Task-evoked dynamic network analysis through hidden Markov modelling
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 cha...
| Main Authors: | , , , , , |
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| Format: | Journal article |
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Frontiers Media
2018
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| _version_ | 1797093731601481728 |
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| author | Quinn, A Vidaurre, D Abeysuriya, R Becker, R Nobre, A Woolrich, M |
| author_facet | Quinn, A Vidaurre, D Abeysuriya, R Becker, R Nobre, A Woolrich, M |
| author_sort | Quinn, A |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T04:04:28Z |
| format | Journal article |
| id | oxford-uuid:c5acfc98-f799-4eff-9841-ea47b3504107 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:04:28Z |
| publishDate | 2018 |
| publisher | Frontiers Media |
| record_format | dspace |
| spelling | oxford-uuid:c5acfc98-f799-4eff-9841-ea47b35041072022-03-27T06:32:48ZTask-evoked dynamic network analysis through hidden Markov modellingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c5acfc98-f799-4eff-9841-ea47b3504107Symplectic Elements at OxfordFrontiers Media2018Quinn, AVidaurre, DAbeysuriya, RBecker, RNobre, AWoolrich, MComplex 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. |
| spellingShingle | Quinn, A Vidaurre, D Abeysuriya, R Becker, R Nobre, A Woolrich, M Task-evoked dynamic network analysis through hidden Markov modelling |
| title | Task-evoked dynamic network analysis through hidden Markov modelling |
| title_full | Task-evoked dynamic network analysis through hidden Markov modelling |
| title_fullStr | Task-evoked dynamic network analysis through hidden Markov modelling |
| title_full_unstemmed | Task-evoked dynamic network analysis through hidden Markov modelling |
| title_short | Task-evoked dynamic network analysis through hidden Markov modelling |
| title_sort | task evoked dynamic network analysis through hidden markov modelling |
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