FALLING ASLEEP : REVEALING THE POINT OF TRANSITION
How can we tell when
someone has fallen asleep? To answer this question, scientists at Massachusetts
General Hospital have developed a new statistical method and behavioural task
to track the dynamic process of falling asleep
Dr Michael Prerau, Dr
Patrick Purdon, and their colleagues used the evolution of brain activity,
behaviour, and other physiological signals during the sleep onset process to
automatically track the continuous changes in wakefulness experienced as a
subject falls asleep.
The study, publishing
today in PLOS Computational Biology, suggests that it is not when
one falls asleep, but how one falls asleep that matters. Using these methods,
the authors quantified a subset of healthy subjects who behaved as though they
were awake even though their brains, by current clinical definitions, were
asleep.
Understanding the
process of falling asleep is an important problem in neuroscience and sleep
medicine. Given that current clinical methods are time-consuming, subjective,
and simplify the sleep onset process in ways that limit the accuracy, the
authors combine the state-of-the-art in neuroscience and signal processing to
design an accurate and efficient way to characterise sleep.
The researchers
replaced a standard measure, the behavioural response task, which uses sounds
that can disturb sleep, with a new task centred on a subject's focused natural
breathing -- an act which may even promote sleep. They modeled the
physiological and behavioural changes occurring during sleep onset as a
continuum that can develop gradually over time.
The identification of
some subjects who continued to perform the task even though current clinical
measures would say they were asleep suggests a natural variation in the way
cortical and thalamic networks interact in these people.
"Ultimately, such
methods could greatly improve clinicians' ability to diagnose sleep disorders
and to more precisely measure the effects of sleep drugs and other
medications," remarked Dr Prerau.
Future work will look
to improve the understanding of the mechanisms underlying neural dynamics
during sleep, as well as the development of more sophisticated diagnostic and
monitoring tools.
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