Resting State Networks in MEG
When subjects lie motionless inside scanners without any particular task to perform, their brains show repeating patterns of spontaneous activity across regions known as resting state networks. Intriguingly, these networks are the same as those that are recruited when the brain is performing tasks. If we can understand spontaneous activity, then this will provide new insights into the structure and function of cortical circuitry in general.
We are developing new methods to characterise the resting state brain using the dynamic information available in MEG. For example, with our collaborators at UCL and Nottingham, we have been able to use Independent Component Analysis (ICA) to identify resting state networks independently in MEG for the first time. These methods are being used to gain new insights in basic neuroscience, and in clinical research into diseases such as Alzheimer's.
We have also developed new techniques to identify these networks are much faster time scales than has been shown before. See Fast Transiently Synchonising Networks for more on this.
Brookes et al. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc Natl Acad Sci USA (2011) vol. 108 (40) pp. 16783-8
Luckhoo et al. Inferring task-related networks using independent component analysis in magnetoencephalography. Neuroimage (2012) vol. 62 (1) pp. 530-41
Luckhoo et al. Multi-session statistics on beamformed MEG data. Neuroimage (2014) pp. 1-6