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Fast Brain Network Dynamics

Fast transient networks in spontaneous human brain activity captured using an HMM

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 [1].

Moreover, we have shown that transitions between different brain networks are not random, and instead are hierarchically organised in a manner that predicts behaviour [2].

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 [3].

Tools for doing this kind of analysis (e.g. HMMMAR) are available to download and use at the OHBA Analysis Group Software Page.

  1. 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.
  2. Vidaurre, D., Smith, S. M., Woolrich, M. Brain Network Dynamics are Hierarchically Organised in Time. PNAS 2017. 
  3. 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.
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