Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model

TL;DR

This study uses a spectral graph model and Bayesian inference to understand how EEG spectral features develop with age, revealing neurobiological changes in neural connectivity and dynamics during maturation.

Contribution

It introduces a Bayesian model inversion approach to fit a spectral graph model to developmental EEG data, linking spectral changes to underlying neural parameters.

Findings

Accurately captures developmental EEG spectral maturation

Identifies age-related changes in neural coupling and conduction speed

Supports neurobiological basis of spectral development

Abstract

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.