Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling.

TLDR: This work shows 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, and reveals task-dependent HMM states that represent whole-brain dynamic networks transiently bursting at millisecond time scales as cognition unfolds.

Abstract: Complex thought and behaviour arise through dynamic recruitment of large-scale brain networks The signatures of this process may be observable in electrophysiological data; yet robust modelling of rapidly changing functional network structure on rapid cognitive timescales remains a considerable challenge Here, we present one potential solution using Hidden Markov Models (HMMs), which are able to identify brain states characterised by engaging distinct functional networks that reoccur over time 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 The analysis pipeline demonstrates a general way in which the HMM can be used to do a statistically valid whole-brain, group-level task analysis on MEG task data, which could be readily adapted to a wide range of task-based studies