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Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
(2019)
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.
As language rhythm relies partly on general acoustic properties, such as intensity and duration, mastering two languages with distinct rhythmic properties (i.e., stress position) may enhance musical rhythm perception. We investigated whether second language (L2) competence affects musical rhythm aptitude in Turkish early (TELG) and late learners (TLLG) of German in comparison to German monolingual speakers (GMC). To account for inter-individual differences, we measured participants’ short-term and working memory capacity, melodic aptitude, and time they spent listening to music. Both L2 speaker groups perceived rhythmic variations significantly better than monolinguals. No differences were found between early and late learners’ performances. Our findings suggest that mastering two languages with different rhythmic properties enhances musical rhythm perception, providing further evidence of cognitive share between language and music.