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The experimental model of self-awakening highlights several important issues, particularly the lack of a reliable model for estimating the time interval, from minutes to hours, that passes between falling asleep at night and the moment of self-awakening. This may be explained by limited knowledge of ultradian brain oscillators (i.e., biorhythms with periods shorter than 24 hours) related to the perception of time during nighttime sleep, although the processes involved in the internal timing of circadian rhythms have been extensively studied. These are biologically regulated by a relatively small group of around 10,000 neurons in the suprachiasmatic nucleus of the hypothalamus, oscillating with a 24-hour rhythm.
Little is known about ultradian timekeepers of sleep, especially related to the REM (Rapid Eye Movement) sleep cycle, but they likely play a crucial role in self-awakening. So far, self-awakening has been studied using various subjective and objective methodologies, including questionnaires, sleep diaries, actigraphy, and polysomnography. However, no study has integrated all these different approaches Objective The primary aim of this project will be to investigate the psychophysiological and neuropsychophysiological characteristics underlying the phenomenon of self-awakening, introducing an innovative multimodal approach by combining three main methodologies (ecological assessment, neuropsychological approach, and polysomnographic recording). Specifically, the first part of the study will aim to confirm, through an actigraphic survey, that subjects who report this ability at a subjective level (evaluated via the SAQ questionnaire) are indeed capable of performing it. Secondly, the polysomnographic study will aim to evaluate the EEG characteristics of 'self-awakeners' in the 30 minutes preceding self-awakening, compared to those preceding forced awakening caused by an external stimulus. Finally, the neuropsychological assessment will aim to evaluate differences in the cognitive control domains and temporal estimation abilities in 'self-awakeners' compared to subjects unable to self-awaken.
The hypothesis is that subjects capable of self-awakening will exhibit distinctive neurophysiological and neuropsychological characteristics. In particular, these subjects are expected to show a reduction in the density and power of slow waves starting 30 minutes before self-awakening, thus indicating the ability to inhibit deep sleep in preparation for wakefulness. Additionally, it is hypothesized that these subjects may exhibit better performance in neuropsychological variables related to cognitive control and temporal estimation.
Sleep is often mistakenly conceived as a passive state; however, it actually plays a fundamental role in regulating daytime cognitive functions such as memory and emotion (Klinzing et al., 2019). Additionally, other phenomena suggest that complex cognitive processing may be active during sleep. For example, lucid dreaming, the ability to be aware of dreaming while in the dream, involves a form of volitional control during sleep (Baird et al., 2019). Another scarcely studied experience related to regained cognitive activity during sleep is self-awakening. People habitually use alarms to wake up at a specific time in the morning. Interestingly, some individuals report the ability to wake up without the aid of any timekeeper; we refer to this phenomenon as self-awakening. Although the experience is commonly reported, certain specifics are needed to identify and understand the phenomenon.
Firstly, awakening from sleep can be induced by external stimuli but may also occur spontaneously. Secondly, spontaneous awakening can be divided into natural awakening, caused by a "natural" cessation of sleep due to the dissipation of physiological sleep pressure, or self-awakening, driven by the intent to wake up. Thirdly, self-awakening itself requires clarification: it may be habitual (i.e., a person wakes up at the same time every day), in which case circadian rhythms are the driving factor, or it can be induced by will. This latter phenomenon is the primary focus of interest here.
Studying self-awakening is not only fascinating but could also provide insights into the presence of specific cognitive processes even at minimal or absent levels of consciousness. There are at least three cognitive operations that a sleeping person must successfully perform to awaken at the intended time. First, they must encode and remember the target time, which should be easily accessible; second, elapsed time during sleep should be constantly estimated and compared to the target time; third, one must regain voluntary control during sleep to awaken. All these operations must be carried out while the sleeping subject is in a state typically characterized by minimal levels of consciousness (Tononi & Massimini, 2008).
The experimental model of self-awakening highlights several important issues, particularly the lack of a reliable model for estimating the time interval, from minutes to hours, that passes between falling asleep at night and the moment of self-awakening. This may be explained by limited knowledge of ultradian brain oscillators (i.e., biorhythms with periods shorter than 24 hours) related to the perception of time during nighttime sleep, although the processes involved in the internal timing of circadian rhythms have been extensively studied. These are biologically regulated by a relatively small group of around 10,000 neurons in the suprachiasmatic nucleus of the hypothalamus, oscillating with a 24-hour rhythm.
Little is known about ultradian timekeepers of sleep, especially related to the REM (Rapid Eye Movement) sleep cycle, but they likely play a crucial role in self-awakening. So far, self-awakening has been studied using various subjective and objective methodologies, including questionnaires, sleep diaries, actigraphy, and polysomnography. However, no study has integrated all these different approaches Objective The primary aim of this project will be to investigate the psychophysiological and neuropsychophysiological characteristics underlying the phenomenon of self-awakening, introducing an innovative multimodal approach by combining three main methodologies (ecological assessment, neuropsychological approach, and polysomnographic recording). Specifically, the first part of the study will aim to confirm, through an actigraphic survey, that subjects who report this ability at a subjective level (evaluated via the SAQ questionnaire) are indeed capable of performing it. Secondly, the polysomnographic study will aim to evaluate the EEG characteristics of 'self-awakeners' in the 30 minutes preceding self-awakening, compared to those preceding forced awakening caused by an external stimulus. Finally, the neuropsychological assessment will aim to evaluate differences in the cognitive control domains and temporal estimation abilities in 'self-awakeners' compared to subjects unable to self-awaken.
The hypothesis is that subjects capable of self-awakening will exhibit distinctive neurophysiological and neuropsychological characteristics. In particular, these subjects are expected to show a reduction in the density and power of slow waves starting 30 minutes before self-awakening, thus indicating the ability to inhibit deep sleep in preparation for wakefulness. Additionally, it is hypothesized that these subjects may exhibit better performance in neuropsychological variables related to cognitive control and temporal estimation.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Self Awakening |
| ||
| Control |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Not applicable- observational study | Other | For observational studies, participants are not assigned an intervention as part of the study |
|
| Measure | Description | Time Frame |
|---|---|---|
| Primary outcomes will involve actigraphic evaluation | • Actigraphic assessment will be conducted over two weeks, during which subjects will be asked to perform self-awakenings in the second week of recording. The actual number of self-awakenings will then be calculated based on the requested self-awakenings, as well as accuracy, defined as the discrepancy in minutes between the target wake-up time and the actual wake-up time. | From enrollment to the end of assessment at 2 weeks |
| Primary outcomes will involve polysomnographic evaluation | Polysomnographic recording will be employed to assess neurophysiological brain dynamics under conditions of forced awakening and during a night with self-awakening, along with a neuropsychological approach to assess cognitive control and time estimation abilities. The reduction in the density and power of slow waves in the 30 minutes prior to awakening in the two different conditions will be calculated. | From enrollment to the end of assessment at 2 weeks |
| Primary outcomes will involve neuropsychological evaluation | Neuropsychological assessment will include the flanker task, measuring accuracy and reaction times under various cognitive control conditions, and an interval reproduction task, evaluating accuracy and errors | From enrollment to the end of assessment at 2 weeks |
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| Measure | Description | Time Frame |
|---|---|---|
| Other relevant outcomes for the study | The selection and identification of self-awakening subjects will involve completing a specific questionnaire for the detection of Self-Awakening, sleep assessment and for the evaluation of anxiety and depressive symptoms and the evaluation of cognitive and somatic levels of pre-sleep arousal. | From enrollment to the end of assessment at 2 weeks |
Inclusion Criteria:
Exclusion Criteria:
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IRCCS Ospedale San Raffaele UO Neurologia - Centro di Medicina del Sonno Via Stamira D'Ancona, 20 20127 Milano, Italy
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Luigi Ferini-Strambi, MD | Contact | +390226433363 | ferinistrambi.luigi@hsr.it |
| Name | Affiliation | Role |
|---|---|---|
| Luigi Ferini-Strambi, MD | UO Neurologia Centro di Medicina del sonno | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| UO Neurologia Centro di Medicina del sonno | Recruiting | Milan | Italy | 20127 | Italy |
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