To detect REM and wake stages in sleep, this study generates feature variables from the correlation of two-channel EOG signals and the amplitude of LEOG signal. By using the VQ method to quantize these signals into different codewords and by calculating the number of appearances of these codewords, we are able to establish a feature vector for every epoch of the recorded EOG signals. Via a three-stage process, the personalized classification accuracy for REM and wake sleep stages are about 95% and 86%, respectively. By combining these personalized classifiers to perform REM and wake stages detection for other unseen individuals, the classification accuracy for REM and wake sleep stages, the classification accuracy become 85% and 92%. However, the sensitivity for the wake stage detection is merely 52%.

Introduction: Cataplexy - the sudden involuntary loss of skeletal muscle tone – is a defining feature of narcolepsy. The current study aimed to determine if cataplexy is influenced by direct manipulation of REM sleep circuitry. We did this by pharmacogenetically inhibiting the REM sleep center, subcoeruleus (Sub-C). Methods: Inhibitory DREADD (hM4D-Gi) was bilaterally targeted to the Sub-C in hypocretin knockout mice (n=7). Intraperitoneal administration of clozapine-n-oxide was used to inhibit Sub-C cells expressing hM4D-Gi. Electrophysiological and behavioral criteria were used to characterize cataplexy and REM sleep. Results: Sub-C inhibition increased REM sleep and cataplexy amounts (p<0.05). Sub-C inhibition increased time spent in cataplexy amounts by increasing the number of cataplexy attacks (p<0.05). This intervention triggered increases in basal muscle tone during REM sleep, but had negligible effects on muscle tone during cataplexy (p>0.05). Conclusion: Pharmacogenetic manipulation of the Sub-C suggest that REM sleep and cataplexy are mediate by similar neural mechanism.

Introduction: Cataplexy - the sudden involuntary loss of skeletal muscle tone – is a defining feature of narcolepsy. The current study aimed to determine if cataplexy is influenced by direct manipulation of REM sleep circuitry. We did this by pharmacogenetically inhibiting the REM sleep center, subcoeruleus (Sub-C). Methods: Inhibitory DREADD (hM4D-Gi) was bilaterally targeted to the Sub-C in hypocretin knockout mice (n=7). Intraperitoneal administration of clozapine-n-oxide was used to inhibit Sub-C cells expressing hM4D-Gi. Electrophysiological and behavioral criteria were used to characterize cataplexy and REM sleep. Results: Sub-C inhibition increased REM sleep and cataplexy amounts (p<0.05). Sub-C inhibition increased time spent in cataplexy amounts by increasing the number of cataplexy attacks (p<0.05). This intervention triggered increases in basal muscle tone during REM sleep, but had negligible effects on muscle tone during cataplexy (p>0.05). Conclusion: Pharmacogenetic manipulation of the Sub-C suggest that REM sleep and cataplexy are mediate by similar neural mechanism.

Nervous systems distinguish between self- and other-generated movements by monitoring discrepancies between planned and performed actions. To do so, when motor systems transmit motor commands to muscles, they simultaneously transmit motor copies, or corollary discharges, to sensory areas. There, corollary discharge signals are compared to sensory feedback arising from movements (reafference), which can result in gating of expected feedback. Curiously, in infant rats, twitches—which are self-generated movements produced exclusively and abundantly during active sleep (AS)—differ from wake-movements in that they trigger robust neural activity. Accordingly, we hypothesized that the gating actions of corollary discharge that predict wake reafference are suspended during twitching. In this dissertation, we first demonstrate that twitches, but not wake movements, robustly activate sensorimotor cortex as they do other brain areas. Next, we demonstrate that wake movements can activate the sensorimotor cortex under conditions involving presumed discrepancies between corollary discharge and reafference signals. Lastly, we reveal a neural mechanism in the brainstem that inhibits reafference, but only during wakefulness; this inhibitory mechanism is suppressed during active sleep. All together, our findings provide the first demonstration of a state-dependent neural comparator of planned and performed actions, one that permits the transmission of sensory feedback from self-generated twitches to the developing nervous system.

One primary topic of sleep studies is the depth of sleep. According to definitions of R&K rules, human sleep can be roughly divided into three different stages: Awake, Non-rapid-eye-movement (NREM) Sleep, and Rapid-eye-movement (REM) Sleep. Moreover, sleep stages are scored mainly by EEG signals and complementally by EOG and EMG signals. Many researchers have indicated that diseases or disorders occur during sleep will affect life quality of patients. For example, REM sleep-related dyssomnia is highly correlated with neurodegenerative or mental disorders such as major depression. Furthermore, sleep apnea is one of the most common sleep disorders at present. Untreated sleep apnea can increase the risk of mental and cardiovascular diseases. This research proposes a detection method of REM sleep. Take into account the environment of homecare, we just extract and analyze EOG signals for the sake of convenience in comparison with EEG channels. By analyzing elementary waveforms of EOG signals based on VQ method, the proposed method performs a classification accuracy of 67.71% in a group application. The corresponding sensitivity and specificity are 73.38% and 68.95% respectively. In contrast, the average classification accuracy is 82.02% in personalized applications. And the corresponding average sensitivity and specificity are 83.05% and 81.62% respectively. Experimental results demonstrate the feasibility of detecting REM sleep via the proposed method, especially in personalized applications. This will be propitious to a long term tracing and research of personal sleep status.

The mechanisms underlying the generation of rapid eye movement (REM) sleep are poorly understood. Despite a lack of direct support, neurons maximally active during REM sleep (REM sleep-active) located in the pedunculopontine tegmental nucleus (PPTn) are hypothesized to generate this state and its component phenomenology. This hypothesis has never been directly tested, since the results of selectively inhibiting this cell-group have never been determined. Using microdialysis, electrophysiology, histochemical and pharmacological methods in freely-behaving rats (n=22) instrumented for sleep-wake state and respiratory muscle recordings, I selectively inhibited REM sleep-active PPTn neurons. Contrary to the prevailing hypothesis, I showed that REM sleep-active PPTn neurons suppress REM sleep by limiting the frequency of its onset. These neurons also shape the impact of REM sleep on breathing. REM sleep-active PPTn neurons restrain behavioural activation of upper-airway musculature during REM sleep, while depressing breathing rate and respiratory activation of the upper-airway musculature across sleep-wake-states.

The mechanisms underlying the generation of rapid eye movement (REM) sleep are poorly understood. Despite a lack of direct support, neurons maximally active during REM sleep (REM sleep-active) located in the pedunculopontine tegmental nucleus (PPTn) are hypothesized to generate this state and its component phenomenology. This hypothesis has never been directly tested, since the results of selectively inhibiting this cell-group have never been determined. Using microdialysis, electrophysiology, histochemical and pharmacological methods in freely-behaving rats (n=22) instrumented for sleep-wake state and respiratory muscle recordings, I selectively inhibited REM sleep-active PPTn neurons. Contrary to the prevailing hypothesis, I showed that REM sleep-active PPTn neurons suppress REM sleep by limiting the frequency of its onset. These neurons also shape the impact of REM sleep on breathing. REM sleep-active PPTn neurons restrain behavioural activation of upper-airway musculature during REM sleep, while depressing breathing rate and respiratory activation of the upper-airway musculature across sleep-wake-states.

Since studies have revealed sleeping quality is highly related to our health conditions, sleep-medicine has attracted more and more attention in recent years. Sleep staging is one of the most important elements of sleep-medicine. Traditionally, it¡¦s done by observing the information form of EEG, EOG and EMG signals. But this is almost not possible to achieve at home. Automatic detection of REM sleep is the main goal of this study. Via comparing the classification performances of different combinations of EEG, EOG and EMG signals, this study also tries to simplify the number of signal channels. By using features extracted from EEG, EOG and EMG signals, the back-propagation neural networks are used to distinguish REM and NREM sleep. By refining the outputs of the neural networks, this study extensively test the efficacy of the proposed approach by using databases from two different sleep centers. This work also investigates the influences of the number of signal channels, REM sleep ratio, AHI, and age on classification results. Data acquired from the sleep centers of China Medical University Hospital (CMUH) and Sheng-Mei Hospital are arranged in ten different groups. For our largest datasets, which consists of 1318 subjects from CMUH, the results show that the proposed method achieves 95.5% epoch-to-epoch agreement with Cohen's Kappa 0.833, sensitivity 85.9% and specificity 97.3%. The generalization accuracy is 94.1% with Cohen's Kappa 0.782, sensitivity 78.5% and specificity 97.3%.

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Among all of the devastating effects that Parkinson’s disease (PD) has on an individual, sleep dysfunction is one that can have a profound effect on the entire family of the patient. The most potentially destructive of these sleep syndromes being that of Rapid Eye Movement Sleep Behavior Disorder (RBD). This disorder not only causes sleep impairment to the patient, but can occasionally result in life-threatening injury to the individual or their bed partner. While this condition is manageable with medication, the current treatment of choice is a long-acting benzodiazepine, clonazepam. This drug, while effective in treating RBD, comes with a significant burden of side effects. Patients with neurodegenerative disorders, like PD, are at even higher risk of suffering the negative impacts of this treatment. One potential alternative treatment that has been considered is a supplement of exogenous melatonin, a hormone that plays a role in maintaining one’s circadian rhythm. Several small case studies have shown potential efficacy of this treatment, and with very few side effects. However, this efficacy has not yet been proven by randomized clinical trial. This proposed study will perform a double-blind randomized clinical trial of melatonin vs. placebo in a population of PD patients with RBD. Subjects will be analyzed via polysomnographic sleep study, where symptoms will be scored on the RBD Severity Scale (RBDSS) at baseline and after a treatment intervention. Statistical analysis will then ascertain whether or not a significant symptom reduction is seen following melatonin treatment, compared to a group receiving placebo. If melatonin proves to be efficacious in this patient population, this would give clinicians a new treatment option to consider to effectively manage symptoms of RBD with a much lower risk of potentially harmful side effects. Finding an effective method of managing this condition, the prevalence of which continues to rise worldwide, will have a great impact on improving the safety and quality of life of these patients.