Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep

Rue-Queralt et al. present a method for calculating low dimensional manifolds in functional magnetic resonance imaging data and use it across human sleep-wake cycles. Their results indicate that non-REM sleep states occupy distinct areas of this intrinsic manifold and can be used to differentiate stages of sleep and waking. Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.

Automated summary

The study presents a method to calculate low dimensional manifolds in human brain dynamics, revealing nonlinear differences between wakefulness and various sleep stages, with a high decoding accuracy of 96%. Interestingly, the intrinsic manifolds of all participants share a common topology, indicating a consistent underlying structure in brain activity dynamics.