Fast Brain Network Dynamics
We are developing novel techniques using methods from deep/machine learning (e.g. RNNs and Hidden Markov Models) that make it possible to identify dynamic brain networks in neuroimaging data at a wide-range of timescales.
Applied to MEG data, this has led to the identification of resting state networks in MEG at 100ms time-scales, orders of magnitude faster than has been shown previously .
Moreover, we have shown that transitions between different brain networks are not random, and instead are hierarchically organised in a manner that predicts behaviour .
We have also shown that the occurrence of network states coincides with synchronised oscillatory activity across distributed brain networks, consistent with the idea that they provide a mechanism for organising communication across the brain .
Tools for doing this kind of analysis (e.g. HMMMAR) are available to download and use at the OHBA Analysis Group Software Page.
- Baker AP, Brookes MJ, Rezek IA, Smith SM, Behrens T, Probert Smith PJ, Woolrich M. Fast transient networks in spontaneous human brain activity. Elife 2014.
- Vidaurre, D., Smith, S. M., Woolrich, M. Brain Network Dynamics are Hierarchically Organised in Time. PNAS 2017.
- Vidaurre D, Hunt L, Quinn AJ, Hunt BAE, Brookes MJ, Nobre AC, Woolrich M. Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nature Communications. 2018.