Dynamic Functional Connectivity and Fast Transient Networks
One puzzling feature of resting state networks is that they seem to last for relatively long times, on the order of several seconds. However, the majority of studies have used fMRI, in which changes in the level of oxygen in the blood are used as a proxy for the activity of a given brain region. Since changes in blood oxygen occur relatively slowly, the ability of fMRI to detect rapid changes in activity is limited.
In this work we have used MEG to show that the activity of resting state networks is much shorter lived than previously thought. MEG scanners measure changes in the magnetic fields generated by electrical currents in the brain, which means that they can detect alterations in brain activity much more rapidly than fMRI.
We have combined MEG measurements with a new methodology based on Hidden Markov Modelling (HMM), which can estimate dynamic functional connectivity. This reveals that individual resting state networks are typically stable for on the order of 100 ms. Moreover, transitions between different networks do not occur randomly; instead, certain networks are much more likely to become active after others. This work suggests that the resting brain is constantly changing between different patterns of activity, which enables it to respond quickly to any given situation.
Analysis methods such as this are being used to gain new insights into the role of functional networks in health and disease.
The tool for doing this kind of analysis (GLEAN) is available to download and use at the OHBA Analysis Group Software Page.
Baker et al. Fast transient networks in spontaneous human brain activity. Elife (2014) vol. 3 pp. e01867
Vidaurre et al. Spectrally resolved fast transient brain states in electrophysiological data. NeuroImage (2016). Accepted