Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling

Abstract

Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a supercritical Hopf bifurcation and studied the dynamical changes when adapting the bifurcation parameter for all brain nodes to best match wakefulness and slow-wave sleep. Furthermore, we analysed differences in effective connectivity between the two states. In addition to significant changes in functional connectivity, synchrony and metastability, this analysis revealed a significant shift of the global dynamic working point of brain dynamics, from the edge of the transition between damped to sustained oscillations during wakefulness, to a stable focus during slow-wave sleep. Moreover, we identified a significant global decrease in effective interactions during slow-wave sleep. These results suggest a mechanism for the empirical functional changes observed during slow-wave sleep, namely a global shift of the brain’s dynamic working point leading to increased stability and decreased effective connectivity.

Dynamical informational structures characterize the different human brain states of wakefulness and deep sleep

“…The lower stability of the awake state can also be inferred from the greater variability of the NoEL in that state according to our results. All this agrees with other studies in which authors claim that, using very different methodologies, that the brain exhibits less stability during wakefulness 80 .…”

“…Recall that this attractor or globally asymptotically stable solution (GASS) has all the components different from zero, that is, it consists of the active complete brain (the 90 active areas) which implies a high degree of effective connectivity. Therefore our results agree with the fact that during slow-wave sleep effective connectivity decreases 80 .…”

“…Although in all cases the highest NoEL was frequent, the most interesting patterns were observed when the frequencies for the maximum values q ∈ [80, 91], were not taken into account. When looking at the frequencies below 80, the probability grew linearly with the NoEL for the deep sleep condition, whereas it showed almost a constant behaviour for the wakefulness condition with q ∈ [1,80], as it can be seen from Fig.6C (red and blue respectively). The pattern typically observed was that the distribution for the awake state was more homogeneous along all the possible NoEL values.…”

Reliable local dynamics in the brain across sessions are revealed by whole‐brain modeling of resting state activity

“…In this work, we developed and applied a supercritical Hopf model (Deco, Kringelbach, et al, ; Jobst et al, ) that uses functional and structural connectivity (SC) information to simulate whole‐brain activity. Importantly, this model has been applied to disease (Saenger et al, ) and altered states of consciousness (Jobst et al, ), revealing important characteristics of the resting brain architecture. As shown in Figure , the model uses brain dynamics from fMRI data as well as the SC from diffusion weighted imaging (DWI) data to construct an interconnected network (Figure a).…”

Characterizing the Dynamical Complexity underlying Meditation

“…Theoretical methods have been successfully applied to characterize different states of consciousness 15 such as wakefulness, sleep, anesthesia or psychedelic states [20, 21,22,23,24,14].…”