The Color of Noise and Weak Stationarity at the NREM to REM Sleep Transition in Mild Cognitive Impaired Subjects

Abstract Introduction Narcolepsy is a chronic sleep disorder characterized by excessive daytime sleepiness and abnormal REM sleep phenomena. Narcolepsy can be distinguished into type 1 (NT1; with cataplexy) and type 2 (NT2; without cataplexy). It has been reported that sleep stage sequences at sleep-onset as well as sleep-wake dynamics across the night may be useful in the differential diagnosis of hypersomnia. Here we studied dynamic features of sleep stage transitions during whole night sleep in patients with NT1, NT2, and other types of hypersomnia (o-HS). Methods Twenty patients with NT1, 14 patients with NT2, and 35 patients with o-HS underwent overnight PSG. Transition probabilities between sleep stages (wake, N1, N2, N3, and REM) and survival curves of continuous runs of each sleep stage were compared between groups. Transition-specific survival curves of continuous runs of each sleep stage, dependent on the subsequent stage of the transition, were also compared. Results The probability of transitions from N1-to-wake was significantly greater in NT1 than in NT2 and o-HS while that from N1-to-N2 was significantly smaller in NT1 than in NT2 and o-HS. The probability of transitions from N2-to-REM was significantly smaller in NT1 than in o-HS. Wake and N1 were significantly more continuous in NT1 than in NT2; specifically, N1 followed by N2 was significantly more continuous in NT1 than in NT2 and o-HS. N2 was significantly less continuous in NT1 and NT2 than in o-HS; this was specifically confirmed for N2 followed by N1/wake. REM sleep was significantly less continuous in NT1 than in NT2 and o-HS; specifically, REM sleep followed by wake was significantly less continuous in NT1 than in o-HS. Continuity of N3 did not differ significantly between groups. Conclusion Dynamics of sleep stage transitions differed between NT1, NT2, and o-HS. Dynamic features of sleep such as sleep instability, persistency of wake/N1, and REM fragmentation may differentiate NT1 from NT2, while N2 continuity may differentiate narcolepsy from o-HS. The results suggest that sleep transition analysis may be of clinical utility and provide insights into the underlying pathophysiology of hypersomnia and narcolepsy. Support JSPS KAKENHI (18K17891 to AK).

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REM sleep-dependent short-term and long-term hourglass processes in the ultradian organization and recovery of REM sleep in the rat

SLEEP ◽

10.1093/sleep/zsaa023 ◽

2020 ◽

Vol 43 (8) ◽

Author(s):

Adrián Ocampo-Garcés ◽

Alejandro Bassi ◽

Enzo Brunetti ◽

Jorge Estrada ◽

Ennio A Vivaldi

Keyword(s):

Sleep Deprivation ◽

Rem Sleep ◽

Transition Probability ◽

Nrem Sleep ◽

Short Term ◽

Rem Sleep Deprivation ◽

Sleep Episode ◽

Organization Of Sleep ◽

Sleep Transition

Abstract Study Objectives To evaluate the contribution of long-term and short-term REM sleep homeostatic processes to REM sleep recovery and the ultradian organization of the sleep wake cycle. Methods Fifteen rats were sleep recorded under a 12:12 LD cycle. Animals were subjected during the rest phase to two protocols (2T2I or 2R2I) performed separately in non-consecutive experimental days. 2T2I consisted of 2 h of total sleep deprivation (TSD) followed immediately by 2 h of intermittent REM sleep deprivation (IRD). 2R2I consisted of 2 h of selective REM sleep deprivation (RSD) followed by 2 h of IRD. IRD was composed of four cycles of 20-min RSD intervals alternating with 10 min of sleep permission windows. Results REM sleep debt that accumulated during deprivation (9.0 and 10.8 min for RSD and TSD, respectively) was fully compensated regardless of cumulated NREM sleep or wakefulness during deprivation. Protocol 2T2I exhibited a delayed REM sleep rebound with respect to 2R2I due to a reduction of REM sleep transitions related to enhanced NREM sleep delta-EEG activity, without affecting REM sleep consolidation. Within IRD permission windows there was a transient and duration-dependent diminution of REM sleep transitions. Conclusions REM sleep recovery in the rat seems to depend on a long-term hourglass process activated by REM sleep absence. Both REM sleep transition probability and REM sleep episode consolidation depend on the long-term REM sleep hourglass. REM sleep activates a short-term REM sleep refractory period that modulates the ultradian organization of sleep states.

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S40-1 Emergence of REM sleep features during wakefulness-sleep transition

Clinical Neurophysiology ◽

10.1016/s1388-2457(10)60248-9 ◽

2010 ◽

Vol 121 ◽

pp. S59

Author(s):

R. Bodizs

Keyword(s):

Rem Sleep ◽

Sleep Transition

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Electrographic status epilepticus during non-REM sleep: A report of three cases

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)89042-x ◽

1997 ◽

Vol 103 (1) ◽

pp. 216

Author(s):

S Hajnsek

Keyword(s):

Status Epilepticus ◽

Rem Sleep

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Spectral and Topographic Microstructure of Brain Alpha Activity During Drowsiness at Sleep Onset and REM Sleep

Journal of Psychophysiology ◽

10.1027//0269-8803.14.3.151 ◽

2000 ◽

Vol 14 (3) ◽

pp. 151-158 ◽

Cited By ~ 6

Author(s):

José Luis Cantero ◽

Mercedes Atienza

Keyword(s):

Rem Sleep ◽

Spectral Component ◽

Sleep Onset ◽

Quantitative Information ◽

Maximum Energy ◽

Alpha Activity ◽

The Other ◽

Classification Algorithms ◽

Microstructural Properties ◽

Brain States

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.

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