The structural connectome constrains fast brain dynamics
Abstract
Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r=0.37, <0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.
Data availability
The MEG data and the reconstructed avalanches are available upon request to the corresponding author (Pierpaolo Sorrentino), conditional on appropriate ethics approval at the local site. The availability of the data was not previously included in the ethical approval, and therefore data cannot be shared directly. In case data are requested, the corresponding author will request an amendment to the local ethical committee. Conditional to approval, the data will be made available. The Matlab code is available at https://github.com/pierpaolosorrentino/Transition-Matrices-
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Funding
No external funding was received for this work.
Ethics
Human subjects: All participants gave written informed consent. The study complied with the declaration of Helsinki and was approved by the local Ethics Committee (Prot.n.93C.E./Reg. n.14-17OSS).
Copyright
© 2021, Sorrentino et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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