Predicting time‐resolved electrophysiological brain networks from structural eigenmodes

Abstract

How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting‐state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time‐resolved amplitude connectivity. Time‐resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co‐occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time‐resolved resting‐state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions.

Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts

“…However, more sophisticated analysis tools exist to capture bursting events, such as the Hidden Markov modelling approach that is sensitive to distinct spectral features of bursts 31 and the "better oscillation detection" (BOSC) method, which are presumably less sensitive to noise and artefacts in the data 42 . The role for coincident bursting may be supported by studies on timeresolved functional connectivity that have demonstrated very brief periods (~0-400 ms) at which dynamic amplitude connectivity exceeds the level of stationary connectivity [43][44][45] . Importantly, a previous paper on coincident bursting using Hidden Markov Modelling indeed showed striking resemblance of coincident bursting events to power correlations 46 , though this was only analysed by assessing correlations without offering a clear mathematical and explanatory framework that explains the dissociation between coherence and power correlations.…”

A Joint Subspace Mapping Between Structural and Functional Brain Connectomes

“…To study the connection between the structural Laplacian eigenmodes, functional connectome eigenmodes, and joint eigenmodes, we also recall the recent works in [57, 58]. The role of structural eigenmodes in the formation and dissolution of temporally evolving functional brain networks was shown in [59]. Authors in [57] investigated the association between spatially extended structural networks and functional networks using a multivariate statistical technique, partial least squares.…”

Disruption in structural–functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness

“…Subsequently, eigenvectors and eigenvalues (together called eigenmodes) were extracted using diagonalization of , resulting in N eigenmodes ( N = number of ROIs). Using a recently introduced approach we mapped functional brain networks at each time point from the structural eigenmodes ( Tewarie et al, 2022 ; Tewarie et al, 2020 ). In other words, we estimated to what extent functional connectivity at each time point could be explained by a linear combination of the eigenmodes as follows: , …”