TY - JOUR
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.
N1 - Funding Information:
This research was funded by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement n. 291339, project 4DEEG: A new tool to investigate the spatial and temporal activity patterns in the brain. YY was supported by NIH National Center for Advancing Translational Sciences (UL1TR001422), Northwestern University Clinical and Translational Research Institute Voucher Program. JD was supported by NIH grants R01HD039343 and R01NS058667. YT and OY were supported by the ImPACT Program of the Council for Science, Technology and Innovation (Cabinet Office, Government of Japan).
Publisher Copyright:
© 2018 Filatova, Yang, Dewald, Tian, Maceira-Elvira, Takeda, Kwakkel, Yamashita and van der Helm.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
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.
KW - Brain dynamics
KW - Diffusion MRI
KW - EEG
KW - Somatosensory evoked potentials (SEP)
KW - Stroke
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U2 - 10.3389/fncir.2018.00079
DO - 10.3389/fncir.2018.00079
M3 - Article
C2 - 30327592
AN - SCOPUS:85054841395
SN - 1662-5110
VL - 12
JO - Frontiers in Neural Circuits
JF - Frontiers in Neural Circuits
M1 - 79
ER -