Neural integration and segregation revealed by a joint time-vertex connectome spectral analysis

Abstract

Brain oscillations are produced by the coordinated activity of large groups of neurons and different rhythms are thought to reflect different modes of information processing. These modes, in turn, are known to occur at different spatial scales. Nevertheless, how these rhythms support different modes of information processing at the brain scale is not yet fully understood. Here we present "Joint Time-Vertex Connectome Spectral Analysis", a framework for characterizing the spectral content of brain activity both in time (temporal frequencies) and in space (spatial connectome harmonics). This method allows us to estimate the contribution of integration (global communication) and segregation (functional specialization) mechanisms at different temporal frequency bands in source-reconstructed M/EEG signals, thus providing a better understanding of the complex interplay between different information processing modes. We validated our method on two different datasets, an auditory steady-state response (ASSR) and a visual grating task. Our results suggest that different information processing mechanisms are carried out at different frequency channels: while integration seems to be a specific mechanism occurring at low temporal frequencies (alpha and theta), segregation is only observed at higher temporal frequencies (high and low gamma). Crucially, the estimated contribution of the integration and segregation mechanisms predicts performance in a behavioral task, demonstrating the neurophysiological relevance of this new framework.

Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain source-networks at high-spatial resolution

“…We have shown in previous publications that the neuronal activity is sparsely represented by the connectome-based graph Fourier transform (see Connectome Spectral Analysis ), which decomposes brain activity into a small number of active brain networks [35, 34]. In addition to our research, other studies have established a theoretical basis for the structural constraint on functional connectivity [32, 36, 47, 48, 49]. These works have revealed that neural mechanisms relying on delayed excitatory-inhibitory interactions facilitate the self-organization towards exciting a relevant set of eigenmodes [32, 48].…”

“…These works have revealed that neural mechanisms relying on delayed excitatory-inhibitory interactions facilitate the self-organization towards exciting a relevant set of eigenmodes [32, 48]. Moreover, previous investigations have successfully applied structural eigenmodes to explain brain activity during rest at very short timescale [49] and evoked activity [47]. These findings suggest that these eigenmodes play a crucial role in dynamically integrating and segregating information across the cortex, thus serving important cognitive functions.…”

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high‐spatial resolution

“…We have shown in previous publications that the neuronal activity is sparsely represented by the connectome-based graph Fourier transform (see Connectome Spectral Analysis), which decomposes brain activity into a small number of active brain networks (Glomb et al, 2020;Rué-Queralt et al, 2021). In addition to our research, other studies have established a theoretical basis for the structural constraint on functional connectivity (Atasoy et al, 2016;Atasoy et al, 2018;Rue Queralt et al, 2022;Tewarie et al, 2019;Tewarie et al, 2022). These works have revealed that neural mechanisms relying on delayed excitatory-inhibitory interactions facilitate the self-organization toward exciting a relevant set of eigenmodes (Atasoy et al, 2016;Tewarie et al, 2019).…”

“…These works have revealed that neural mechanisms relying on delayed excitatory‐inhibitory interactions facilitate the self‐organization toward exciting a relevant set of eigenmodes (Atasoy et al, 2016 ; Tewarie et al, 2019 ). Moreover, previous investigations have successfully applied structural eigenmodes to explain brain activity during rest at very short timescale (Tewarie et al, 2022 ) and evoked activity (Rue Queralt et al, 2022 ). These findings suggest that these eigenmodes play a crucial role in dynamically integrating and segregating information across the cortex, thus serving important cognitive functions.…”

Structure-function coupling and decoupling during movie-watching and resting-state: Novel insights bridging EEG and structural imaging

“…in Griffa and Preti, 2022;Preti and Van De Ville, 2019) or as the average connectivity matrix across the population (Rué-Queralt et al, 2023). Taken together, despite the important progress in probing the nature of structure-function coupling through the lens of task-based electrophysiological responses, the relationship between the continuous EEG and the underlying structural connectivity remains elusive.…”