The intrinsic ignition method revealed that the mean Ignition-Driven Mean Integration across brain areas was significantly different between wakefulness and deep sleep.
Observational (n=63)
A novel intrinsic ignition method applied to fMRI data successfully distinguishes between wakefulness and deep sleep by measuring the dynamical complexity and integration of brain activity.
p-value: p=<0.0001
A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing 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.
Deco et al. (2017) conducted an observational in Healthy volunteers (n=63). Deep sleep (N3 stage) vs. Wakefulness was evaluated on Mean Ignition-Driven Mean Integration (IDMI) across brain areas (p=<0.0001). The intrinsic ignition method revealed that the mean Ignition-Driven Mean Integration across brain areas was significantly different between wakefulness and deep sleep.