Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia

Dang‐Vu, Thien Thanh; Hatch, Benjamin; Salimi, Ali; Mograss, Melodee; Boucetta, Soufiane; O’Byrne, Jordan; Brandewinder, Marie; Berthomier, Christian; Gouin, Jean‐Philippe

doi:10.1016/j.sleep.2017.08.012

Cited by 13 publications

(8 citation statements)

References 38 publications

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“…Concerning learning potential in declarative or procedural memory tasks, spindle activity correlated positively irrespective of whether it was mea- (228,391,393,528). Individual spindle activity also predicted the success of cognitive-behavioral therapy in treating insomnia (147). There is also strong evidence from a well-powered study for an important role of sex (603).…”

Section: Relation To Intelligence Quotientsmentioning

confidence: 94%

Sleep Spindles: Mechanisms and Functions

Fernandez

Lüthi

2020

Physiological Reviews

408

532

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Sleep spindles are burstlike signals in the electroencephalogram (EEG) of the sleeping mammalian brain and electrical surface correlates of neuronal oscillations in thalamus. As one of the most inheritable sleep EEG signatures, sleep spindles probably reflect the strength and malleability of thalamocortical circuits that underlie individual cognitive profiles. We review the characteristics, organization, regulation, and origins of sleep spindles and their implication in non-rapid-eye-movement sleep (NREMS) and its functions, focusing on human and rodent. Spatially, sleep spindle-related neuronal activity appears on scales ranging from small thalamic circuits to functional cortical areas, and generates a cortical state favoring intracortical plasticity while limiting cortical output. Temporally, sleep spindles are discrete events, part of a continuous power band, and elements grouped on an infraslow time scale over which NREMS alternates between continuity and fragility. We synthesize diverse and seemingly unlinked functions of sleep spindles for sleep architecture, sensory processing, synaptic plasticity, memory formation, and cognitive abilities into a unifying sleep spindle concept, according to which sleep spindles 1) generate neural conditions of large-scale functional connectivity and plasticity that outlast their appearance as discrete EEG events, 2) appear preferentially in thalamic circuits engaged in learning and attention-based experience during wakefulness, and 3) enable a selective reactivation and routing of wake-instated neuronal traces between brain areas such as hippocampus and cortex. Their fine spatiotemporal organization reflects NREMS as a physiological state coordinated over brain and body and may indicate, if not anticipate and ultimately differentiate, pathologies in sleep and neurodevelopmental, -degenerative, and -psychiatric conditions.

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Section: Relation To Intelligence Quotientsmentioning

confidence: 94%

Sleep Spindles: Mechanisms and Functions

Fernandez

Lüthi

2020

Physiological Reviews

408

532

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“…The spectral power was computed in the frequency bands delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), sigma (12–16 Hz), and beta (16–50 Hz), for all the automatically scored R and NREM epochs. After their automatic detection, the spindles were characterized by their duration (s), power (squared microvolts), maximum amplitude (microvolts), and frequency (Hz) [for more details on spindle detection method see (53)]. The density of spindles was computed as the average number of detected spindles per 30 s epoch for each subject.…”

Section: Methodsmentioning

confidence: 99%

Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population

et al. 2019

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Objective: Recent research suggests that sleep disorders or changes in sleep stages or EEG waveform precede over time the onset of the clinical signs of pathological cognitive impairment (e.g., Alzheimer's disease). The aim of this study was to identify biomarkers based on EEG power values and spindle characteristics during sleep that occur in the early stages of mild cognitive impairment (MCI) in older adults.Methods: This study was a case-control cross-sectional study with 1-year follow-up of cases. Patients with isolated subjective cognitive complaints (SCC) or MCI were recruited in the Bordeaux Memory Clinic (MEMENTO cohort). Cognitively normal controls were recruited. All participants were recorded with two successive polysomnography 1 year apart. Delta, theta, and sigma absolute spectral power and spindle characteristics (frequency, density, and amplitude) were analyzed from purified EEG during NREM and REM sleep periods during the entire second night.Results: Twenty-nine patients (8 males, age = 71 ± 7 years) and 29 controls were recruited at T0. Logistic regression analyses demonstrated that age-related cognitive impairment were associated with a reduced delta power (odds ratio (OR) 0.072, P < 0.05), theta power (OR 0.018, P < 0.01), sigma power (OR 0.033, P < 0.05), and spindle maximal amplitude (OR 0.002, P < 0.05) during NREM sleep. Variables were adjusted on age, gender, body mass index, educational level, and medication use. Seventeen patients were evaluated at 1-year follow-up. Correlations showed that changes in self-reported sleep complaints, sleep consolidation, and spindle characteristics (spectral power, maximal amplitude, duration, and frequency) were associated with cognitive impairment (P < 0.05).Conclusion: A reduction in slow-wave, theta and sigma activities, and a modification in spindle characteristics during NREM sleep are associated very early with a greater risk of the occurrence of cognitive impairment. Poor sleep consolidation, lower amplitude, and faster frequency of spindles may be early sleep biomarkers of worsening cognitive decline in older adults.

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“…A few studies have investigated sleep-microstructure and specific EEG elements as biomarkers to predict CBT-I outcomes. A pilot study showed that basal differences in SS density (SSD) were associated with some measures of response to CBT-I, both in the short and long term 66. As there is evidence suggesting a protective role of SS in maintaining sleep, lower SSD might represent an individual EEG marker for the development of ID 67–69…”

Section: Psg/eeg Featuresmentioning

confidence: 99%

<p>Impact Of Phenotypic Heterogeneity Of Insomnia On The Patients’ Response To Cognitive-Behavioral Therapy For Insomnia: Current Perspectives</p>

Galbiati¹,

Sforza²,

Fasiello³

et al. 2019

NSS

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Insomnia is one of the most common mental disorders and the most frequent sleep disorder encountered in clinical practice, with a prevalence of about 7% in the European population. Insomnia Disorder (ID) is defined as a disturbance of sleep initiation or maintenance, followed by a feeling of non-restorative sleep and several diurnal consequences ranging from occupational and social difficulties to cognitive impairment. Cognitive-Behavioral Therapy for Insomnia (CBT-I) is considered the first-choice therapy for this disorder because its effectiveness has been proven to be greater in the long term with fewer side effects in comparison to pharmacotherapy. Although its effectiveness has been well established, it has been reported that nearly 40% of patients do not achieve remission after treatment. This finding could be the consequence of heterogeneity of ID between patients. It has been proposed that this heterogeneity might be ascribable to indices that are not related to sleep quality and quantity, such as comorbidities, life events, and personality traits. However, several works focused on the role of sleep markers, in particular objective total sleep time, for the phenotypization of ID and treatment response. The aim of this work is to summarize the available scientific literature regarding the impact of ID subtype on CBT-I response.

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Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia

Cited by 13 publications

References 38 publications

Sleep Spindles: Mechanisms and Functions

Sleep Spindles: Mechanisms and Functions

Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population

<p>Impact Of Phenotypic Heterogeneity Of Insomnia On The Patients’ Response To Cognitive-Behavioral Therapy For Insomnia: Current Perspectives</p>

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