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 ...
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.
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