Validity of an algorithm for determining sleep/wake states using FS-760 in school-aged children.

BACKGROUND: Actigraphy is a method used for determining sleep (S)/wakefulness (W) by actigraph, a device equipped with a built-in accelerometer and an algorithm validated for each device. The S/W determination algorithm for the waist-worn actigraph FS-760 has been formulated for adults. However, the algorithm for children has not been established. The purpose of this study was to formulate an algorithm for discriminating S/W in school-aged children using FS-760 and to evaluate its validity. We further tested the generalizability of existing algorithm for adults by applying it to the children's activity data and then examined factors associated with adult algorithm agreement rates by multiple regression analysis using combined adult and children data. METHODS: Sixty-five, healthy, school-aged children (aged 6 to 15 years) were recruited and randomly assigned to two groups: A (n = 33) and B (n = 32). They underwent 8-h polysomnography (PSG) and wore FS-760 simultaneously to obtain activity data. To determine the central epoch of the sleep/wake states (𝑥), a five-order linear discriminant analysis was conducted using the activity intensity of group A for five epochs (𝑥-2, 𝑥-1, 𝑥, 𝑥+1, 𝑥+2; 10 min) and evaluate its accuracy with the activity of group B. To reveal the factors associated with adult algorithm agreement rate, we integrated the activity, age, sleep efficiency of 15 adults (aged 20 to 39 years) and those of 65 children for multiple regression analysis. RESULTS: The mean agreement rate of the developed algorithm was 91.0%, with a mean sensitivity (true sleep detection rate) of 93.0% and a mean specificity (true wakefulness detection rate) of 63.9%. The agreement rate of the adult algorithm applied to children's activity was significantly lower (81.8%) than that of the children algorithm. Multiple regression analysis showed that the agreement rates calculated by the adult algorithm were significantly related to mean activity of the 𝑥 epoch in NREM and REM sleep as well as age and sleep efficiency. CONCLUSIONS: The S/W states in school-aged children can be reliably assessed using the developed algorithm for waist-worn actigraph FS-760. Since the accuracy of the adult algorithms decreased when applied it to children which have different activity levels during sleep, the establishment and validation of population-specific S/W algorithms should be required.

The Role of the Thalamus in the Neurological Mechanism of Subjective Sleepiness: An fMRI Study.

PURPOSE: The thalamus, the region that forms the attentional network and transmits external sensory signals to the entire brain, is important for sleepiness. Herein, we examined the relationship between activity in the thalamus-seed brain network and subjective sleepiness. MATERIALS AND METHODS: Fifteen healthy male participants underwent an experiment comprising a baseline evaluation and two successive interventions, a 9-day sleep extension followed by 1-night total sleep deprivation. Pre- and post-intervention tests included the Karolinska sleepiness scale and neuroimaging for arterial spin labeling and functional connectivity. We examined the association between subjective sleepiness and the functional magnetic resonance imaging indices. RESULTS: The functional connectivity between the left or right thalamus and various brain regions displayed a significant negative association with subjective sleepiness, and the functional connectivity between the left and right thalamus displayed a significant positive association with subjective sleepiness. The graph theory analysis indicated that the number of positive functional connectivity related to the thalamus showed a strong negative association with subjective sleepiness, and conversely, the number of negative functional connectivity showed a positive association with subjective sleepiness. Arterial spin labeling analysis indicated that the blood flow in both the left and right thalami was significantly negatively associated with subjective sleepiness. Functional connectivity between the anterior cingulate cortex and salience network areas of the left insular cortex, and that between the anterior and posterior cingulate cortices showed a strong positive and negative association with subjective sleepiness, respectively. CONCLUSION: Subjective sleepiness and the thalamic-cortical network dynamics are strongly related, indicating the application of graph theory to study sleepiness and consciousness. These results also demonstrate that resting functional connectivity largely reflects the "state" of the subject, suggesting that the control of sleep and conscious states is essential when using functional magnetic resonance imaging indices as biomarkers.

Tracking intermediate performance of vigilant attention using multiple eye metrics.

Vigilance deficits account for a substantial number of accidents and errors. Current techniques to detect vigilance impairment measure only the most severe level evident in eyelid closure and falling asleep, which is often too late to avoid an accident or error. The present study sought to identify ocular biometrics of intermediate impairment of vigilance and develop a new technique that could detect a range of deficits in vigilant attention (VA). Sixteen healthy adults performed well-validated Psychomotor Vigilance Test (PVT) for tracking vigilance attention while undergoing simultaneous recording of eye metrics every 2 hours during 38 hours of continuous wakefulness. A novel marker was found that measured VA when the eyes were open-the prevalence of microsaccades. Notably, the prevalence of microsaccades decreased in response to sleep deprivation and time-on-task. In addition, a novel algorithm for detecting multilevel VA was developed, which estimated performance on the PVT by integrating the novel marker with other eye-related indices. The novel algorithm also tracked changes in intermediate level of VA (specific reaction times in the PVT, i.e. 300-500 ms) during prolonged time-on-task and sleep deprivation, which had not been tracked previously by conventional techniques. The implication of the findings is that this novel algorithm, named "eye-metrical estimation version of the PVT: PVT-E," can be used to reduce human-error-related accidents caused by vigilance impairment even when its level is intermediate.

Unrecognized Sleep Loss Accumulated in Daily Life Can Promote Brain Hyperreactivity to Food Cue.

Epidemiological studies have shown that sleep debt increases the risk of obesity. Experimental total sleep deprivation (TSD) has been reported to activate the reward system in response to food stimuli, but food-related responses in everyday sleep habits, which could lead to obesity, have not been addressed. Here, we report that habitual sleep time at home among volunteers without any sleep concerns was shorter than their optimal sleep time estimated by the 9-day extended sleep intervention, which indicates that participants had already been in sleep debt in their usual sleep habits. The amygdala and anterior insula, which are responsible for both affective responses and reward prediction, were found to exhibit significantly lowered activity in the optimal sleep condition. Additionally, a subsequent one-night period of TSD reactivated the right anterior insula in response to food images; however, the activity level of amygdala remained lowered. These findings indicate that (1) our brain is at risk of hyperactivation to food triggers in everyday life, which could be a risk factor for obesity and lifestyle diseases, and (2) optimal sleep appears to reduce this hypersensitivity to food stimuli.

Recovery from Unrecognized Sleep Loss Accumulated in Daily Life Improved Mood Regulation via Prefrontal Suppression of Amygdala Activity.

Many modern people suffer from sleep debt that has accumulated in everyday life but is not subjectively noticed [potential sleep debt (PSD)]. Our hypothesis for this study was that resolution of PSD through sleep extension optimizes mood regulation by altering the functional connectivity between the amygdala and prefrontal cortex. Fifteen healthy male participants underwent an experiment consisting of a baseline (BL) evaluation followed by two successive interventions, namely, a 9-day sleep extension followed by one night of total sleep deprivation (TSD). Tests performed before and after the interventions included a questionnaire on negative mood and neuroimaging with arterial spin labeling MRI for evaluating regional cerebral blood flow (rCBF) and functional connectivity. Negative mood and amygdala rCBF were significantly reduced after sleep extension compared with BL. The amygdala had a significant negative functional connectivity with the medial prefrontal cortex (FCamg-MPFC), and this negative connectivity was greater after sleep extension than at BL. After TSD, these indices reverted to the same level as at BL. An additional path analysis with structural equation modeling showed that the FCamg-MPFC significantly explained the amygdala rCBF and that the amygdala rCBF significantly explained the negative mood. These findings suggest that the use of our sleep extension protocol normalized amygdala activity via negative amygdala-MPFC functional connectivity. The resolution of unnoticed PSD may improve mood by enhancing frontal suppression of hyperactivity in the amygdala caused by PSD accumulating in everyday life.

Estimating individual optimal sleep duration and potential sleep debt.

In this study, we hypothesized that dynamics of sleep time obtained over consecutive days of extended sleep in a laboratory reflect an individual's optimal sleep duration (OSD) and that the difference between OSD and habitual sleep duration (HSD) at home represents potential sleep debt (PSD). We found that OSD varies among individuals and PSD showed stronger correlation with subjective/objective sleepiness than actual sleep time, interacting with individual's vulnerability of sleep loss. Furthermore, only 1 h of PSD takes four days to recover to their optimal level. Recovery from PSD was also associated with the improvement in glycometabolism, thyrotropic activity and hypothalamic-pituitary-adrenocortical axis. Additionally, the increase (rebound) in total sleep time from HSD at the first extended sleep would be a simple indicator of PSD. These findings confirmed self-evaluating the degree of sleep debt at home as a useful clinical marker. To establish appropriate sleep habits, it is necessary to evaluate OSD, vulnerability to sleep loss, and sleep homeostasis characteristics on an individual basis.

Validity of an algorithm for determining sleep/wake states using a new actigraph.

BACKGROUND: This study aimed to develop an algorithm for determining sleep/wake states by using chronological data on the amount of physical activity (activity intensity) measured with the FS-750 actigraph, a device that can be worn at the waist, allows for its data to be downloaded at home, and is suitable for use in both sleep research and remote sleep medicine. METHODS: Participants were 34 healthy young adults randomly assigned to two groups, A (n =17) and B (n =17), who underwent an 8-hour polysomnography (PSG) in the laboratory environment. Simultaneous activity data were obtained using the FS-750 attached at the front waist. Sleep/wake state and activity intensity were calculated every 2 minutes (1 epoch). To determine the central epoch of the sleep/wake states (x), a five-variable linear model was developed using the activity intensity of Group A for five epochs (x-2, x-1, x, x+1, x+2; 10 minutes). The optimal coefficients were calculated using discriminant analysis. The agreement rate of the developed algorithm was then retested with Group B, and its validity was examined. RESULTS: The overall agreement rates for group A and group B calculated using the sleep/wake score algorithm developed were 84.7% and 85.4%, respectively. Mean sensitivity (agreement rate for sleep state) was 88.3% and 90.0% and mean specificity (agreement rate for wakeful state) was 66.0% and 64.9%, respectively. These results confirmed comparable agreement rates between the two groups. Furthermore, when applying an estimation rule developed for the sleep parameters measured by the FS-750, no differences were found in the average values between the calculated scores and PSG results, and we also observed a correlation between the two sets of results. Thus, the validity of these evaluation indices based on measurements from the FS-750 is confirmed. CONCLUSIONS: The developed algorithm could determine sleep/wake states from activity intensity data obtained with the FS-750 with sensitivity and specificity equivalent to that determined with conventional actigraphs. The FS-750, which is smaller, less expensive, and able to take measurements over longer periods than conventional devices, is a promising tool for advancing sleep studies at home and in remote sleep medicine.