Nowadays, functional magnetic resonance imaging generates satisfactory representations of the brain. However, brain dynamics during different states remain unclear. Here, an analysis method using dimensionality reduction is developed to decode imaging data and elucidate spatiotemporal dynamics of brain activity in different states. The results elucidated nonlinear differences between different states and these data allowed the researchers to efficiently classify them. Moreover, further analysis of a subject group revealed a shared topology between individuals constrained by the brain state rather than the differences between participants. Overall, the researchers revealed the intrinsic manifold that describes brain dynamics and enables decoding and classifying different brain states.
Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep
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