Abstract
Introduction
Spindles are currently defined clinically based on observed patterns in the EEG waveform trace, with automated methods seeking to replicate visual scoring by experts. Recent ...work suggests that sleep spindles may be more readily observed as time-frequency peaks in the EEG spectrogram. This study compares spectral peaks in the multitaper spectrogram to expert and automatic detection scoring, characterizes the variability of spindles across a night, and investigates topographical and temporal clustering of spindles within individual EEG records.
Methods
We compared spectral peaks, expert scoring, and automatic detection in two datasets (DREAMS, and a high-density control study). Peaks were identified using multitaper spectral estimation and the peak prominence of the normalized power spectrum for each channel. Spatiotemporal variability analysis was performed using cluster and pattern recognition algorithms including penalized sorting of channel activation order, 2D-cross correlation, PCA and UMAP cluster analysis, and the seqNMF method.
Results
Spectral peaks were shown to be highly robust to and easily differentiated from broadband noise, occuring at rates (10-16 per min) far exceeding spindle rates reported in literature (~2.5 per min). Expert scoring and automated scoring failed to capture clear spectral peaks in the time-frequency domain, indicating an underreporting of the phenomenology. No apparent clustering or patterns of sleep spindle-like activity was observed using the proposed methods, suggesting high variability of spatiotemporal evolution of spindles.
Conclusion
These results suggest that the difficulty of time-domain visual scoring of spindles causes an artificially low estimate of the underlying phenomenology, which is mirrored in the assumptions implicit in the thresholds of automated scorers. This work shows that spindles are highly variable in their spatiotemporal evolution, suggesting that there is no optimal single electrode for analysis and casting doubt on the presence of a single cortical generation mechanism. We must therefore revisit the concept of the spindle using the time-frequency domain to more robustly characterize underlying phenomenology.
Support
National Institute Of Neurological Disorders And Stroke Grant R01 NS-096177
Abstract
Introduction
The impact of EEG referencing on sleep oscillations, such as spindles and slow oscillations, is largely overlooked across studies. While it is recognized that a topographic head ...plot of EEG activity does not reflect the true location of the underlying cortical activity, spatial distributions, as well as spectral properties and morphology of EEG oscillations can change dramatically as a function of referencing scheme. It is therefore vital to understand the impact of referencing when drawing inferences about the nature of EEG sleep oscillations. In this study, we use MRI structural data to construct subject-specific forward models of EEG signals. Using these models, we can simulate cortical activity and observe its true representation on the scalp. In particular, we simulate spindles and slow wave oscillations and examine how referencing affects topography, spectral power, and phase of oscillations.
Methods
High-density EEG (Brain Vision, 64-channel) polysomnography was performed on 9 healthy young subjects. 3T structural MRI scans were acquired and forward models were built in MNE-Python using 3-shell Boundary Element Models (BEM) based on individual anatomical details processed with Freesurfer. Simulations of various sleep spindle and slow oscillation dynamics were projected to the sensor space. Different referencing schemes (common average, Laplacian, linked-mastoid) were then applied to the experimental and simulated data and analyzed for effects on time-frequency characteristics of sleep oscillations.
Results
Analyses of experimental data showed distinct reference-based differences in topographical distribution of spectral power and phase of oscillations. Simulated data revealed many scenarios in which the spatial distribution of activity the EEG sensor space poorly represented the true location of the underlying source activity. Moreover, there were alterations to the spatial spread and envelope form of sleep spindle events under different referencing schemes despite from identical source activities.
Conclusion
This study shows that spindle and slow oscillation activity is highly variable across referencing schemes and that EEG topographical plots on the scalp may poorly represent cortical activity locations. It is thus vital to consider the choice of referencing when quantifying characteristics of sleep EEG oscillations.
Support
This work was supported by R01 NS-096177.
1 Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, Massachusetts;
2 Department of Brain and Cognitive Sciences and
3 McGovern Institute for Brain Research, ...Massachusetts Institute of Technology, Cambridge, Massachusetts;
4 Center for Neural Science, New York University, New York City, New York;
5 Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology, Cambridge, Massachusetts;
6 Department of Mathematics and Statistics and
7 Program in Neuroscience, Boston University, Boston, Massachusetts
Submitted 24 November 2008;
accepted in final form 17 August 2009
ABSTRACT
Continuous observations, such as reaction and run times, and binary observations, such as correct/incorrect responses, are recorded routinely in behavioral learning experiments. Although both types of performance measures are often recorded simultaneously, the two have not been used in combination to evaluate learning. We present a state-space model of learning in which the observation process has simultaneously recorded continuous and binary measures of performance. We use these performance measures simultaneously to estimate the model parameters and the unobserved cognitive state process by maximum likelihood using an approximate expectation maximization (EM) algorithm. We introduce the concept of a reaction-time curve and reformulate our previous definitions of the learning curve, the ideal observer curve, the learning trial and between-trial comparisons of performance in terms of the new model. We illustrate the properties of the new model in an analysis of a simulated learning experiment. In the simulated data analysis, simultaneous use of the two measures of performance provided more credible and accurate estimates of the learning than either measure analyzed separately. We also analyze two actual learning experiments in which the performance of rats and of monkeys was tracked across trials by simultaneously recorded reaction and run times and the correct and incorrect responses. In the analysis of the actual experiments, our algorithm gave a straightforward, efficient way to characterize learning by combining continuous and binary measures of performance. This analysis paradigm has implications for characterizing learning and for the more general problem of combining different data types to characterize the properties of a neural system.
Address for reprint requests and other correspondence: E. N. Brown, Neuroscience Statistics Research Laboratory, Dept. of Anesthesia and Critical Care, Massachusetts General Hospital, 55 Fruit St, Clinics 3, Boston, MA 02114-2696 (E-mail: brown{at}neurostat.mgh.harvard.edu ).
During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately ...describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible-elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications.
We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night ...electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults.
Abstract
Introduction
Sleep is traditionally characterized through patterns observed in the time-domain electroencephalogram (EEG). These include persistent oscillations, such as slow wave activity, ...or transient oscillatory activity, such as spindles and K-complexes. Time-domain identification of oscillations is difficult, with waveforms frequently being obscured by other oscillations and noise, limiting analyses to easily discernible waveforms. These difficulties lead to large inter-scorer variability—especially for transient oscillations like sleep spindles, the identification of which varies greatly depending on scorer or automated method used. This variability is a major challenge in analysis of sleep differences in clinical populations, such as patients with schizophrenia (SZ), in whom cognitive deficits and symptoms correlate with measurements of spindle density based on traditional techniques. Here we demonstrate a new, objective analysis method that is agnostic to arbitrary criteria and robust to time-domain obfuscations, and that may offer improved characterization of the dynamics of sleep, its natural variation, and biomarkers of disease.
Methods
We demonstrate a novel approach to the analysis of sleep EEG oscillations based on the observation that transient oscillations will appear as distinct peaks in the time-frequency spectrogram. By characterizing distributions of peak properties, rather than seeking to identify specific, pre-defined oscillations, we can perform definition-agnostic analyses and comparisons of transient oscillatory activity across subjects or groups. We applied this approach to full-night EEG recordings from 21 SZ patients and 17 healthy controls and compared the peak feature distributions between the groups.
Results
We found marked differences in the frequency distributions of time-frequency peaks between SZ and control participants, suggesting less frequent and less frequency-specific spindle-like peaks in SZ patients. The SZ distribution is also elevated over the control from 8-12Hz. The distribution of SZ peaks also suggests they are generally of smaller height than those of the control group.
Conclusion
These results demonstrate that transient oscillations can be robustly identified and objectively analyzed in this definition-agnostic approach. Furthermore, this approach could more comprehensively characterize differences in clinical populations and reduce method-based variability in spindle detection and other analyses.
Support (If Any)
R01NS096177; R01MH092638; K24MH099421.
Abstract
Introduction:
Clinically, sleep apnea is characterized using metrics such as the Apnea-Hypopnea Index (AHI), a single rate averaged over the total sleep time. While clinically informative, ...it does not reflect either context or temporal distribution of the events. Thus, it is possible for patients with identical AHIs to present vastly different apnea phenotypes. Additionally, while descriptors relating factors such as sleep stage and position to apnea events can provide useful clinical information, current methods do not quantify the degree to which these factors contribute to the events. It is therefore crucial to develop phenotyping methods that can disambiguate differences in apnea event context, as well as in the relative contributions of different behavioral and physiological factors.
Methods:
We develop a point process approach for quantifying the contributions of different polysomnographic (PSG) observations to the instantaneous respiratory event probability. A point process is any system that can be represented as a series of stochastic momentary events, which is governed by time-varying instantaneous rate or probability. We use a generalized linear model (GLM) framework to estimate the degree to which sleep stage, body position, and previous event timing predicts the instantaneous probability of an event occurring.
Results:
We applied our point process framework to technician-scored PSGs from a cohort of subjects with severe disease (AHI>30). Models including only position and sleep stage were poor predictors of sleep apnea (Kolmogorov-Smirnov test on time rescaled events). However, by adding the timing of past apnea events to the model, we observed a marked improvement to the model goodness-of-fit. Moreover, the degree to which past events influenced future apnea probability showed heterogeneity and clustering across subjects.
Conclusion:
These results indicate that past apnea history may be a major influencing factor on apnea probability. This suggests that a single apnea event, while mediated by other factors such as position and stage, may set off a cascade of subsequent respiratory events. Therefore, the structure of the history dependence may be a novel feature for apnea phenotyping and target for evaluating the effects of clinical intervention.
Support (If Any):
NINDS R01 NS-096177 (M.J.P.).
The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering ...questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Introduction:
Sleep spindles are typically characterized as intermittent, oscillatory activity observed between 11-16Hz in electroencephalography (EEG) data. Accurate identification of ...spindles is valuable, not only to the identification of the stages of healthy sleep, but also to the characterization of disorders, such as schizophrenia, in which spindle activity and morphology is altered. Developing accurate spindle detectors has been challenging due to absence of an objective “ground truth” and reliance on the highly-variable results of visual scoring. Furthermore, automated spindle detectors are typically based on features of visually identified spindles, rather than principled analysis of the properties of the time-varying oscillations and their relationship to known correlates of spindle function. It is thus crucial to develop principled methods for establishing an objective, data-driven spindle definition, which would greatly facilitate our understanding of sleep dynamics as well as provide biomarkers of disease.
Methods:
We propose a novel approach that uses the topography of the EEG spectrogram to identify and characterize spindles in the time-frequency domain. For each significant time-frequency peak found in the spectrogram, we compute a set of time-frequency features, which provides a high-dimensional description of that peak. By placing constraints on these features, we can select subsets of related spectral peaks. We explore several data-driven approaches to defining these constraints so as to identify and characterize the peaks corresponding to spindles.
Results:
In this preliminary work, we analyzed EEG data from the DREAMS database with technician-scored spindles. We outlined a statistical framework for quantifying the distribution of peak features that are being scored by a given technician or automated method. These distributions can be used to evaluate inter- and intra-scorer consistency, as well as adherence to a given standard. We also developed an unsupervised learning framework for data-driven characterization of spindles, identifying clusters of related peaks, and illustrated how additional features robust to noise, such as coherence, can greatly facilitate spindle cluster separation.
Conclusion:
This work provides strong proof-of-concept for a rigorous quantitative analysis and characterization of spindle properties, and paves the way for further experimental work.
Support (If Any):
NINDS R01 NS-096177 (M.J.P.).
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