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.
Story Source:
The above story is based on materials provided by PLOS. Note:
Materials may be edited for content and length.
Journal Reference:
Michael J. Prerau, Katie E. Hartnack, Gabriel Obregon-Henao,
Aaron Sampson, Margaret Merlino, Karen Gannon, Matt T. Bianchi, Jeffrey M.
Ellenbogen, Patrick L. Purdon. Tracking the Sleep Onset Process: An
Empirical Model of Behavioral and Physiological Dynamics. PLoS
Computational Biology, 2014; 10 (10): e1003866 DOI: 10.1371/journal.pcbi.1003866
Cite This Page:
PLOS. "Falling asleep: Revealing the point of
transition." Science Daily,
2 October 2014. <www.sciencedaily.com/releases/2014/10/141002141805.htm>.
