Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling.

Quinn AJ, Vidaurre D, Abeysuriya R, Becker R, Nobre AC, Woolrich MW

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

Complex thought and behavior arise through dynamic recruitment of large-scale brain networks. The signatures of this process may be observable in electrophysiological data; yet robust modeling 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 characterized 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.

Original languageEnglish
Article number603
JournalFrontiers in Neuroscience
Volume12
IssueAUG
ISSN1662-4548
DOIs
Publication statusPublished - 2018
Externally publishedYes

Keywords

  • Dynamic
  • Hidden Markov model
  • MEG analysis
  • Magnetoencephalography
  • Network

Fingerprint

Dive into the research topics of 'Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling.'. Together they form a unique fingerprint.

Cite this