Brain Network Dynamics using HMMs
Hidden Markov Models
We are developing novel techniques using methods from machine learning, such as Hidden Markov Models (HMMs), that make it possible to identify dynamic brain networks and dynamic functional connectivity in neuroimaging data at a wide range of time scales. These techniques have also been adapted to work on big population data .
Applied to MEG data, this has led to the identification of resting state networks in MEG at 100 ms time scales, orders of magnitude faster than has been shown previously . Using fMRI data, 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 brain network states in MEG coincides with synchronised oscillatory activity across distributed brain networks, consistent with the idea that they provide a mechanism for organising communication across the brain .
The occurrence of brain network states has also been used to predict different psychiatric and neurological conditions, e.g. multiple sclerosis  and Alzheimer’s Disease ; to predict task conditions ; and to predict occurrence of spontaneous replay of recently acquired information .
Tools for performing these kinds of HMM analyses are available to download and use at the OHBA Analysis Group Software Page (using the HMM-MAR toolbox).
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- 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.
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- Higgins C, Liu Y, Vidaurre D, Kurth-Nelson Z, Dolan R, Behrens T, Woolrich M. Replay bursts in humans coincide with activation of the default mode and parietal alpha networks. Neuron, 2021.