Task-Evoked Dynamic Network Analysis through Hidden Markov Modelling
Date created: | Last Updated:
: DOI | ARK
Creating DOI. Please wait...
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.