Dynamic information flow based on EEG and diffusion MRI in stroke

A proof-of-principle study

Journal article (2018)

Authors

O.G. Filatova Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Y. Yang Northwestern University, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
J.P.A. Dewald Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering , Northwestern University
Runfeng Tian Student
Pablo Maceira-Elvira Swiss Federal Institute of Technology, Student
Yusuke Takeda ATR, RIKEN Center for Emergent Matter Science (CEMS)
Gert Kwakkel Amsterdam UMC
Okito Yamashita RIKEN Center for Emergent Matter Science (CEMS), ATR
Frans van der Helm Northwestern University, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Frans C T van der Helm Northwestern University, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
FCT van der Helm Northwestern University, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Frans C.T. van der Helm Northwestern University, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
F.C.T. van der Helm Northwestern University, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 O.G. Filatova, Y. Yang, J.P.A. Dewald, Runfeng Tian, Pablo Maceira-Elvira, Yusuke Takeda, Gert Kwakkel, Okito Yamashita, F.C.T. van der Helm
DOI: https://doi.org/10.3389/fncir.2018.00079
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Published Date

2018

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

Abstract

In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.

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