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Task-Evoked Dynamic Network Analysis through Hidden Markov Modelling
- Andrew Quinn
- Diego Vidaurre
- Romesh Abeysuriya
- Robert Becker
- Anna Christina Nobre
- Mark Woolrich
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Category: Analysis
Description: We show how the HMM can be inferred on continuous, parcellated source-space Magnetoencephalography (MEG) task data in an unsupervised manner, without any knowledge of the task timings. We apply this to a freely available MEG dataset in which participants completed a face perception task, and reveal task-dependent HMM states that represent whole-brain dynamic networks transiently bursting at millisecond time scales as cognition unfolds.
This project contains the code and toolboxes accompanying the Quinn et. al. manuscript "Task-Evoked Dynamic Network Analysis through Hidden Markov Modelling"
The main analysis code can be viewed or downloaded from the ohba-analysis github page here ( also linked under 'Files' below). The full code along with associated toolboxes can be downloaded in the 'HMM_Task_Download.zip' file linked below.
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