Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia

Wei, Yishul; Colombo, Michele; Ramautar, Jennifer; Blanken, Tessa F.; Werf, Ysbrand D. van der; Spiegelhalder, Kai; Feige, Bernd; Riemann, Dieter; Someren, Eus J.W. Van

doi:10.1093/sleep/zsx117

Cited by 44 publications

(47 citation statements)

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“…Patients with severe OSA had notably more N1 → N2 → wake, N2 → wake → N2 and N2 → N1 → N2 transitions compared with controls, which agrees with results obtained by Schlemmer et al (2015) and implies a N2 vulnerability. A N2 vulnerability was also found in patients with insomnia (Wei et al, 2017). In patients over 60 years, we have seen significantly higher frequencies for N1 → wake → N1 and wake → N1 → wake transitions.…”

Section: Discussionsupporting

confidence: 66%

“…() and implies a N2 vulnerability. A N2 vulnerability was also found in patients with insomnia (Wei et al., ). In patients over 60 years, we have seen significantly higher frequencies for N1 → wake → N1 and wake → N1 → wake transitions.…”

Section: Discussionmentioning

confidence: 87%

“…Recent research showed that patients with narcolepsy have an altered wake episode duration, while the sleep episode duration was unaltered (Zhang et al, 2017). Furthermore, sleep dynamic analysis could distinguish narcolepsy type 1 from type 2 patients (Pizza et al, 2015), demarcate patients with chronic fatigue syndrome and those with fibromyalgia (Kishi et al, 2011), and disclosed a N2 vulnerability in insomnia (Wei et al, 2017). In patients with OSA, transition analysis revealed more wake to NREM sleep and NREM sleep to wake transitions compared with subjects without OSA (Swihart et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Unique sleep‐stage transitions determined by obstructive sleep apnea severity, age and gender

Wächter

Kantelhardt

Bonsignore

et al. 2019

Journal of Sleep Research

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Summary In obstructive sleep apnea, patients’ sleep is fragmented leading to excessive daytime sleepiness and co‐morbidities like arterial hypertension. However, traditional metrics are not always directly correlated with daytime sleepiness, and the association between traditional sleep quality metrics like sleep duration and arterial hypertension is still ambiguous. In a development cohort, we analysed hypnograms from mild (n = 209), moderate (n = 222) and severe (n = 272) obstructive sleep apnea patients as well as healthy controls (n = 105) from the European Sleep Apnea Database. We assessed sleep by the analysis of two‐step transitions depending on obstructive sleep apnea severity and anthropometric factors. Two‐step transition patterns were examined for an association to arterial hypertension or daytime sleepiness. We also tested cumulative distributions of wake as well as sleep‐states for power‐laws (exponent α) and exponential distributions (decay time τ) in dependency on obstructive sleep apnea severity and potential confounders. Independent of obstructive sleep apnea severity and potential confounders, wake‐state durations followed a power‐law distribution, while sleep‐state durations were characterized by an exponential distribution. Sleep‐stage transitions are influenced by obstructive sleep apnea severity, age and gender. N2 → N3 → wake transitions were associated with high diastolic blood pressure. We observed higher frequencies of alternating (symmetric) patterns (e.g. N2 → N1 → N2, N2 → wake → N2) in sleepy patients both in the development cohort and in a validation cohort (n = 425). In conclusion, effects of obstructive sleep apnea severity and potential confounders on sleep architecture are small, but transition patterns still link sleep fragmentation directly to obstructive sleep apnea‐related clinical outcomes like arterial hypertension and daytime sleepiness.

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Section: Discussionsupporting

confidence: 66%

Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Unique sleep‐stage transitions determined by obstructive sleep apnea severity, age and gender

Wächter

Kantelhardt

Bonsignore

et al. 2019

Journal of Sleep Research

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“…An extensive meta-analysis shows that persons with insomnia have significantly reduced slow-wave sleep compared with controls with no sleep problems , and the network of reduced connectivity identified in this study may be one of the mechanisms behind the reduction in slow-wave sleep in insomnia. Furthermore, a recent study shows that the sleep EEG of Insomnia Disorder is characterized most by a difficulty to transition from N2 sleep to the deeper sleep stage N3 characterized by slow waves (Wei et al, 2017). Concertedly, these previous findings and the current results make it tempting to suggest involvement of the reduced connectivity of the left insula and frontal regions in persons with Insomnia Disorder in their difficulty to enter slow-wave sleep.…”

Section: Left Hemisphere Lateralizationsupporting

confidence: 57%

Reduced structural connectivity in Insomnia Disorder

Jespersen

Stevner

Fernandes

et al. 2019

Journal of Sleep Research

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Insomnia Disorder is the most prevalent sleep disorder, and it involves both sleep difficulties and daytime complaints. The neural underpinnings of Insomnia Disorder are poorly understood. Several existing neuroimaging studies focused on local measures and specific regions of interests, which makes it difficult to judge their whole‐brain significance. We therefore here applied a data‐driven approach to assess differences in whole‐brain structural connectivity between adults with Insomnia Disorder and matched controls without sleep complaints. We used diffusion tensor imaging and probabilistic tractography to assess whole‐brain structural connectivity, and examined group differences using network‐based statistics. The results revealed a significant difference in the structural connectivity of the two groups (p = .014). Participants with Insomnia Disorder showed reduced connectivity in a sub‐network that included mainly fronto‐subcortical connections with the insula as a key region. By taking a whole‐brain network perspective, our study enables the integration of previous inconsistent findings. Our results reveal that reduced structural connectivity of the left insula and the connections between frontal and subcortical regions are central neurobiological features of Insomnia Disorder. The importance of these areas for interoception, emotional processing, stress responses and the generation of slow‐wave sleep may help guide the development of neurobiology‐based models of the prevalent condition of Insomnia Disorder.

show abstract

“…An extensive meta-analysis shows that persons with insomnia have significantly reduced slow wave sleep compared to controls with no sleep problems , and the network of reduced connectivity identified in this study may be one of the mechanisms behind the reduction in slow wave sleep in insomnia. Furthermore, a recent study shows that the sleep EEG of Insomnia Disorder is characterized most by a difficulty to transition from N2 sleep to the deeper sleep stage N3 characterized by slow waves (Wei et al, 2017). Concertedly, these previous findings and the current results make it tempting to suggest involvement of the reduced connectivity of the left insula and frontal regions in persons with Insomnia Disorder in their difficulty to enter slow-wave sleep.…”

Section: Left Hemisphere Lateralizationsupporting

confidence: 57%

Reduced structural connectivity in Insomnia Disorder

Jespersen

Stevner

Fernandes

et al. 2019

Preprint

View full text Add to dashboard Cite

Insomnia Disorder is the most prevalent sleep disorder and it involves both sleep difficulties and daytime complaints. The neural underpinnings of Insomnia Disorder are poorly understood.Existing neuroimaging studies are limited by their focus on local measures and specific regions of interests. To address this shortcoming, we applied a data-driven approach to assess differences in whole-brain structural connectivity between adults with Insomnia Disorder and matched controls without sleep complaints. We used diffusion tensor imaging and probabilistic tractography to assess whole-brain structural connectivity and examined group differences using Network-Based Statistics. The results revealed a significant difference in the structural connectivity of the two groups. Participants with Insomnia Disorder showed reduced connectivity in a subnetwork that was largely left lateralized, including mainly fronto-subcortical connections with the insula as a key region. By taking a whole-brain network perspective, our study succeeds at integrating previous inconsistent findings, and our results reveal that reduced structural connectivity of the left insula and the connections between frontal and subcortical regions are central neurobiological features of Insomnia Disorder. The importance of these areas for interoception, emotional processing, stress responses and the generation of slow wave sleep may help guide the development of neurobiologybased models of the highly prevalent condition of Insomnia Disorder.

show abstract

Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia

Abstract: Quantification of sleep stage dynamics revealed a particular vulnerability of stage N2 in insomnia. The feature characterizes insomnia better than-and independently of-any conventional sleep parameter.

Cited by 44 publications

References 44 publications

Unique sleep‐stage transitions determined by obstructive sleep apnea severity, age and gender

Unique sleep‐stage transitions determined by obstructive sleep apnea severity, age and gender

Reduced structural connectivity in Insomnia Disorder

Reduced structural connectivity in Insomnia Disorder

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