposing multivariate autoregressive models of oscillatory networks.
- To define Spatio-Spectral Eigenmodes (SSEs) for a parsimonious description of neural oscillations.
- To analyze individual differences in brain oscillatory network structure and spectral content.
Main Methods:
- Modal decomposition of a multivariate autoregressive model.
- Identification of oscillations by peak frequency, damping time, and network structure.
- Rewriting the system transfer function in modal coordinates.
- Analysis of resting-state MEG data from the Human Connectome Project.
Main Results:
- The proposed Spatio-Spectral Eigenmodes (SSEs) provide a parsimonious description of oscillatory networks.
- The multivariate system transfer function is a linear superposition of SSEs.
- Significant between-participant variability in peak frequency and network structure of alpha oscillations was observed.
- A distinction between occipital 'high-frequency alpha' and parietal 'low-frequency alpha' was identified within individuals.
Conclusions:
- The Spatio-Spectral Eigenmodes (SSEs) method effectively isolates and analyzes individual oscillatory signals.
- Characterizing individual neural phenotypes through oscillatory dynamics can enhance studies of behavior, cognition, and clinical states.
- Understanding individual differences in neural oscillations is crucial for personalized neuroscience.
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