Main content
Task-Evoked Dynamic Network Analysis through Hidden Markov Modelling
- Andrew Quinn
- Diego Vidaurre
- Romesh Abeysuriya
- Robert Becker
- Anna Christina Nobre
- Mark Woolrich
Date created: | Last Updated:
: DOI | ARK
Creating DOI. Please wait...
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.
Add important information, links, or images here to describe your project.
Citation
Recent Activity
Start managing your projects on the OSF today.
Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.