Jacqueline Fairley

Modulation of Vigilance in the Primary Hypersomnias by Endogenous Enhancement of GABAA Receptors

A component of cerebrospinal fluid in excessively sleepy people activates an inhibitory signaling pathway and mimics the actions of sedative-hypnotics. Awake and Refreshed A spindle prick on the finger, and Princess Aurora couldn’t keep her eyes open; one hundred years later, Sleeping Beauty was awakened with a kiss. But persistent daytime sleepiness—hypersomnolence—is no fairy tale, and neither the cause nor a cure is apparent. Now, Rye et al. begin to illuminate, in patients with primary hypersomnias, the neurobiology that underlies sleepiness of unknown etiology. A disabling condition, primary hypersomnia is characterized by lethargy, trance-like states, and “sleep drunkenness” even after prolonged, deep, nonrestorative sleep. The authors showed that cerebrospinal fluid (CSF) from these hypersomnolent subjects contains a small (500 to 3000 daltons) trypsin-sensitive substance that stimulates the in vitro function of selected γ-aminobutyric acid (GABA) receptors only in the presence of GABA—an inhibitory neurotransmitter that stimulates GABA receptors, quells consciousness, and induces sleep. GABA receptors are known to bind a class of psychoactive sedating drugs called benzodiazepines (BZDs). Hypersomnolent CSF samples mimicked the effects of BZD on GABA receptors but did not compete with BZD binding in human brain tissue, suggesting that the newly identified substance functions by a distinct mechanism. Furthermore, the BZD receptor antagonist flumazenil reversed hypersomnolent-CSF activation of GABA signaling, even though the drug is known to be a competitive antagonist of BZD and blocks BZD action by binding to the classical BZD-binding domain of GABA receptors. Most importantly, flumazenil restored vigilance in some hypersomnolent subjects. Together, these mechanistic studies pinpoint a potential new neuropharmacological pathway for a 25-year-old drug. The current study suggests that one of the “spindle pricks” that puts hypersomnolent subjects to sleep is a substance in CSF that augments inhibitory GABA signaling. A deeper understanding of the neurobiology of primary hypersomnia should help scientists discover new “kisses” that restore wakefulness—in fewer than 100 years. The biology underlying excessive daytime sleepiness (hypersomnolence) is incompletely understood. After excluding known causes of sleepiness in 32 hypersomnolent patients, we showed that, in the presence of 10 μM γ-aminobutyric acid (GABA), cerebrospinal fluid (CSF) from these subjects stimulated GABAA receptor function in vitro by 84.0 ± 40.7% (SD) relative to the 35.8 ± 7.5% (SD) stimulation obtained with CSF from control subjects (Student’s t test, t = 6.47, P < 0.0001); CSF alone had no effect on GABAA signaling. The bioactive CSF component had a mass of 500 to 3000 daltons and was neutralized by trypsin. Enhancement was greater for α2 subunit– versus α1 subunit–containing GABAA receptors and negligible for α4 subunit–containing ones. CSF samples from hypersomnolent patients also modestly enhanced benzodiazepine (BZD)–insensitive GABAA receptors and did not competitively displace BZDs from human brain tissue. Flumazenil—a drug that is generally believed to antagonize the sedative-hypnotic actions of BZDs only at the classical BZD-binding domain in GABAA receptors and to lack intrinsic activity—nevertheless reversed enhancement of GABAA signaling by hypersomnolent CSF in vitro. Furthermore, flumazenil normalized vigilance in seven hypersomnolent patients. We conclude that a naturally occurring substance in CSF augments inhibitory GABA signaling, thus revealing a new pathophysiology associated with excessive daytime sleepiness.

Computer detection approaches for the identification of phasic electromyographic (EMG) activity during human sleep

BACKGROUND: Examination of spontaneously occurring phasic muscle activity from the human polysomnogram may have considerable clinical importance for patient care, yet most attempts to quantify the detection of such activity have relied upon laborious and intensive visual analyses. We describe in this study innovative signal processing approaches to this issue. METHODS: We examined multiple features of surface electromyographic signals based on 16,200 individual 1-second intervals of low impedance sleep recordings. We validated which of those features most closely mirrored the careful judgments of trained human observers in making discriminations of the presence of short-lived (100-500 msec) phasic activity, and also examined which features provided maximal differences across 1-second intervals and which features were least susceptible to residual levels of amplifier noise. RESULTS: Our data suggested particularly promising and novel features (e.g., Non-linear energy, 95(th) percentile of Spectral Edge Frequency) for developing automated systems for quantifying muscle activity during human sleep. CONCLUSIONS: The EMG signals recorded from surface electrodes during sleep can be processed with techniques that reflect the visually based analyses of the human scorer but also offer potential for discerning far more subtle effects, Future studies will explore both the clinical utility of these techniques and their relative susceptibility to and/or independence from signal artifacts.

Sleep Correlates of Trait Executive Function and Memory in Parkinson's Disease

BACKGROUND Sleep disturbance and cognitive impairment are correlated in non-diseased populations, but their association in Parkinsons disease (PD) is uncertain. Prior studies examining measures of cognition in relation to sleep have used either self-report or actigraphically measured sleep and have produced conflicting findings. OBJECTIVE In this descriptive study, we correlated measurements of sleep in PD patients derived from the gold-standard measurement, in-lab polysomnography, with an extensive battery of cognitive measures. We hypothesized that poorer sleep would be related to relatively more impaired cognition. METHODS Idiopathic PD patients (n = 34) completed a cognitive battery encompassing three broad domains (executive function, immediate memory and delayed memory), and underwent PSG for two nights. Scores for each domain from individual cognitive measures were converted to z-scores and then averaged to produce a composite score. We used second night PSG data and quantified measures of sleep architecture, sleep continuity, sleep apnea and nocturnal movement (periodic leg movements, PLMS). RESULTS Lower executive function was associated with higher PLMS after controlling for chronological age, mini-mental state examination scores, and UPDRS motor subscale scores. These results were independent from psychomotor speed. There was a marginally significant positive correlation between the proportion of time spent in REM and immediate recall ability. Measures of sleep continuity and sleep apnea were unrelated to cognition in these patients. CONCLUSIONS PLMS, known to be a frequent feature of PSG-measured sleep in PD, may be an important correlate of impaired executive function in PD. Whether treating this disorder of sleep results in improvement in cognition remains to be determined.

Phasic Electromyographic Metric detection based on wavelet analysis

The Phasic Electromyographic Metric (PEM) has been recently introduced as a sensitive indicator to differentiate Parkinsons Disease (PD) patients from controls, non-PD patients with a history of Rapid Eye Movement Disorder (RBD) from controls, and PD patients with early and late stage disease. However, PEM assessment through visual inspection is a cumbersome and time consuming process. Therefore, a reliable automated approach is required so as to increase the utilization of PEM as a reliable and efficient clinical tool to track PD progression. In this study an automated method for the detection of PEM is presented, based on the use of signal analysis and pattern recognition techniques. The results are promising indicating that an automatic PEM identification procedure is feasible.

Automated polysomnogram artifact compensation using the generalized singular value decomposition algorithm

Manual/visual polysomnogram (psg) analysis is a standard and commonly implemented procedure utilized in the diagnosis and treatment of sleep related human pathologies. Current technological trends in psg analysis focus upon translating manual psg analysis into automated/computerized approaches. A necessary first step in establishing efficient automated human sleep analysis systems is the development of reliable pre-processing tools to discriminate between outlier/artifact instances and data of interest. This paper investigates the application of an automated approach, using the generalized singular value decomposition algorithm, to compensate for specific psg artifacts.

Semi-Automated Annotation of Phasic Electromyographic Activity

Recent research on manual/visual identification of phasic muscle activity utilizing the phasic electromyographic metric (PEM) in human polysomnograms (PSGs) cites evidence that PEM is a potentially reliable quantitative metric to assist in distinguishing between neurodegenerative disorder populations and age-matched controls. However, visual scoring of PEM activity is time consuming-preventing feasible implementation within a clinical setting. Therefore, here we propose an assistive/semi-supervised software platform designed and tested to automatically identify and characterize PEM events in a clinical setting that will be extremely useful for sleep physicians and technicians. The proposed semi-automated approach consists of four levels: A) Signal Parsing, B) Calculation of quantitative features on candidate PEM events, C) Classification of PEM and non-PEM events using a linear classifier, and D) Post-processing/Expert feedback to correct/remove automated misclassifications of PEM and Non-PEM events. Performance evaluation of the designed software compared to manual labeling is provided for electromyographic (EMG) activity from the PSG of a control subject. Results indicate that the semi-automated approach provides an excellent benchmark that could be embedded into a clinical decision support system to detect PEM events that would be used in neurological disorder identification and treatment.

Inter-rater Agreement for Visual Discrimination of Phasic and Tonic Electromyographic Activity in Sleep.

Study Objectives The objective of this study was to determine the confidence of expert raters in discriminating phasic and tonic electromyographic (EMG) activity. We undertook this study because we suspected that even expert scorers may disagree on whether a given EMG segment contained phasic activity, tonic activity, or both. Methods Six individuals holding either Fellowship status in the American Academy of Sleep Medicine or Board Certification in Sleep Medicine with at least 5 years experience in interpreting polysomnography visually examined 60 segments containing EMG activity. Raters determined their relative confidence that each segment contained phasic and tonic activity by noting whether they were highly certain or somewhat certain that the segment contained such activity or somewhat certain or highly certain that each segment did not contain such activity. Every segment was rated by every rater twice, once for phasic and once for tonic activity. Results Substantial differences among raters existed in certainty regarding presence/absence of both phasic and tonic activity, although raters agreed on segments far above chance. Consensus was higher on certainty regarding presence of phasic, relative to tonic, activity. Conclusions These findings indicate the limitations of visual analyses for discriminating abnormal muscle activity during sleep. Conversely, when expert judgments are combined with digitized measurements of EMG activity in sleep (e.g. REM atonia index), some allowance must be made for the unique contribution of visual analyses to such judgments, most notably for short duration EMG signals. These results may have relevance for polysomnographic interpretation in suspected synucleinopathies.

Towards a fully automated tool for annotation of phasic electromyographic activity

Salient muscle activity identification via the phasic electromyographic metric (PEM) in human polysomnograms/sleep studies (PSGs) represent a potential quantitative metric to aid in differentiation between neurodegenerative disorder populations and age-matched controls. A major impairment to the implementation of PEM analysis for clinical assessment of neurodegenerative disorders includes the time consuming aspects for both visual and automated supervised methods, which require exhaustive expert scoring of PEM and non-PEM events. In order to surmount the aforementioned concerns, we propose a semi-supervised classification methodology encased within an easy-to-use graphical user interface (GUI) utilizing an embedded Minimum Description Length (MDL) criterion to automatically classify PEM and non-PEM events based on expert labeling of a single PEM instance. Results indicate that the application of a semi-supervised approach for PEM identification provides an excellent option to reduce the labeling burden within current human PSG muscle activity identification schemes.