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 GABA(A) 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 GABA(A) 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 GABA(A) receptors and negligible for α4 subunit-containing ones. CSF samples from hypersomnolent patients also modestly enhanced benzodiazepine (BZD)-insensitive GABA(A) 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 GABA(A) receptors and to lack intrinsic activity--nevertheless reversed enhancement of GABA(A) 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.
Study Objectives: In young adults, napping is hypothesized to benefit episodic memory retention (eg, via consolidation). Whether this relationship is present in older adults has not been adequately tested but is an important question because older adults display marked changes in sleep and memory. Design: Between-subjects design. Setting: Sleep laboratory at Emory University School of Medicine. Participants: Fifty healthy young adults (18-29) and 45 community-dwelling older adults (58-83). Intervention: Participants were randomly assigned to a 90-minute nap opportunity or an equal interval of quiet wakefulness. Measurements and Results: Participants underwent an item-wise directed forgetting learning procedure in which they studied words that were individually followed by the instruction to "remember" or "forget." Following a 90-minute retention interval filled with quiet wakefulness or a nap opportunity, they were asked to free recall and recognize those words. Young adults retained significantly more words following a nap interval than a quiet wakefulness interval on both free recall and recognition tests. There was modest evidence for greater nap-related retention of "remember" items relative to "forget" items for free recall but not recognition. Older adults' memory retention did not differ across nap and quiet wakefulness conditions, although they demonstrated greater fragmentation, lower N3, and lower rapid eye movement duration than the young adults. Conclusions: In young adults, an afternoon nap benefits episodic memory retention, but such benefits decrease with advancing age.
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.
Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100–500 msec, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1 sec epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets.
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