Tracking of dynamic functional connectivity from MEG data with Kalman filtering

Filip Tronarp; Lauri Parkkonen; Simo Särkkä; Narayan P Subramaniyam

doi:10.1109/EMBC.2018.8512456

Tracking of dynamic functional connectivity from MEG data with Kalman filtering

Filip Tronarp, Lauri Parkkonen, Simo Särkkä, Narayan P Subramaniyam

Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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 (

Original language	English
Title of host publication	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	978-1-5386-3646-6, 978-1-5386-3645-9
ISBN (Print)	978-1-5386-3647-3
DOIs	https://doi.org/10.1109/EMBC.2018.8512456
Publication status	Published - 2018
Externally published	Yes
Event	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, United States Duration: 2018 Jul 18 → 2018 Jul 21

Conference

Conference	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Country/Territory	United States
City	Honolulu
Period	2018/07/18 → 2018/07/21

Subject classification (UKÄ)

Computer graphics and computer vision

Access to Document

10.1109/EMBC.2018.8512456

Cite this

@inproceedings{0dd9b37a37b6483d94f9418c8da38ccf,

title = "Tracking of dynamic functional connectivity from MEG data with Kalman filtering",

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 (",

author = "Filip Tronarp and Lauri Parkkonen and Simo S{\"a}rkk{\"a} and Subramaniyam, {Narayan P}",

year = "2018",

doi = "10.1109/EMBC.2018.8512456",

language = "English",

isbn = "978-1-5386-3647-3",

booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ; Conference date: 18-07-2018 Through 21-07-2018",

}

Tronarp, F, Parkkonen, L, Särkkä, S & Subramaniyam, NP 2018, Tracking of dynamic functional connectivity from MEG data with Kalman filtering. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE - Institute of Electrical and Electronics Engineers Inc., 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) , Honolulu, United States, 2018/07/18. https://doi.org/10.1109/EMBC.2018.8512456

Tracking of dynamic functional connectivity from MEG data with Kalman filtering. / Tronarp, Filip; Parkkonen, Lauri; Särkkä, Simo et al.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE - Institute of Electrical and Electronics Engineers Inc., 2018.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Tracking of dynamic functional connectivity from MEG data with Kalman filtering

AU - Tronarp, Filip

AU - Parkkonen, Lauri

AU - Särkkä, Simo

AU - Subramaniyam, Narayan P

PY - 2018

Y1 - 2018

N2 - 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 (

AB - 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 (

U2 - 10.1109/EMBC.2018.8512456

DO - 10.1109/EMBC.2018.8512456

M3 - Paper in conference proceeding

SN - 978-1-5386-3647-3

BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Y2 - 18 July 2018 through 21 July 2018

ER -

Tracking of dynamic functional connectivity from MEG data with Kalman filtering

Abstract

Conference

Subject classification (UKÄ)

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