scholarly journals Voxelwise-based Brain Function Network using Multi-Graph Model

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhongyang Wang ◽  
Junchang Xin ◽  
Xinlei Wang ◽  
Zhiqiong Wang ◽  
Yue Zhao ◽  
...  
Keyword(s):  
Brain Function ◽  
Graph Model ◽  
Function Network

scholarly journals Processing of Affective Pictures: A Study Based on Functional Connectivity Network in the Cerebral Cortex

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongyang He ◽  
Kai Yang ◽  
Ning Zhuang ◽  
Ying Zeng
Keyword(s):  
Cerebral Cortex ◽  
Brain Function ◽  
Middle Temporal Gyrus ◽  
Emotional States ◽  
Eeg Signals ◽  
Brain Areas ◽  
Middle Temporal ◽  
Network Connections ◽  
The Difference ◽  
Function Network

Emotion plays an important role in people’s life. However, the existing researches do not give a unified conclusion on the brain function network under different emotional states. In this study, pictures from the international affective picture system (IAPS) of different valences were presented to subjects with a fixed frequency blinking frequency to induce stable state visual evoked potential (SSVEP). With the source location method, the cerebral cortex source signal was reconstructed based on EEG signals, and then the difference in SSVEP amplitudes in key brain areas under different emotional states and the difference in brain function network connections among different brain areas were analysed in cortical space. The results of the study show that positive and negative emotions evoked greater activation intensities in the prefrontal, temporal, and parietal lobes compared with those of neutral emotion. The network connections with a significant difference between emotional states mainly appear in the alpha and gamma bands, and the network connections with significant differences between positive emotion and negative emotion mainly exist in the right middle temporal gyrus and the superior frontal gyrus on both sides. In addition, the long-range connections play an important role in the process of emotional processing, especially the connections among frontal gyrus, middle temporal gyrus, and middle occipital gyrus. The results of this study provide a reliable scientific basis for revealing and elucidating the neural mechanism of emotion processing and the selection of brain regions and frequency bands in emotion recognition based on EEG signals.

Research on Modeling of Brain Function Network in Major Depressive Disorder

2014 ◽  
Vol 989-994 ◽  
pp. 2037-2042
Author(s):  
Li Min Niu ◽  
Hao Guo ◽  
Jun Jie Chen
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Brain Function ◽  
Evaluation Criteria ◽  
Real Data ◽  
Class Model ◽  
Major Depressive ◽  
Common Neighbor ◽  
Model Based ◽  
Function Network

In order to analyze the gap of function network between Major depressive disorder and health person, this paper studies with modeling approach. This paper analyzes the function network of Major depressive disorder with the model based on anatomical distance and the number of common neighbor. The result shows that the distribution of the optimal brain function network is linear in all volunteer. And the slope of the linear relationship in the patients is less than health, so we hope this point can be as secondary evidence to determine the person whether fall ill. And we also propose two models and those models of brain function are based on anatomical distance or the number of common neighbor. Create the evaluation criteria for select the optimal brain function model network in each class model based on select the maximum value in the proportion of the common edges of two network accounted all edges. Select the model that can simulate the real brain function network by comparison with real data fMRI network. Finally, the results show the best model only is based on anatomical distance .

scholarly journals Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rui Chen ◽  
Zhenzhong Li ◽  
Yi Lai
Keyword(s):  
Fractal Dimension ◽  
Data Fusion ◽  
Human Brain ◽  
Brain Function ◽  
Fusion System ◽  
Multimodal Neuroimaging ◽  
Neuroimaging Data ◽  
Time Accuracy ◽  
The Brain ◽  
Function Network

The purpose of this research is to explore the optimization and fusion application of multimodal neuroimaging technology and analyze the evaluation method of human brain fatigue based on multimodal neuroimaging technology. Based on electroencephalogram (EEG) and fMRI (functional magnetic resonance imaging), the four-dimensional consistency of local neural activities (FOCA) and local multimodal serial analysis (LMSA) are first introduced to fuse EEG and fMRI organically. Second, the eigenspace maximal information canonical correlation analysis (emiCCA) is introduced to construct the multimodal neuroimaging data fusion system. Finally, how the brain function network is constructed is introduced. Based on the binary and the weighted brain function networks, the relationship between the human brain fatigue and the brain function network is evaluated by calculating the fractal dimension. Results demonstrate that FOCA performs well in temporal and spatial consistency indexes, and the mean level and standard deviation in the case of temporal and spatial consistency are approximately 0.45. The effect of LMSA indexes is significantly better than generalized linear models (GLMs). Under different signal-to-noise ratios (SNRs), the regression coefficient based on LMSA is much larger than the GLM estimate; the corresponding significance level is p < 0.05 ; and the maximum value of the regression coefficient appears near 0.2. In the data fusion system, the time-space matching has good results under the time accuracy based on EEG and the space accuracy based on fMRI, with the time accuracy above 88% and the space accuracy above 89%. The fractal dimension analysis based on the brain function network reveals that the weighted brain function network is more sensitive to mental fatigue. The state of human brain fatigue will make the brain function network more complicated. The fractal dimension with more network edges is around 2.2, while the fractal dimension with fewer network edges is around 1.6. The proposed data analysis and fusion system have great application potential and propose a new idea for analyzing human brain fatigue and brain aging.

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