Sleep spindle activity correlates with implicit statistical learning consolidation in untreated obstructive sleep apnea patients

Stevens, David; Leong, Celeste W.Y.; Cheung, Helena; Arciuli, Joanne; Vakulin, Andrew; Kim, Jong Won; Openshaw, Hannah; Rae, Caroline; Wong, Keith; Dijk, Derk Jan; Leow, Josiah Wei Siong; Saini, Bandana; Grunstein, Ronald R.; D'Rozario, Angela L

doi:10.1016/j.sleep.2021.01.035

Cited by 9 publications

(6 citation statements)

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“…Higher spindle occurrence (and density) have been associated with better memory performance and vigilance (Lafortune et al, 2014;Hennies et al, 2016) in cross-sectional studies with moderate sample sizes (n < 100). In a clinical population of 47 patients with Obstructive sleep apnea (OSA) Headers are correct Confirmed, greater sleep spindle activity was associated with better implicit learning (Stevens et al, 2021). A recent analysis of two large US-cohorts (n∼3800) also supported an association between higher spindle occurrence and spindle power with greater performance on multiple cognitive tests (Djonlagic et al, 2021).…”

Section: Sleep Spindlesmentioning

confidence: 93%

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

et al. 2021

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Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

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Section: Sleep Spindlesmentioning

confidence: 93%

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

et al. 2021

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“…Previous research by our team found that hypercapnia had negative impacts on the logical memory and working memory of OSAS patients, especially on delayed logical memory, verbal working memory and spatial working memory impairment [ 43 ]. Stevens et al found that spindle activity was diminished in the frontal and central regions during NREM, causing impaired learning consolidation [ 44 ]. The present study further finds that the hyper-connectivity of the sleep spindle between the left and right brain areas in the central and prefrontal regions is related to visual memory and logical memory.…”

Section: Discussionmentioning

confidence: 99%

Sleep Spindle Characteristics and Relationship with Memory Ability in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome

Zhu

Han

Wang

et al. 2023

JCM

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Obstructive sleep apnea syndrome (OSAS) causes intermittent hypoxia and sleep disruption in the brain, resulting in cognitive dysfunction, but its pathogenesis is unclear. The sleep spindle wave is a transient neural event involved in sleep memory consolidation and synaptic plasticity. This study aimed to investigate the characteristics of sleep spindle activity and its relationship with memory ability in patients with OSAS. A total of 119 patients, who were divided into the OSAS group (n = 59, AHI ≥ 15) and control group (n = 60, AHI < 15) according to the Apnea Hypopnea Index (AHI), were enrolled and underwent polysomnography. Power spectral density (PSD) and omega complexity were used to analyze the characteristics of single and different brain regions of sleep spindles. Memory-related cognitive functions were assessed in all subjects, including logical memory, digit ordering, pattern recognition, spatial recognition and spatial working memory. The spindle PSD of the OSAS group was significantly slower than the control group, regardless of the slow, fast, or total spindle. The complexity of the spindles in the prefrontal and central region decreased significantly, whereas it increased in the occipital region. Sleep spindle PSD was positively correlated with logical memory and working memory. Spindle complexity was positively correlated with immediate logical and visual memory in the prefrontal region and positively correlated with immediate/delayed logical and working memory in the central region. In contrast, spindle complexity in the occipital region negatively correlated with delayed logical memory. Spindle hyperconnectivity in the prefrontal and central regions underlies declines in logical, visual and working memory and weak connections in the occipital spindles underlie the decline in delayed logical memory.

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“…The literature in cluster 21 “caffeine” showed that QEEG can be used to study the neural mechanism underlying the withdrawal response to drugs, such as caffeine and cocaine ( 119 , 120 ). Cluster 24 “obstructive sleep apnea (OSA)” suggested that QEEG during sleep could help to reveal the pathological mechanism of OSA, while awake QEEG could evaluate the impact of OSA on cognitive functions ( 118 , 121 , 122 ).…”

Section: Discussionmentioning

confidence: 99%

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

Yao

Zhu

et al. 2022

Front. Psychiatry

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BackgroundWith the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.MethodsQEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.ResultsA total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.ConclusionThe present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

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Sleep spindle activity correlates with implicit statistical learning consolidation in untreated obstructive sleep apnea patients

Cited by 9 publications

References 48 publications

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Sleep Spindle Characteristics and Relationship with Memory Ability in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

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