| Reference code: | PT/FB/BL-2012-220.02 |
| Location: | Arquivo PCA - Pasta 15/2012
|
Title:
| Using whole-brain computational modelling for identifying hubs necessary for transitioning between sleep stages measured with MEG
|
| Publication year: | 2014
|
URL:
| http://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=4da09ec6-f098-4bf1-9af3-8457926fe748&cKey=56e8f950-5ebd-4db6-819e-f6589b25cdf9&mKey=54c85d94-6d69-4b09-afaa-502c0e680ca7
|
| Abstract/Results: | ABSTRACT:
Sleep in normal adults is characterised by highly consistent state-transitions in the brain over time. Compared to the descent to sleep, which is, at least partly, a voluntary act, the switching between sleep stages appears almost mechanistic. The temporal order and relationship between the brain states of various sleep stages are remarkably constant. Describing the whole-brain activity of individual sleep stages was one of the first merits of electroencephalography (EEG), and more advanced forms of neuroimaging have expanded our understanding of the spatiotemporal unfolding of sleep. Yet, the mechanisms underlying, and brain regions orchestrating the transitions between wakefulness and the various sleep states remain unresolved. Understanding this may lead to important insights into not only the fundamental principles of the human brain function but also the causes of sleep disorders. Viewing the brain as an intricately connected network, in which activity occurs as a result of communication between parts of this network has helped the investigation of spontaneous brain activity. By combining analysis of structural imaging data, such as diffusion tensor imaging (DTI), and functional imaging data, such as functional Magnetic Resonance Imaging and magnetoencephalography (MEG), computational modelling has successfully been applied to describe how spontaneous dynamics can arise from the structural properties of the network. Modelling of whole-brain activity can assist in elucidating the causal links facilitating the transitions between brain states of sleep. In computational terms the aim is to understand the interplay between integration and segregation in the brain and to find the important binding regions that are necessary and sufficient for network transitions between states. In the current study we used MEG to measure whole-brain activity of 11 healthy adults that went through the different phases of sleep. We obtained the spatiotemporal dynamics of brain activity by extracting the slow fluctuating changes in the Hilbert power envelope of frequency filtered and beamformed time-series. A Hidden Markov Model (HMM) makes it possible to resolve non-stationarity of functional networks. Thus each sleep stage was tested as individual transient states of the network. Finally, we applied a whole-brain computational model that allowed us to identify the necessary and sufficient brain regions binding information across the brain and facilitating the transitions between brain states during sleep.
|
| Accessibility: | Document does exist in file
|
Language:
| eng
|
Author:
| Stevner, A.
|
Secondary author(s):
| Piantoni, G., Colclough, G., Woolrich, M., Parsons, C., Cabral, J., Van Someren, E., van der Werf, Y., Deco, G., Kringelbach, M.
|
Document type:
| Online abstract
|
Number of reproductions:
| 1
|
Reference:
| Stevner, A., Piantoni, G., Colclough, G., Woolrich, M., Parsons, C., Cabral, J., Van Someren, E., van der Werf, Y., Deco, G., & Kringelbach, M. (2014, November). Using whole-brain computational modelling for identifying hubs necessary for transitioning between sleep stages measured with MEG. Poster presented at the 2014 Society for Neuroscience meeting, Washington, D. C. Abstract retrieved from http://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=4da09ec6-f098-4bf1-9af3-8457926fe748&cKey=56e8f950-5ebd-4db6-819e-f6589b25cdf9&mKey=54c85d94-6d69-4b09-afaa-502c0e680ca7
|
| Indexed document: | No
|
| Keywords: | Sleep / Network / MEG
|
|