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.
Highlights
Stroke is a sudden interruption of the blood supply to the brain due to a vessel occlusion or rupture (World Health Organization, 2015)
This study is a proof of principle for the clinical applicability of Variational Bayesian Multimodal Encephalography (VBMEG) method, demonstrating its new application regarding the somatosensory stimulations and indicating its potential for the study of hemiparetic stroke, which has not been done in previous studies
Different from conventional EEG connectivity methods that are purely based on mathematical modeling and signal correlation, our method considers physical connections between cortical sources, which reduces the chance of false positive in connectivity assessment, as indicated by Figure 7 and Table 6
Summary
Stroke is a sudden interruption of the blood supply to the brain due to a vessel occlusion or rupture (World Health Organization, 2015). Most survivors suffer from hemiparesis, making it more difficult to perform activities of daily living. Clinical tests, such as Fugl-Meyer motor scores, indicate the severity of neural impairment following a stroke, but do not provide insight to the changes within the brain that occur after the incident and during recovery (Gladstone et al, 2002). One of the main strategies is to investigate brain responses to external stimuli. This can be achieved with various non-invasive brain imaging techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) (Bandara et al, 2016; Weinstein et al, 2017)
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