Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Olena G. Filatova, Yuan Yang, Julius P A Dewald, Runfeng Tian, Pablo Maceira-Elvira, Yusuke Takeda, Gert Kwakkel, Okito Yamashita, Frans C.T. van der Helm

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number79
JournalFrontiers in Neural Circuits
Volume12
DOIs
StatePublished - Oct 1 2018

Fingerprint

Diffusion Magnetic Resonance Imaging
Electroencephalography
Stroke
Brain
Neuroimaging
Multimodal Imaging
Magnetic Resonance Imaging
Brain Injuries
Fingers
Extremities
Hand
Research

Keywords

  • Brain dynamics
  • Diffusion MRI
  • EEG
  • Somatosensory evoked potentials (SEP)
  • Stroke

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Sensory Systems
  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

Cite this

Filatova, Olena G. ; Yang, Yuan ; Dewald, Julius P A ; Tian, Runfeng ; Maceira-Elvira, Pablo ; Takeda, Yusuke ; Kwakkel, Gert ; Yamashita, Okito ; van der Helm, Frans C.T. / Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke : A Proof-of-Principle Study. In: Frontiers in Neural Circuits. 2018 ; Vol. 12.
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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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Filatova, OG, Yang, Y, Dewald, JPA, Tian, R, Maceira-Elvira, P, Takeda, Y, Kwakkel, G, Yamashita, O & van der Helm, FCT 2018, 'Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study' Frontiers in Neural Circuits, vol. 12, 79. https://doi.org/10.3389/fncir.2018.00079

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke : A Proof-of-Principle Study. / Filatova, Olena G.; Yang, Yuan; Dewald, Julius P A; Tian, Runfeng; Maceira-Elvira, Pablo; Takeda, Yusuke; Kwakkel, Gert; Yamashita, Okito; van der Helm, Frans C.T.

In: Frontiers in Neural Circuits, Vol. 12, 79, 01.10.2018.

Research output: Contribution to journalArticle

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T1 - Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke

T2 - A Proof-of-Principle Study

AU - Filatova, Olena G.

AU - Yang, Yuan

AU - Dewald, Julius P A

AU - Tian, Runfeng

AU - Maceira-Elvira, Pablo

AU - Takeda, Yusuke

AU - Kwakkel, Gert

AU - Yamashita, Okito

AU - van der Helm, Frans C.T.

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AB - 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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