Changes in sleep structure and sleep spindles are associated with the neuropsychiatric profile in paradoxical insomnia

Şenel, Gülçin Benbir; Aydın, Ömer; Aydın, Ezgi Tanrıöver; Bayar, Mahmut Reha; Karadeniz, Derya

doi:10.1016/j.ijpsycho.2021.07.626

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…While our algorithmic approach to space-time detection of sleep rhythms profiles is a step removed from current clinical sleep settings, our data-driven approach can reveal structural properties of spindle space-time patterns that can be different in clinical populations, for example in those with generalized epilepsies that are thalamic-driven [ 85 , 86 ]. Greater understanding of the space-time profiles of spindles may contribute to revealing mechanisms underlying a wide range of functions and interventions, including targeting specific memories for reinforcement [ 62 , 87 ] or deletion [ 88 ]; developmental changes [ 56 , 89 ] and clinical conditions [ 90–93 ]; attentional processes [ 94 ], as well as indicators of consciousness [ 95 ], and as factors contrasting sleep fragility [ 96 , 97 ].…”

Section: Discussionmentioning

confidence: 99%

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Malerba

Whitehurst

Mednick

2022

Sleep

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Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.

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Section: Discussionmentioning

confidence: 99%

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Malerba

Whitehurst

Mednick

2022

Sleep

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“…TST discrepancy has commonly been used to describe sleep perception [ 16 ]. In our study, the TST was overestimated and objective TST was the shortest in cluster 2, with a mean value of only 338.5 min.…”

Section: Discussionmentioning

confidence: 99%

“…As previously, we used differences in subjective and objective TST, SOL, and WASO to reflect sleep discrepancy. A cutoff of 60 min was used for TST discrepancy (TSTdis), with TST underestimation defined as a TSTdis < − 60 min, normal perception as − 60 min ≤ TSTdis ≤ 60 min, and overestimation as TSTdis > 60 min [ 16 ]. A cutoff of 30 min was used for SOL discrepancy (SOLdis) [ 17 ], with SOL underestimation defined as SOLdis < − 30 min, normal perception as − 30 min ≤ SOLdis ≤ 30 min, and overestimation as TSTdis > 30 min.…”

Section: Methodsmentioning

confidence: 99%

Clinical phenotypes of obstructive sleep apnea: a cluster analysis based on sleep perception and sleep quality

et al. 2023

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Purpose To determine obstructive sleep apnea (OSA) phenotypes using cluster analysis including variables of sleep perception and sleep quality and to further explore factors correlated with poor sleep quality in different clusters. Methods This retrospective study included patients with OSA undergoing polysomnography (PSG) between December 2020 and April 2022. Two-step cluster analysis was performed to detect distinct clusters using sleep perception variables including discrepancy in total sleep time (TST), sleep onset latency (SOL), and wakefulness after sleep onset (WASO); objective TST, SOL, and WASO; and sleep quality. One-way analysis of variance or chi-squared tests were used to compare clinical and PSG characteristics between clusters. Binary logistic regression analyses were used to explore factors correlated with poor sleep quality. Results A total of 1118 patients were included (81.6% men) with mean age ± SD 43.3 ± 13.1 years, Epworth sleepiness score, 5.7 ± 4.4, and insomnia severity index 3.0 ± 2.4. Five distinct OSA clusters were identified: cluster 1 (n = 254), underestimated TST; cluster 2 (n = 158), overestimated TST; cluster 3 (n = 169), overestimated SOL; cluster 4 (n = 155), normal sleep discrepancy and poor sleep quality; and cluster 5 (n = 382), normal sleep discrepancy and good sleep quality. Patients in cluster 2 were older, more commonly had hypertension, and had the lowest apnea–hypopnea index and oxygen desaturation index. Age and sleep efficiency were correlated with poor sleep quality in clusters 1, 2, and 5, and also AHI in cluster 2. Conclusion Subgroups of patients with OSA have different patterns of sleep perception and quality that may help us to further understand the characteristics of sleep perception in OSA and provide clues for personalized treatment.

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“…These individuals have excessive anxiety because of not getting enough sleep or because of the daytime consequences of night-time disturbances, which result in triggering or worsening of insomnia. Changes in the microstructural elements of sleep (such as sleep spindles) have been shown in paradoxical insomnia, correlated with the increased level of uncontrollable and persistent anxiety in the Penn State Worry Questionnaire and the extroversion dimension in the Eysenck Personality Questionnaire [9,10]. When the focus and selective attention to initiate sleep increases, the awareness of the environment also increases, leading to the hyperarousal state.…”

Section: Pathophysiologymentioning

confidence: 99%

Insomnia – Is it a Symptom or a Disorder?

Şenel¹

2022

TONEUJ

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Insomnia disorder is a common public health problem with a prevalence of approximately 2-5% of the population. It is of major importance to differentiate the insomnia disorder from the isolated symptoms and normal variants, and to define the secondary or associated conditions. Insomnia was mainly classified as acute and chronic insomnia disorder based on the 3rd edition of the International Classification of Sleep Disorders. Many models have been developed to explain the underlying mechanisms of insomnia, such as the Drosophila model, the cognitive model, the psychobiological inhibition model, the neurocognitive model, stimulus control model, hyperarousal model and the “3P model” (Spielman model). Optimizing the environmental conditions, lifestyle changes and elaborating the triggering factors are the first step in the management of insomnia disorders.

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Changes in sleep structure and sleep spindles are associated with the neuropsychiatric profile in paradoxical insomnia

Cited by 10 publications

References 25 publications

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Clinical phenotypes of obstructive sleep apnea: a cluster analysis based on sleep perception and sleep quality

Insomnia – Is it a Symptom or a Disorder?

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