Sleep spindle evolution from infancy to adolescence

Scholle, Sabine; Zwacka, G; Scholle, Hans‐Christoph

doi:10.1016/j.clinph.2007.03.007

Cited by 89 publications

(92 citation statements)

References 35 publications

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“…As shown in Figures 5A,B, sudden persistent increases in both N2% sleep and N2 spindle density occur between ages 3 and 4 (168). There is also an increase in spindle duration slightly later, from 4 to 5 years (Figure 5C).…”

Section: The Stress Acceleration Hypothesis Of Nightmaresmentioning

confidence: 85%

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The Stress Acceleration Hypothesis of Nightmares

Nielsen

2017

Front. Neurol.

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Adverse childhood experiences can deleteriously affect future physical and mental health, increasing risk for many illnesses, including psychiatric problems, sleep disorders, and, according to the present hypothesis, idiopathic nightmares. Much like post-traumatic nightmares, which are triggered by trauma and lead to recurrent emotional dreaming about the trauma, idiopathic nightmares are hypothesized to originate in early adverse experiences that lead in later life to the expression of early memories and emotions in dream content. Accordingly, the objectives of this paper are to (1) review existing literature on sleep, dreaming and nightmares in relation to early adverse experiences, drawing upon both empirical studies of dreaming and nightmares and books and chapters by recognized nightmare experts and (2) propose a new approach to explaining nightmares that is based upon the Stress Acceleration Hypothesis of mental illness. The latter stipulates that susceptibility to mental illness is increased by adversity occurring during a developmentally sensitive window for emotional maturation—the infantile amnesia period—that ends around age 3½. Early adversity accelerates the neural and behavioral maturation of emotional systems governing the expression, learning, and extinction of fear memories and may afford short-term adaptive value. But it also engenders long-term dysfunctional consequences including an increased risk for nightmares. Two mechanisms are proposed: (1) disruption of infantile amnesia allows normally forgotten early childhood memories to influence later emotions, cognitions and behavior, including the common expression of threats in nightmares; (2) alterations of normal emotion regulation processes of both waking and sleep lead to increased fear sensitivity and less effective fear extinction. These changes influence an affect network previously hypothesized to regulate fear extinction during REM sleep, disruption of which leads to nightmares. This network consists of a fear circuit that includes amygdala, hippocampus, and medial prefrontal cortex and whose substantial overlap with the stress acceleration findings allows the latter to be incorporated into a wider, more developmentally coherent framework.

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Section: The Stress Acceleration Hypothesis Of Nightmaresmentioning

confidence: 85%

“…There is a marked increase in N2% and simultaneous decrease in N3% at the infantile amnesia boundary [from Ref. (168)]. (B) Spindle density shows a clear and persistent increase across the infantile amnesia boundary, whereas (C) spindle duration increases from ages 4 to 5 [from Ref.…”

Section: The Stress Acceleration Hypothesis Of Nightmaresmentioning

confidence: 99%

The Stress Acceleration Hypothesis of Nightmares

Nielsen

2017

Front. Neurol.

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“…Furthermore, sleep spindles manifesting as high amplitude bursts of brain activity within the broad 10-16 Hz range are evident as early as 2-4 months. At this time Kcomplexes, waveforms exhibiting a strong negative then positive component lasting longer than 0.5 s, become numerous and easily identifiable (Jenni et al, 2004;Scholle, Zwacka, & Scholle, 2007;Tarullo et al, 2011). Sleep spindles are notable for their association with better retention in studies of memory formation in adults (De Gennaro & Ferrara, 2003;Fogel & Smith, 2011;Loomis, Harvey, & Hobart, 1937).…”

Section: Development Of Sleep Physiology In Early Infancymentioning

confidence: 93%

Sleep confers a benefit for retention of statistical language learning in 6.5 month old infants

et al. 2017

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“…However, assessing automated spindles detection, specifically in childhood, seems important as spindles show modification in amplitude, frequency, length, density, interspindle interval and topological distribution from infancy to adolescence (Nagata et al, 1996;Shinomiya et al, 1999;Scholle et al, 2007); therefore, automated algorithms should be able to adapt to those variations. Published spindle detection algorithms in children and in infants are based on empirical-mode decomposition and Hilbert-Huang transform (Causa et al, 2010), expert procedure and fuzzy logic (Held et al, 2004(Held et al, , 2006, peak identification , merge neural gas model (Estévez et al, 2007), and neuro fuzzy approach .…”

Section: Introductionmentioning

confidence: 99%

Sleep spindle detection through amplitude–frequency normal modelling

Nonclercq

Urbain

Verheulpen

et al. 2013

Journal of Neuroscience Methods

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h i g h l i g h t sAn automated model-based spindle detection algorithm is proposed. It models the amplitude-frequency spindle distribution with a bivariate normal distribution. It automatically adapts to each individual subject and derivation. It was tested in seven healthy children and six adult patients suffering from different pathologies, and performs similarly or better than sleep experts. Normal modelling enhances spindle detection quality compared to fixed amplitude and frequency thresholds. t r a c tManual scoring of sleep spindles can be very time-consuming, and achieving accurate manual scoring on a long-term recording requires high and sustained levels of vigilance, which makes it a highly demanding task with the associated risk of decreased diagnosis accuracy. Although automatic spindle detection would be attractive, most available algorithms are sensitive to variations in spindle amplitude and frequency that occur between both subjects and derivations, reducing their effectiveness.We propose here an algorithm that models the amplitude-frequency spindle distribution with a bivariate normal distribution (one normal model per derivation). Subsequently, spindles are detected when their amplitude-frequency characteristics are included within a given tolerance interval of the corresponding model. As a consequence, spindle detection is not directly based on amplitude and frequency thresholds, but instead on a spindle distribution model that is automatically adapted to each individual subject and derivation.The algorithm was first assessed against the scoring of one sleep scoring expert on EEG samples from seven healthy children. Afterward, a second study compared performance of two additional experts versus the algorithm on a dataset of six EEG samples from adult patients suffering from different pathologies, to submit the method to more challenging and clinically realistic conditions. Smaller and shorter spindles were more difficult to evaluate, as false positives and false negatives showed lower amplitude and smaller length than true positives. In both studies, normal modelling enhanced performance compared to fixed amplitude and frequency thresholds. Normal modelling is therefore attractive, as it enhances spindle detection quality.

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Sleep spindle evolution from infancy to adolescence

Cited by 89 publications

References 35 publications

The Stress Acceleration Hypothesis of Nightmares

The Stress Acceleration Hypothesis of Nightmares

Sleep confers a benefit for retention of statistical language learning in 6.5 month old infants

Sleep spindle detection through amplitude–frequency normal modelling

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