Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis.

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.

Sleep deficiency and motor vehicle crash risk in the general population: a prospective cohort study.

BACKGROUND: Insufficient sleep duration and obstructive sleep apnea, two common causes of sleep deficiency in adults, can result in excessive sleepiness, a well-recognized cause of motor vehicle crashes, although their contribution to crash risk in the general population remains uncertain. The objective of this study was to evaluate the relation of sleep apnea, sleep duration, and excessive sleepiness to crash risk in a community-dwelling population. METHODS: This was a prospective observational cohort study nested within the Sleep Heart Health Study, a community-based study of the health consequences of sleep apnea. The participants were 1745 men and 1456 women aged 40-89 years. Sleep apnea was measured by home polysomnography and questionnaires were used to assess usual sleep duration and daytime sleepiness. A follow-up questionnaire 2 years after baseline ascertained driving habits and motor vehicle crash history. Logistic regression analysis was used to examine the relation of sleep apnea and sleep duration at baseline to the occurrence of motor vehicle crashes during the year preceding the follow-up visit, adjusting for relevant covariates. The population-attributable fraction of motor vehicle crashes was estimated from the sample proportion of motor vehicle crashes and the adjusted odds ratios for motor vehicle crash within each exposure category. RESULTS: Among 3201 evaluable participants, 222 (6.9%) reported at least one motor vehicle crash during the prior year. A higher apnea-hypopnea index (p < 0.01), fewer hours of sleep (p = 0.04), and self-reported excessive sleepiness (p < 0.01) were each significantly associated with crash risk. Severe sleep apnea was associated with a 123% increased crash risk, compared to no sleep apnea. Sleeping 6 hours per night was associated with a 33% increased crash risk, compared to sleeping 7 or 8 hours per night. These associations were present even in those who did not report excessive sleepiness. The population-attributable fraction of motor vehicle crashes was 10% due to sleep apnea and 9% due to sleep duration less than 7 hours. CONCLUSIONS: Sleep deficiency due to either sleep apnea or insufficient sleep duration is strongly associated with motor vehicle crashes in the general population, independent of self-reported excessive sleepiness.

ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit.

We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with valid R-peak detections and 60 epochs containing artifacts leading to missed or false positive R-peak detections. Next, we calculated the absolute value of the difference between two adjacent RRIs (adRRI), and obtained the empirical probability distributions of adRRI values for valid R-peaks and artifacts. From these, we calculated an optimal threshold for separating adRRI values arising from artifact versus non-artefactual data. We compared the performance of our method with the methods of Berntson and Clifford on the same data. We identified 257,458 R-peak detections, of which 235,644 (91.5%) were true detections and 21,814 (8.5%) arose from artifacts. Our method showed superior performance for detecting artifacts with sensitivity 100%, specificity 99%, precision 99%, positive likelihood ratio of 100 and negative likelihood ratio <0.001 compared to Berntson's and Clifford's method with a sensitivity, specificity, precision and positive and negative likelihood ratio of 99%, 78%, 82%, 4.5, 0.013 for Berntson's method and 55%, 98%, 96%, 27.5, 0.460 for Clifford's method, respectively. A novel algorithm using a patient-independent threshold derived from the distribution of adRRI values in ICU ECG data identifies artifacts accurately, and outperforms two other methods in common use. Furthermore, the threshold was calculated based on real data from critically ill patients and the algorithm is easy to implement.

Estimating Total Cerebral Microinfarct Burden From Diffusion-Weighted Imaging.

BACKGROUND AND PURPOSE: Cerebral microinfarcts (CMI) are important contributors to vascular cognitive impairment. Magnetic resonance imaging diffusion-weighted imaging (DWI) hyperintensities have been suggested to represent acute CMI. We aim to describe a mathematical method for estimating total number of CMI based on the presence of incidental DWI lesions. METHODS: We reviewed magnetic resonance imaging scans of subjects with cognitive decline, cognitively normal subjects and previously reported subjects with past intracerebral hemorrhage (ICH). Based on temporal and spatial characteristics of DWI lesions, we estimated the annual rate of CMI needed to explain the observed rate of DWI lesion detection in each group. To confirm our estimates, we performed extensive sampling for CMI in the brain of a deceased subject with past lobar ICH who found to have a DWI lesion during life. RESULTS: Clinically silent DWI lesions were present in 13 of 343 (3.8%) cognitively impaired and 10 of 199 (5%) cognitively intact normal non-ICH patients, both lower than the incidence in the past ICH patients (23 of 178; 12.9%; P<0.0006). The predicted annual incidence of CMI ranges from 16 to 1566 for non-ICH and 50 to 5041 for ICH individuals. Histological sampling revealed a total of 60 lesions in 32 sections. Based on previously reported methods, this density of CMI yields an estimated total brain burden maximum likelihood estimate of 9321 CMIs (95% confidence interval, 7255-11 990). CONCLUSIONS: Detecting even a single DWI lesion suggests an annual incidence of hundreds of new CMI. The cumulative effects of these lesions may directly contribute to small-vessel-related vascular cognitive impairment.

Tracking the sleep onset process: an empirical model of behavioral and physiological dynamics.

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.

Weighing the value of memory loss in the surgical evaluation of left temporal lobe epilepsy: a decision analysis.

OBJECTIVES: Anterior temporal lobectomy is curative for many patients with disabling medically refractory temporal lobe epilepsy, but carries an inherent risk of disabling verbal memory loss. Although accurate prediction of iatrogenic memory loss is becoming increasingly possible, it remains unclear how much weight such predictions should have in surgical decision making. Here we aim to create a framework that facilitates a systematic and integrated assessment of the relative risks and benefits of surgery versus medical management for patients with left temporal lobe epilepsy. METHODS: We constructed a Markov decision model to evaluate the probabilistic outcomes and associated health utilities associated with choosing to undergo a left anterior temporal lobectomy versus continuing with medical management for patients with medically refractory left temporal lobe epilepsy. Three base-cases were considered, representing a spectrum of surgical candidates encountered in practice, with varying degrees of epilepsy-related disability and potential for decreased quality of life in response to post-surgical verbal memory deficits. RESULTS: For patients with moderately severe seizures and moderate risk of verbal memory loss, medical management was the preferred decision, with increased quality-adjusted life expectancy. However, the preferred choice was sensitive to clinically meaningful changes in several parameters, including quality of life impact of verbal memory decline, quality of life with seizures, mortality rate with medical management, probability of remission following surgery, and probability of remission with medical management. SIGNIFICANCE: Our decision model suggests that for patients with left temporal lobe epilepsy, quantitative assessment of risk and benefit should guide recommendation of therapy. In particular, risk for and potential impact of verbal memory decline should be carefully weighed against the degree of disability conferred by continued seizures on a patient-by-patient basis.

Interactions of time of day and sleep with between-session habituation and extinction memory in young adult males.

Within-session habituation and extinction learning co-occur as do subsequent consolidation of habituation (i.e., between-session habituation) and extinction memory. We sought to determine whether, as we predicted: (1) between-session habituation is greater across a night of sleep versus a day awake; (2) time-of-day accounts for differences; (3) between-session habituation predicts consolidation of extinction memory; (4) sleep predicts between-session habituation and/or extinction memory. Participants (N = 28) completed 4-5 sessions alternating between mornings and evenings over 3 successive days (2 nights) with session 1 in either the morning (N = 13) or evening (N = 15). Twelve participants underwent laboratory polysomnography. During 4 sessions, participants completed a loud-tone habituation protocol, while skin conductance response (SCR), blink startle electromyography (EMG), heart-rate acceleration and heart-rate deceleration (HRD) were recorded. For sessions 1 and 2, between-session habituation of EMG, SCR and HRD was greater across sleep. SCR and HRD were generally lower in the morning. Between-session habituation of SCR for sessions 1 and 2 was positively related to intervening (first night) slow wave sleep. In the evening before night 2, participants also underwent fear conditioning and extinction learning phases of a second protocol. Extinction recall was tested the following morning. Extinction recall was predicted only by between-session habituation of SCR across the same night (second night) and by intervening REM. We conclude that: (1) sleep augments between-session habituation, as does morning testing; (2) extinction recall is predicted by concurrent between-session habituation; and (3) both phenomena may be influenced by sleep.

Sleep misperception in persons with epilepsy.

Being able to confidently ascertain the amount of sleep is critical to the clinical management of epilepsy. Sleep misperception is the phenomenon in which an individual underestimates the amount of time spent asleep. Little is known about sleep misperception in patients with epilepsy. We conducted retrospective chart reviews on individuals who self-identified as having epilepsy in a questionnaire database of patients undergoing polysomnography (PSG) at the Massachusetts General Hospital Sleep Laboratory. Our metric for sleep misperception was the difference between subjective and objective sleep latency (S-O SL) and subjective and objective total sleep time (S-O TST) with subjective values based on questionnaire and objective values based on PSG. We confirmed 64 patients with epilepsy. We then selected age- and sex-matched diagnostic PSG data for comparison from 50 patients with insomnia symptoms but no obstructive sleep apnea (OSA) and another 50 patients with OSA but no insomnia symptoms. In our cohort with epilepsy, the median SL overestimation was 20 min (p<0.05), and the median TST underestimation was 45 min (p<0.05). Sleep misperception was similar regardless of potential confounding factors such as categorical epilepsy refractoriness, cognitive impairment, or psychiatric comorbidity. Our findings suggest that sleep misperception occurs similarly in patients with epilepsy as in patients without epilepsy with insomnia. Our findings further support the potential clinical utility of objective PSG testing in patients with epilepsy, as this may not only identify occult OSA but also disclose sleep misperception.

Emergence of stable functional networks in long-term human electroencephalography.

Functional connectivity networks have become a central focus in neuroscience because they reveal key higher-dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from 1 s to multiple hours across different states of consciousness were compared. We show that, although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ∼100 s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network "core." Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state and frequency specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template on which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency-dependent manner.

The subjective-objective mismatch in sleep perception among those with insomnia and sleep apnea.

The diagnosis and management of insomnia relies primarily on clinical history. However, patient self-report of sleep-wake times may not agree with objective measurements. We hypothesized that those with shallow or fragmented sleep would under-report sleep quantity, and that this might account for some of the mismatch. We compared objective and subjective sleep-wake times for 277 patients who underwent diagnostic polysomnography. The group included those with insomnia symptoms (n = 92), obstructive sleep apnea (n = 66) or both (n = 119). Mismatch of wake duration was context dependent: all three groups overestimated sleep latency but underestimated wakefulness after sleep onset. The insomnia group underestimated total sleep time by a median of 81 min. However, contrary to our hypothesis, measures of fragmentation (N1, arousal index, sleep efficiency, etc.) did not correlate with the subjective sleep duration estimates. To unmask a potential relationship between sleep architecture and subjective duration, we tested three hypotheses: N1 is perceived as wake; sleep bouts under 10 min are perceived as wake; or N1 and N2 are perceived in a weighted fashion. None of these hypotheses exposed a match between subjective and objective sleep duration. We show only modest performance of a Naïve Bayes Classifier algorithm for predicting mismatch using clinical and polysomnographic variables. Subjective-objective mismatch is common in patients reporting insomnia symptoms. We conclude that mismatch was not attributable to commonly measured polysomnographic measures of fragmentation. Further insight is needed into the complex relationships between subjective perception of sleep and conventional, objective measurements.

Power laws and fragility in flow networks.

What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.

Calculating the risk benefit equation for aggressive treatment of non-convulsive status epilepticus.

OBJECTIVE: To address the question: does non-convulsive status epilepticus warrant the same aggressive treatment as convulsive status epilepticus? METHODS: We used a decision model to evaluate the risks and benefits of treating non-convulsive status epilepticus with intravenous anesthetics and ICU-level aggressive care. We investigated how the decision to use aggressive versus non-aggressive management for non-convulsive status epilepticus impacts expected patient outcome for four etiologies: absence epilepsy, discontinued antiepileptic drugs, intraparenchymal hemorrhage, and hypoxic ischemic encephalopathy. Each etiology was defined by distinct values for five key parameters: baseline mortality rate of the inciting etiology; efficacy of non-aggressive treatment in gaining control of seizures; the relative contribution of seizures to overall mortality; the degree of excess disability expected in the case of delayed seizure control; and the mortality risk of aggressive treatment. RESULTS: Non-aggressive treatment was favored for etiologies with low morbidity and mortality such as absence epilepsy and discontinued antiepileptic drugs. The risk of aggressive treatment was only warranted in etiologies where there was significant risk of seizure-induced neurologic damage. In the case of post-anoxic status epilepticus, expected outcomes were poor regardless of the treatment chosen. The favored strategy in each case was determined by strong interactions of all five model parameters. CONCLUSIONS: Determination of the optimal management approach to non-convulsive status epilepticus is complex and is ultimately determined by the inciting etiology.

Emergence of persistent networks in long-term intracranial EEG recordings.

Over the past two decades, the increased ability to analyze network relationships among neural structures has provided novel insights into brain function. Most network approaches, however, focus on static representations of the brain's physical or statistical connectivity. Few studies have examined how brain functional networks evolve spontaneously over long epochs of continuous time. To address this, we examine functional connectivity networks deduced from continuous long-term electrocorticogram recordings. For a population of six human patients, we identify a persistent pattern of connections that form a frequency-band-dependent network template, and a set of core connections that appear frequently and together. These structures are robust, emerging from brief time intervals (~100 s) regardless of cognitive state. These results suggest that a metastable, frequency-band-dependent scaffold of brain connectivity exists from which transient activity emerges and recedes.

Mammalian sleep genetics.

Mammalian sleep is a complex phenomenon governed by the interplay of neural circuits and signaling systems. The impact of genetic manipulations on sleep-wake dynamics provides important insights into this complex behavior. Here we review the sleep-related phenotypes of over 50 transgenic animal models spanning a variety of signaling systems. This heterogeneous literature includes outcomes spanning motor activity patterns, sleep-wake stage architecture, responses to sleep deprivation, circadian rhythmicity, and other perturbations such as food restriction, temperature challenge, and infection exposure. Insights from these animal experiments hold potential to converge with the well-known sleep-wake neurocircuitry as well as the increasingly available human genetic information, especially in patient populations exhibiting sleep-wake pathology.

Revising the "Rule of Three" for inferring seizure freedom.

PURPOSE: How long after starting a new medication must a patient go without seizures before they can be regarded as seizure-free? A recent International League Against Epilepsy (ILAE) task force proposed using a "Rule of Three" as an operational definition of seizure freedom, according to which a patient should be considered seizure-free following an intervention after a period without seizures has elapsed equal to three times the longest preintervention interseizure interval over the previous year. This rule was motivated in large part by statistical considerations advanced in a classic 1983 paper by Hanley and Lippman-Hand. However, strict adherence to the statistical logic of this rule generally requires waiting much longer than recommended by the ILAE task force. Therefore, we set out to determine whether an alternative approach to the Rule of Three might be possible, and under what conditions the rule may be expected to hold or would need to be extended. METHODS: Probabilistic modeling and application of Bayes' rule. KEY FINDINGS: We find that an alternative approach to the problem of inferring seizure freedom supports using the Rule of Three in the way proposed by the ILAE in many cases, particularly in evaluating responses to a first trial of antiseizure medication, and to favorably-selected epilepsy surgical candidates. In cases where the a priori odds of success are less favorable, our analysis requires longer seizure-free observation periods before declaring seizure freedom, up to six times the average preintervention interseizure interval. The key to our approach is to take into account not only the time elapsed without seizures but also empirical data regarding the a priori probability of achieving seizure freedom conferred by a particular intervention. SIGNIFICANCE: In many cases it may be reasonable to consider a patient seizure-free after they have gone without seizures for a period equal to three times the preintervention interseizure interval, as proposed on pragmatic grounds in a recent ILAE position paper, although in other commonly encountered cases a waiting time up to six times this interval is required. In this work we have provided a coherent theoretical basis for modified criterion for seizure freedom, which we call the "Rule of Three-To-Six."

Classification algorithms for predicting sleepiness and sleep apnea severity.

Identifying predictors of subjective sleepiness and severity of sleep apnea are important yet challenging goals in sleep medicine. Classification algorithms may provide insights, especially when large data sets are available. We analyzed polysomnography and clinical features available from the Sleep Heart Health Study. The Epworth Sleepiness Scale and the apnea-hypopnea index were the targets of three classifiers: k-nearest neighbor, naive Bayes and support vector machine algorithms. Classification was based on up to 26 features including demographics, polysomnogram, and electrocardiogram (spectrogram). Naive Bayes was best for predicting abnormal Epworth class (0-10 versus 11-24), although prediction was weak: polysomnogram features had 16.7% sensitivity and 88.8% specificity; spectrogram features had 5.3% sensitivity and 96.5% specificity. The support vector machine performed similarly to naive Bayes for predicting sleep apnea class (0-5 versus >5): 59.0% sensitivity and 74.5% specificity using clinical features and 43.4% sensitivity and 83.5% specificity using spectrographic features compared with the naive Bayes classifier, which had 57.5% sensitivity and 73.7% specificity (clinical), and 39.0% sensitivity and 82.7% specificity (spectrogram). Mutual information analysis confirmed the minimal dependency of the Epworth score on any feature, while the apnea-hypopnea index showed modest dependency on body mass index, arousal index, oxygenation and spectrogram features. Apnea classification was modestly accurate, using either clinical or spectrogram features, and showed lower sensitivity and higher specificity than common sleep apnea screening tools. Thus, clinical prediction of sleep apnea may be feasible with easily obtained demographic and electrocardiographic analysis, but the utility of the Epworth is questioned by its minimal relation to clinical, electrocardiographic, or polysomnographic features.

Probabilistic sleep architecture models in patients with and without sleep apnea.

Sleep fragmentation of any cause is disruptive to the rejuvenating value of sleep. However, methods to quantify sleep architecture remain limited. We have previously shown that human sleep-wake stage distributions exhibit multi-exponential dynamics, which are fragmented by obstructive sleep apnea (OSA), suggesting that Markov models may be a useful method to quantify architecture in health and disease. Sleep stage data were obtained from two subsets of the Sleep Heart Health Study database: control subjects with no medications, no OSA, no medical co-morbidities and no sleepiness (n = 374); and subjects with severe OSA (n = 338). Sleep architecture was simplified into three stages: wake after sleep onset (WASO); non-rapid eye movement (NREM) sleep; and rapid eye movement (REM) sleep. The connectivity and transition rates among eight 'generator' states of a first-order continuous-time Markov model were inferred from the observed ('phenotypic') distributions: three exponentials each of NREM sleep and WASO; and two exponentials of REM sleep. Ultradian REM cycling was accomplished by imposing time-variation to REM state entry rates. Fragmentation in subjects with severe OSA involved faster transition probabilities as well as additional state transition paths within the model. The Markov models exhibit two important features of human sleep architecture: multi-exponential stage dynamics (accounting for observed bout distributions); and probabilistic transitions (an inherent source of variability). In addition, the model quantifies the fragmentation associated with severe OSA. Markov sleep models may prove important for quantifying sleep disruption to provide objective metrics to correlate with endpoints ranging from sleepiness to cardiovascular morbidity.