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Abstract
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.
Highlights
Current state-of-the-art functional magnetic resonance imaging offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states remains unknown
The word continuity, is used here in the spatial sense referring to the structured and non-random spatial distribution of data points on the intrinsic manifold and does not refer to the temporal smoothness, i.e., temporal continuity of the data points on the manifold. In this low dimensional representation, time-points acquired during the same stage lie together, yet different stages fall onto different branches and are separated through shortcuts in the temporal dynamics, where the brain dynamics exhibit a jump on the intrinsic manifold from one branch to that of another
Our results suggest that classification performed on the intrinsic manifold of brain dynamics measured with functional magnetic resonance imaging (fMRI) allows for an accurate decoding of the different sleep stages as well as wakefulness
Summary
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. We hypothesized that due to the anatomical and physiological constraints, and most importantly, due to strong correlations among neural populations[17], does the largescale dynamics of human brain activity span a lower-dimensional subspace but it lies on a smooth manifold Besides testing this hypothesis we investigate whether this compact manifold representation can be utilized to characterize different dynamical regimes in the space of all brain states, in particular to characterize different stages of the human sleep cycle. These techniques assume that relations between the time courses of two different voxels (in fMRI) or electrodes (in EEG) are linear; and neglect the nonlinear properties of brain activity, which have been suggested to be relevant for sleep processes: evidence from intracortical recordings have pointed out the differences in the occurrence of neuronal avalanches between wakefulness and sleep[33], and it has been demonstrated that the addition of nonlinear features of the data can improve the discrimination between sleep stages using scalp EEG34
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