Abstract
A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterising the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states.
Significance Statement We introduce a novel intrinsic ignition method for characterising the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. We show that the intrinsic ignition method can clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Furthermore, we use whole-brain modeling to show that the optimal working point is found where there is maximal variability of intrinsic ignition across brain regions.
Footnotes
The authors declare to have no conflict of interest.
EC | European Research Council (ERC) [295129]; [615539].
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
Jump to comment: