High resolution study of sleep spindles

Żygierewicz, Jarosław; Blinowska, Katarzyna J.; Durka, P.J.; Szelenberger, Waldemar; Niemcewicz, Szymon; Androsiuk, W.

doi:10.1016/s1388-2457(99)00175-3

Cited by 146 publications

(130 citation statements)

References 20 publications

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“…The present study used wholehead MEG to examine the cortical regions involved in maxima of spindle amplitude activity in healthy, normal sleeping individual subjects during a morning nap. Despite the fact that in the present study spindles were obtained from stage 2 sleep during morning nap, our results show that sleep spindles have multiple cortical sources that are seen in frontal, temporal and parietal brain regions that correspond to the previous EEG and ⁄ or MEG findings (Anderer et al, 2001;Broughton and Hasan, 1995;Gibbs and Gibbs, 1950;Ishii et al, 2003;Lu et al, 1992;Manshanden et al, 2002;Shih et al, 2000;Urakami, 2008;Zeitlhofer et al, 1997;Zygierewicz et al, 1999). In addition, a new approach for spindle localization used in this study, such as averaging spindle amplitude and localizing the source of the maximal activity, showed specific locations for the maximal spindle activity centered at precentral and postcentral gyri.…”

Section: Discussioncontrasting

confidence: 66%

“…Our findings also show that this maximal activity probably reflects a unifying network for both slow and fast spindles within these regions. The complex nature of the spindle activity, especially slowand fast-frequency spindle activity was revealed by topographic EEG studies (Anderer et al, 2001;Zeitlhofer et al, 1997;Zygierewicz et al, 1999;for a review, De Gennaro and Ferrara, 2003). The findings from these studies show that the neuronal network underlying these two types of spindles is topographically distinct.…”

Section: Discussionmentioning

confidence: 99%

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Cortical locations of maximal spindle activity: magnetoencephalography (MEG) study

Gumenyuk

Roth

Moran

et al. 2009

Journal of Sleep Research

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SUMMAR Y The aim of this study was to determine the main cortical regions related to maximal spindle activity of sleep stage 2 in healthy individual subjects during a brief morning nap using magnetoencephalography (MEG). Eight volunteers (mean age: 26.1 ± 8.7, six women) all right handed, free of any medical psychiatric or sleep disorders were studied. Whole-head 148-channel MEG and a conventional polysomnography montage (EEG; C3, C4, O1 and O2 scalp electrodes and EOG, EMG and ECG electrodes) were used for data collection. Sleep MEG ⁄ EEG spindles were visually identified during 15 min of stage 2 sleep for each participant. The distribution of brain activity corresponding to each spindle was calculated using a combination of independent component analysis and a current source density technique superimposed upon individual MRIs. The absolute maximum of spindle activation was localized to frontal, temporal and parietal lobes. However, the most common cortical regions for maximal source spindle activity were precentral and ⁄ or postcentral areas across all individuals. The present study suggests that maximal spindle activity localized to these two regions may represent a single event for two types of spindle frequency: slow (at 12 Hz) and fast (at 14 Hz) within global thalamocortical coherence.k e y w o r d s magnetoencephalography, MR-FOCUSS, sleep spindle, source localization

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Section: Discussioncontrasting

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Cortical locations of maximal spindle activity: magnetoencephalography (MEG) study

Gumenyuk

Roth

Moran

et al. 2009

Journal of Sleep Research

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“…Based on previous data-driven analysis of human sleep data, it appears that the frequency that best separates slow from fast spindles is Ϸ13 Hz (5,6). Consequently, slow and fast sleep spindles were identified on band pass-filtered data between 11-13 Hz and 13-15 Hz respectively, using an automatic detection algorithm inspired from Mölle et al (7).…”

Section: Resultsmentioning

confidence: 99%

Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep

Schabus

Dang‐Vu

Albouy

et al. 2007

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

442

392

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In humans, some evidence suggests that there are two different types of spindles during sleep, which differ by their scalp topography and possibly some aspects of their regulation. To test for the existence of two different spindle types, we characterized the activity associated with slow (11-13 Hz) and fast (13-15 Hz) spindles, identified as discrete events during non-rapid eye movement sleep, in non-sleepdeprived human volunteers, using simultaneous electroencephalography and functional MRI. An activation pattern common to both spindle types involved the thalami, paralimbic areas (anterior cingulate and insular cortices), and superior temporal gyri. No thalamic difference was detected in the direct comparison between slow and fast spindles although some thalamic areas were preferentially activated in relation to either spindle type. Beyond the common activation pattern, the increases in cortical activity differed significantly between the two spindle types. Slow spindles were associated with increased activity in the superior frontal gyrus. In contrast, fast spindles recruited a set of cortical regions involved in sensorimotor processing, as well as the mesial frontal cortex and hippocampus. The recruitment of partially segregated cortical networks for slow and fast spindles further supports the existence of two spindle types during human non-rapid eye movement sleep, with potentially different functional significance.H uman sleep is associated with a profound modification of consciousness and the emergence of distinct sleep oscillations. In the early stages of non-rapid eye movement (NREM) sleep, electroencephalographic recordings show characteristic spindle oscillations. In humans, spindles consist of waxing-and-waning 11-to 15-Hz oscillations, lasting 0.5-3 sec. At the cellular level, spindles are associated with substantial neuronal activity. Spindles arise from cyclic inhibition of thalamo-cortical (TC) neurons by reticular thalamic neurons. Postinihibitory rebound spike bursts in TC cells entrain cortical populations in spindle oscillations (1). These neuronal mechanisms, which involve large TC populations, are thought to shape the processing of information during light NREM sleep and participate in the alteration of consciousness that characterizes this stage of sleep.Little is known on the cerebral correlates of human spindles. Early positron emission tomography studies reported a negative relationship between thalamic cerebral blood flow and the power spectrum in the spindle frequency band (2). However, the low temporal resolution of positron emission tomography did not allow for a fine-grained characterization of the cerebral correlates of human spindles. In addition, two kinds of spindles are described in humans. Slow spindles (Ͻ13 Hz) predominate over frontal, whereas fast spindles (Ͼ13 Hz) prevail over centro-parietal areas. The difference in spindle scalp topography is also reflected by profound functional differences. These two spindling activities differ by their circadian and homeostatic regul...

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“…Achieving accurate manual scoring on long-term recordings requires a high level of vigilance, resulting in a highly demanding task that augments the risk of decreased accuracy in the diagnosis, especially for sleep-related studies, for which precise information (such as spindle's amplitude, frequency, and length) is often required. Beside the laborious aspect of the task, visual analysis involves some subjectivity (inter-human agreement is estimated to be around 80-90% (Campbell et al, 1980), and degree of consent is 70 ± 8% (Zygierewicz et al, 1999)). A reliable spindle detection is therefore attractive, as it would enhance the speed, accuracy, and inter-rater agreement of spindle scoring.…”

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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High resolution study of sleep spindles

Cited by 146 publications

References 20 publications

Cortical locations of maximal spindle activity: magnetoencephalography (MEG) study

Cortical locations of maximal spindle activity: magnetoencephalography (MEG) study

Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep

Sleep spindle detection through amplitude–frequency normal modelling

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