Reduced Sleep Spindle Activity in Schizophrenia Patients

Ferrarelli, Fabio; Huber, Reto; Peterson, Michael J.; Massimini, Marcello; Murphy, Michael; Riedner, Brady A.; Watson, Adam; Bria, Pietro; Tononi, Giulio

doi:10.1176/ajp.2007.164.3.483

Cited by 424 publications

(423 citation statements)

References 47 publications

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“…There was no deficit in delta power. Previous investigations of SW activity in schizophrenia have been inconsistent reporting no differences during NREM sleep in medicated patients 61,62 , reduced N3 SW count and delta power in antipsychotic-naïve and unmedicated patients 63,64 and reduced delta power in N3 but not N2 in medicated patients 65 . These inconsistencies may reflect differences in definitions of delta power, sleep stages considered and medication status of the participants.…”

Section: Discussionmentioning

confidence: 93%

Coordination of Slow Waves With Sleep Spindles Predicts Sleep-Dependent Memory Consolidation in Schizophrenia

Demanuele

Bartsch

Baran

et al. 2016

Sleep

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Demanuele et al., 2016 1 This study is not a clinical trial. Demanuele et al., 2016 3 Coordination of Slow Waves with Sleep Spindles Predicts Sleep-Dependent Memory Consolidation in Schizophrenia AbstractStudy Objectives: Schizophrenia patients have correlated deficits in sleep spindle density and sleep-dependent memory consolidation. In addition to spindle density, memory consolidation is thought to rely on the precise temporal coordination of spindles with slow waves (SWs). We investigated whether this coordination is intact in schizophrenia and its relation to motor procedural memory consolidation.Methods: Twenty-one chronic medicated schizophrenia patients and 17 demographicallymatched healthy controls underwent two nights of polysomnography with training on the finger tapping motor sequence task (MST) on the second night and testing the following morning. We detected SWs (0.5-4Hz) and spindles during non-rapid eye-movement (NREM) sleep. We measured SW-spindle phase-amplitude coupling and its relation with overnight improvement of MST performance.Results: Patients did not differ from controls in the timing of SW-spindle coupling. In both groups spindles peaked during the SW upstate. For patients only, the later in the SW upstate that spindles peaked and the more reliable this phase relationship, the greater the overnight MST improvement.Regression models that included both spindle density and SW-spindle coordination predicted overnight improvement significantly better than either parameter alone suggesting that both contribute to memory consolidation.Conclusions: Schizophrenia patients show intact spindle-SW temporal coordination and these timing relationships, together with spindle density, predict sleep-dependent memory consolidation.These relations were seen only in patients suggesting that their memory is more dependent on optimal spindle-SW timing, possibly due to reduced spindle density. Interventions to improve memory may need to both increase spindle density and optimize the coordination of NREM oscillations.

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Section: Discussionmentioning

confidence: 93%

Coordination of Slow Waves With Sleep Spindles Predicts Sleep-Dependent Memory Consolidation in Schizophrenia

Demanuele

Bartsch

Baran

et al. 2016

Sleep

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“…An automatic sleep spindle detection algorithm, similar to the method found in ref. 38, was used to count the number of discrete spindle events on each electrode. For each identified NREM sleep epoch, the down-sampled and normalized electrode traces were band-pass filtered between 11 and 16 Hz using a fourth order Butterworth filter.…”

Section: Methodsmentioning

confidence: 99%

Sleep spindles are locally modulated by training on a brain–computer interface

Johnson

Blakely

Hermes

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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The learning of a motor task is known to be improved by sleep, and sleep spindles are thought to facilitate this learning by enabling synaptic plasticity. In this study subjects implanted with electrocorticography (ECoG) arrays for long-term epilepsy monitoring were trained to control a cursor on a computer screen by modulating either the high-gamma or mu/beta power at a single electrode located over the motor or premotor area. In all trained subjects, spindle density in posttraining sleep was increased with respect to pretraining sleep in a remarkably spatially specific manner. The pattern of increased spindle activity reflects the functionally specific regions that were involved in learning of a highly novel and salient task during wakefulness, supporting the idea that sleep spindles are involved in learning to use a motorbased brain-computer interface device.

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“…It is clear that spindles are involved in learning and memory (Clemens et al 2006;Cox et al 2014;Yotsumoto et al 2009) and their characteristics are correlated with intelligence and cognitive ability (Geiger et al 2011;Fogel et al 2007;Schabus et al 2006). They are greatly reduced in developmental abnormalities in children (Ellingson and Peters 1980;Selvitellia et al 2009;Godbout et al 2000;Limoges et al 2005), in adult schizophrenia (Ferrarelli et al 2007;Ferrarelli et al 2010;Manoach et al 2010;Manoach et al 2014) and in patients with Parkinson's disease with dementia (Latreille et al 2015). Spindle density, duration and amplitude decrease with age but the rate of decline is not the same in all subjects (Nicolas et al 2001;Crowley et al 2002;Guazzelli et al 1986;Wei et al 1999;Principe and Smith 1982).…”

Section: Assessment Of Sleep Spindles and K Complexesmentioning

confidence: 99%

The case for using digital EEG analysis in clinical sleep medicine

Younes

2017

Sleep Science Practice

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Evaluation of sleep in clinical polysomnograms continues to rely almost exclusively on visual scoring that implements rules proposed by Rechtschaffen and Kales nearly 50 years ago. Apart from its cost and timeconsuming nature, visual scoring has limitations including: A) Sleep depth, which is a continuous variable, is treated as if it changes in a stepwise fashion from light (stage 1), to intermediate (stage 2) to deep (stage 3). B) Even with this limited scale, there is considerable inter-scorer variability, particularly in scoring stages 1 and 3 of non-REM sleep, thereby adding uncertainty to %time spent in these stages as a reliable metric for evaluating sleep depth. C) Limitation in scoring some of EEG features, including 1) arousal intensity, 2) extent of Alpha intrusion and 3) frequency, and characteristics of sleep spindles and K complexes. Digital analysis can solve these problems but producing a reliable system has been a challenge. In this review I begin with recent advances in digital scoring of sleep according to the Rechtschaffen and Kales rules and conclude that this technology has progressed enough to make it possible to obtain reliable, reproducible scoring, comparable in accuracy to scoring by highly experienced technologists, with minimal editing. This is followed by description of several new metrics that can be obtained if digital scoring systems were to be used routinely in clinical studies. The scientific evidence supporting the potential of these metrics to positively impact sleep medicine practice and the wide range of such metrics in patients studied in the sleep laboratory are highlighted.

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Reduced Sleep Spindle Activity in Schizophrenia Patients

Cited by 424 publications

References 47 publications

Coordination of Slow Waves With Sleep Spindles Predicts Sleep-Dependent Memory Consolidation in Schizophrenia

Coordination of Slow Waves With Sleep Spindles Predicts Sleep-Dependent Memory Consolidation in Schizophrenia

Sleep spindles are locally modulated by training on a brain–computer interface

The case for using digital EEG analysis in clinical sleep medicine

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