scholarly journals Removing zero-lag functional connections can alter EEG-source space networks at rest

2019 ◽  
Author(s):  
Jennifer Rizkallah ◽  
Hassan Amoud ◽  
Matteo Fraschini ◽  
Fabrice Wendling ◽  
Mahmoud Hassan
Keyword(s):  
Phase Locking ◽  
Real Data ◽  
Phase Lag ◽  
Network Characteristics ◽  
Space Resolution ◽  
Functional Connections ◽  
Space Networks ◽  
Time Space ◽  
Human Connectome Project ◽  
Phase Locking Value

AbstractElectro-encephalography (EEG) source connectivity is an emerging approach to estimate brain networks with high time/space resolution. Here, we aim to evaluate the effect of different functional connectivity (FC) methods on the EEG-source space networks at rest. The two main families of FC methods tested are: i) the FC methods that do not remove the zero-lag connectivity including the Phase Locking Value (PLV) and the Amplitude Envelope Correlation (AEC) and ii) the FC methods that remove the zero-lag connections such as the Phase Lag Index (PLI) and orthogonalisation approach combined with PLV (PLVorth) and AEC (AECorth). Methods are evaluated on resting state dense-EEG signals recorded from 20 healthy participants. Networks obtained by each FC method are compared with fMRI networks at rest (from the Human Connectome Project -HCP-, N=487). Results show low correlations for all the FC methods, however PLV and AEC networks are significantly correlated with fMRI networks (ρ = 0.12, p = 1.93×10−8 and ρ = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for each EEG frequency bands and for different FC matrices threshold. Furthermore, the effect of electrode density was also tested using four EEG montages (dense-EEG 256 electrodes, 128, 64 and 32 electrodes). Results show no significant differences between the four EEG montages in terms of correlations with the fMRI networks. Our main message here is to be careful when selecting the FC methods and mainly those that remove the zero-lag connections as they can affect the network characteristics. More comparative studies (based on simulation and real data) are still needed in order to make EEG source connectivity a mature technique to address questions in cognitive and clinical neuroscience.

Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity

2021 ◽  
Author(s):  
Naishi Feng ◽  
Fo Hu ◽  
Hong Wang ◽  
Bin Zhou
Keyword(s):  
Functional Connectivity ◽  
Degrees Of Freedom ◽  
Phase Locking ◽  
Phase Lag ◽  
Neural Signals ◽  
Convolutional Network ◽  
Synchronization Likelihood ◽  
Motor Intention ◽  
External Equipment ◽  
Phase Locking Value

Decoding brain intention from noninvasively measured neural signals has recently been a hot topic in brain-computer interface (BCI). The motor commands about the movements of fine parts can increase the degrees of freedom under control and be applied to external equipment without stimulus. In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on functional connectivity was proposed to decode the motor intention of four fine parts movements (shoulder, elbow, wrist, hand). First, event-related desynchronization was analyzed to reveal the differences between the four classes. Second, functional connectivity was constructed by using synchronization likelihood (SL), phase-locking value (PLV), H index (H), mutual information (MI), and weighted phase-lag index (WPLI) to acquire the electrode pairs with a difference. Subsequently, a GCN and convolutional neural networks (CNN) were performed based on functional topological structures and time points, respectively. The results demonstrated that the proposed method achieved a decoding accuracy of up to 92.81% in the four-class task. Besides, the combination of GCN and functional connectivity can promote the development of BCI.

scholarly journals Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features

2022 ◽  
Vol 15 ◽  
Author(s):  
Zhaobo Li ◽  
Xinzui Wang ◽  
Weidong Shen ◽  
Shiming Yang ◽  
David Y. Zhao ◽  
...  
Keyword(s):  
Resting State ◽  
Phase Locking ◽  
Pearson Correlation ◽  
Computing Time ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Phase Lag ◽  
Eeg Signals ◽  
Time Frequency ◽  
Phase Locking Value

Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in electroencephalography (EEG) signals can be used as the biomarkers for an efficient and fast diagnosis method for chronic tinnitus.Methods: In this study, the resting-state EEG signals of tinnitus patients with different tinnitus locations were recorded. Four connectivity features [including the Phase-locking value (PLV), Phase lag index (PLI), Pearson correlation coefficient (PCC), and Transfer entropy (TE)] and two time-frequency domain features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources.Results: Classification accuracy was highest when the SVM algorithm or the MLP algorithm was applied to the PCC feature sets, achieving final average classification accuracies of 99.42 or 99.1%, respectively. And based on the PLV feature, the classification result was also particularly good. And MLP ran the fastest, with an average computing time of only 4.2 s, which was more suitable than other methods when a real-time diagnosis was required.Conclusion: Connectivity features of the resting-state EEG signals could characterize the differentiation of tinnitus location. The connectivity features (PCC and PLV) were more suitable as the biomarkers for the objective diagnosing of tinnitus. And the results were helpful for clinicians in the initial diagnosis of tinnitus.

scholarly journals High-accuracy individual identification using a “thin slice” of the functional connectome

2019 ◽  
Vol 3 (2) ◽  
pp. 363-383 ◽  
Author(s):  
Lisa Byrge ◽  
Daniel P. Kennedy
Keyword(s):  
High Accuracy ◽  
Individual Identification ◽  
Brain Functioning ◽  
Replication Sample ◽  
Thin Slice ◽  
Functional Connections ◽  
Random Samples ◽  
Human Connectome Project ◽  
Independent Replication

Connectome fingerprinting—a method that uses many thousands of functional connections in aggregate to identify individuals—holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance—how many and which functional connections are necessary and/or sufficient for high accuracy—will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small “thin slice” of the connectome—as few as 40 out of 64,620 functional connections—was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain.

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 Highway Passenger Transport Based Express Parcel Service Network Design: Model and Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Yuan Jiang ◽  
Baofeng Sun ◽  
Gendao Li ◽  
Zhibin Lin ◽  
Changxu Zheng ◽  
...  
Keyword(s):  
Network Design ◽  
Network Flow ◽  
Programming Model ◽  
Real Data ◽  
Path Selection ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Passenger Transport ◽  
Service Network ◽  
Time Space

Highway passenger transport based express parcel service (HPTB-EPS) is an emerging business that uses unutilised room of coach trunk to ship parcels between major cities. While it is reaping more and more express market, the managers are facing difficult decisions to design the service network. This paper investigates the HPTB-EPS network design problem and analyses the time-space characteristics of such network. A mixed-integer programming model is formulated integrating the service decision, frequency, and network flow distribution. To solve the model, a decomposition-based heuristic algorithm is designed by decomposing the problem as three steps: construction of service network, service path selection, and distribution of network flow. Numerical experiment using real data from our partner company demonstrates the effectiveness of our model and algorithm. We found that our solution could reduce the total cost by up to 16.3% compared to the carrier’s solution. The sensitivity analysis demonstrates the robustness and flexibility of the solutions of the model.

Spreading Activation Connectivity Based Approach to Network Clustering

2018 ◽  
pp. 207-219
Author(s):  
Alexander Troussov ◽  
Sergey Maruev ◽  
Sergey Vinogradov ◽  
Mikhail Zhizhin
Keyword(s):  
Big Data ◽  
Social Network ◽  
Social Systems ◽  
Network Models ◽  
Real Data ◽  
Spreading Activation ◽  
Network Clustering ◽  
Clustering Method ◽  
Network Characteristics ◽  
Structure Detection

Techno-social systems generate data, which are rather different, than data, traditionally studied in social network analysis and other fields. In massive social networks agents simultaneously participate in several contexts, in different communities. Network models of many real data from techno-social systems reflect various dimensionalities and rationales of actor's actions and interactions. The data are inherently multidimensional, where “everything is deeply intertwingled”. The multidimensional nature of Big Data and the emergence of typical network characteristics in Big Data, makes it reasonable to address the challenges of structure detection in network models, including a) development of novel methods for local overlapping clustering with outliers, b) with near linear performance, c) preferably combined with the computation of the structural importance of nodes. In this chapter the spreading connectivity based clustering method is introduced. The viability of the approach and its advantages are demonstrated on the data from the largest European social network VK.

scholarly journals Frequency-dependent circuits anchored in the dorsal and ventral left anterior insula

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yifeng Wang ◽  
Qijun Zou ◽  
Yujia Ao ◽  
Yang Liu ◽  
Yujie Ouyang ◽  
...  
Keyword(s):  
Functional Organization ◽  
Functional Integration ◽  
Anterior Insula ◽  
Frequency Effect ◽  
Frequency Dependent ◽  
Functional Connections ◽  
Human Connectome Project ◽  
Cognitive Neurosciences ◽  
The Right

Abstract The hub role of the right anterior insula (AI) has been emphasized in cognitive neurosciences and been demonstrated to be frequency-dependently organized. However, the functional organization of left AI (LAI) has not been systematically investigated. Here we used 100 unrelated datasets from the Human Connectome Project to study the frequency-dependent organization of LAI along slow 6 to slow 1 bands. The broadband functional connectivity of LAI was similar to previous findings. In slow 6-slow 3 bands, both dorsal and ventral seeds in LAI were correlated to the salience network (SN) and language network (LN) and anti-correlated to the default mode network (DMN). However, these seeds were only correlated to the LAI in slow 2-slow 1 bands. These findings indicate that broadband and narrow band functional connections reflect different functional organizations of the LAI. Furthermore, the dorsal seed had a stronger connection with the LN and anti-correlation with DMN while the ventral seed had a stronger connection within the SN in slow 6-slow 3 bands. In slow 2-slow 1 bands, both seeds had stronger connections with themselves. These observations indicate distinctive functional organizations for the two parts of LAI. Significant frequency effect and frequency by seed interaction were also found, suggesting different frequency characteristics of these two seeds. The functional integration and functional segregation of LDAI and LVAI were further supported by their cognitive associations. The frequency- and seed-dependent functional organizations of LAI may enlighten future clinical and cognitive investigations.

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