Tracking of dynamic functional connectivity from MEG data with Kalman filtering

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Abstract

Owing to their millisecond-scale temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) are well-suited tools to study dynamic functional connectivity between regions in the human brain. However, current techniques to estimate functional connectivity from MEG/EEG are based on a two-step approach; first, the MEG/EEG inverse problem is solved to estimate the source activity, and second, connectivity is estimated between the sources. In this work, we propose a method for simultaneous estimation of source activities and their dynamic functional connectivity using a Kalman filter. Based on simulations, our approach can reliably estimate source activities and resolve their time-varying interactions even at low SNR (< 1). When applied on empirical MEG responses to simple visual stimuli, our approach could capture the dynamic patterns of the underlying functional connectivity changes between the lower (pericalcarine) and higher (fusiform and parahippocampal) visual areas. In conclusion, we demonstrate that our approach is capable of tracking changes in functional connectivity at the millisecond resolution of MEG/EEG and thus making it suitable for real-time tracking of functional connectivity, which none of the current techniques are capable of.

Original languageEnglish
Title of host publicationProceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherIEEE
Pages1003-1006
Number of pages4
Volume2018-July
ISBN (Electronic)9781538636466
DOIs
Publication statusPublished - 26 Oct 2018
MoE publication typeA4 Article in a conference publication
EventAnnual International Conference of the IEEE Engineering in Medicine and Biology Society - Honolulu, United States
Duration: 17 Jul 201821 Jul 2018
Conference number: 40

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
ISSN (Print)2375-7477
ISSN (Electronic)1557-170X

Conference

ConferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC
CountryUnited States
CityHonolulu
Period17/07/201821/07/2018

Keywords

  • electroencephalography
  • dynamic functional connectivity
  • MEG data
  • Kalman filtering
  • millisecond-scale temporal resolution
  • MEG-EEG inverse problem
  • magnetoencephalography
  • source activities
  • time-varying interactions
  • visual stimuli
  • parahippocampal area
  • fusiform area
  • pericalcarine area

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  • Cite this

    Tronarp, F., Subramaniyam, N. P., Särkkä, S., & Parkkonen, L. (2018). Tracking of dynamic functional connectivity from MEG data with Kalman filtering. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (Vol. 2018-July, pp. 1003-1006). [8512456] (Annual International Conference of the IEEE Engineering in Medicine and Biology Society). IEEE. https://doi.org/10.1109/EMBC.2018.8512456