Functional Integration and Separation of Brain Network Based on Phase Locking Value During Emotion Processing

2020 ◽  
pp. 1-1
Author(s):  
Zhong-Min Wang ◽  
Rui Zhou ◽  
Yan He ◽  
Xiao-Min Guo
Keyword(s):  
Phase Locking ◽  
Emotion Processing ◽  
Functional Integration ◽  
Brain Network ◽  
Phase Locking Value

scholarly journals Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding

2021 ◽  
Vol 15 ◽  
Author(s):  
Baoguo Xu ◽  
Leying Deng ◽  
Dalin Zhang ◽  
Muhui Xue ◽  
Huijun Li ◽  
...  
Keyword(s):  
Parietal Lobe ◽  
Phase Locking ◽  
Brain Network ◽  
Internal Communication ◽  
Peak Performance ◽  
Source Imaging ◽  
Planning Stage ◽  
Grand Average ◽  
Reach And Grasp ◽  
Phase Locking Value

Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals A note on the phase locking value and its properties

NeuroImage ◽  
2013 ◽  
Vol 74 ◽  
pp. 231-244 ◽  
Author(s):  
Sergul Aydore ◽  
Dimitrios Pantazis ◽  
Richard M. Leahy
Keyword(s):  
Phase Locking ◽  
Phase Locking Value

Experts EEG Cognitive Analysis and Substance Classification in Security Inspection

2019 ◽  
Vol 33 (04) ◽  
pp. 1950006 ◽  
Author(s):  
Yue Yuan ◽  
Hong Wang ◽  
Peixin Yuan
Keyword(s):  
Phase Locking ◽  
Low Frequency ◽  
Brain Network ◽  
Eeg Signals ◽  
Graph Theoretic ◽  
Security Officers ◽  
Detection Technology ◽  
Functional Brain Network ◽  
New Perspective ◽  
Security Inspection

In recent years, the workload of security offers has increased along with the requirement of anti-terrorism. In the paper, a series of evaluation index of security inspection based on the EEG signals of the security officers were proposed to improve the accuracy of dangerous instances detection and decrease the workload of the officers. We performed an experiment to record the EEG data of security officers when they were watching the picture with or without the dangerous item in the uncovered and obscured scenes. Brain network analysis based on graph theory was applied to generate the indexes from the EEG induced by the parcel picture of security inspection, and is a new perspective on the classification of the parcel composition. The paper studied the low-frequency, multi-channel experts EEG signals, calculated the phase locking value (PLV) between every two channels to construct the topological functional brain network (FBN). The appropriate binary FBNs were obtained by setting the thresholds, and then the complex brain network parameters were estimated by the graph-theoretic methods, which were used for classification with 10-fold cross-validation and the average accuracy was 83.3[Formula: see text][Formula: see text][Formula: see text]97.78%. The method was effectively applied to the substance classification and would further improve the recognition accuracy of the target by combining this method with the existing detection technology.

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