scholarly journals Brain network reconstruction of speech production based on electro-encephalography and eye movement

2020 ◽  
Vol 41 (1) ◽  
pp. 349-350
Author(s):  
Bin Zhao ◽  
Jinfeng Huang ◽  
Gaoyan Zhang ◽  
Jianwu Dang ◽  
Minbo Chen ◽  
...  
Keyword(s):  
Eye Movement ◽  
Speech Production ◽  
Network Reconstruction ◽  
Brain Network

Revealing Spatiotemporal Brain Dynamics of Speech Production Based on EEG and Eye Movement

2018 ◽  
Author(s):  
Bin Zhao ◽  
Jinfeng Huang ◽  
Gaoyan Zhang ◽  
Jianwu Dang ◽  
Minbo Chen ◽  
...  
Keyword(s):  
Eye Movement ◽  
Speech Production ◽  
Brain Dynamics

scholarly journals Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep

2010 ◽  
Vol 30 (34) ◽  
pp. 11379-11387 ◽  
Author(s):  
V. I. Spoormaker ◽  
M. S. Schroter ◽  
P. M. Gleiser ◽  
K. C. Andrade ◽  
M. Dresler ◽  
...  
Keyword(s):  
Eye Movement ◽  
Large Scale ◽  
Brain Network ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals Changes in cross-frequency coupling following closed-loop auditory stimulation in non-rapid eye movement sleep

2019 ◽  
Author(s):  
Krugliakova Elena ◽  
Volk Carina ◽  
Jaramillo Valeria ◽  
Sousouri Georgia ◽  
Huber Reto
Keyword(s):  
Eye Movement ◽  
Closed Loop ◽  
Auditory Stimulation ◽  
Brain Network ◽  
Slow Waves ◽  
Dependent Manner ◽  
Rapid Eye Movement ◽  
Frequency Coupling ◽  
The Right ◽  
Cross Frequency Coupling

AbstractThe activity of different brain networks in non-rapid eye movement (NREM) sleep is regulated locally in an experience-dependent manner, reflecting the extent of the network load during wakefulness. In particular, improved task performance after sleep correlates with the local post-learning power increase of neocortical slow waves and faster oscillations such as sleep spindles and their temporal coupling. Recently, it was demonstrated that by targeting slow waves in a particular region at a particular phase with closed-loop auditory stimulation it is possible to locally manipulate slow-wave activity and interact with training-induced neuroplastic changes. Based on this finding, we tested whether closed-loop auditory stimulation targeting the up-phase of slow-waves over the right sensorimotor area might affect power in delta, theta and sigma bands and coupling between these oscillations within the circumscribed region. We demonstrate that while closed-loop auditory stimulation globally enhances power in delta, theta and sigma bands, changes in cross-frequency coupling of these oscillations were more spatially restricted. In particular, stimulation induced a significant decrease of delta-theta coupling in frontal channels, within the area of the strongest baseline coupling between these frequency bands. In contrast, a significant increase in delta-sigma coupling was observed over the right parietal area, located directly posterior to the target electrode. These findings suggest that closed-loop auditory stimulation locally modulates coupling between delta phase and sigma power in a targeted region, which could be used to manipulate sleep-dependent memory formation within the brain network of interest.

scholarly journals Adaptive Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes

2017 ◽  
Author(s):  
Seyedeh-Rezvan Farahibozorg ◽  
Richard N Henson ◽  
Olaf Hauk
Keyword(s):  
Image Segmentation ◽  
Cross Talk ◽  
Real Data ◽  
Network Reconstruction ◽  
Brain Network ◽  
Reconstruction Method ◽  
List Type ◽  
Resolution Limit ◽  
Starting Point ◽  
Sensor Configuration

AbstractThere is growing interest in the rich temporal and spectral properties of the brain's functional connectome that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of parcels while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions for EEG/MEG configurations from real data, both algorithms yielded approximately 70 parcels despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, resolution matrices of adaptive parcellations showed notably higher sensitivity and distinguishability of parcels. Furthermore, extensive simulations of realistic networks under various circumstances revealed significant improvements in network reconstruction accuracies, particularly in reducing false leakage-induced connections. Adaptive parcellations therefore allow a more accurate reconstruction of functional EEG/MEG connectomes.HighlightsWe introduce adaptive cortical parcellation algorithms for E/MEG source estimation.Algorithms are based on cross-talk functions and image segmentation methods.The resulting parcellations yielded ~70 parcels regardless of starting point.Sensitivity and distinguishability improved compared to anatomical parcellations.Accuracy of realistic whole-brain network reconstruction improved significantly.

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