Abstract
The brain rapidly processes and adapts to new information by dynamically transitioning between whole-brain functional networks. In this whole-brain modeling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100–900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify the richness of the dynamic repertoire at each spatiotemporal scale by computing the entropy of transitions between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 ms. Overall, this study provides much needed evidence for the relevant spatiotemporal scales and recommendations for analyses of brain dynamics.
Highlights
The brain is a dynamical system that can process and adapt to new information by rapidly transitioning between multiple states
By focusing our analysis on functional networks, we ensured that the information we gained on the temporal dynamics was relevant for whole-brain information processing
As our study focused on the dynamical alteration of functional networks, we ensured that the properties of the resulting functional networks from the simulation were comparable to the properties of the networks derived from the empirical time series
Summary
The brain is a dynamical system that can process and adapt to new information by rapidly transitioning between multiple states. These functional states contain orchestrated activity of several networks of brain regions, which transition between each other in recurring patterns over time (Alexandrov, 1999; Ashourvan et al, 2017; van der Meer et al, 2020) These network transitions have been associated with cognition and (ab)normal behavior (Engel et al, 2001; Bassett et al, 2011; Thompson et al, 2013; Vidaurre et al, 2017; Liégeois et al, 2019; Yoo et al, 2020). This unclarity has led to arbitrary decisions in neuroimaging experiments regarding spatiotemporal scales, risking losing relevant information on network transitions
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