scholarly journals Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

2019 ◽  
Author(s):  
Amir Hossein Ghaderi ◽  
Bianca R. Baltaretu ◽  
Masood Nemati Andevari ◽  
Vishal Bharmauria ◽  
Fuat Balci
Keyword(s):  
Graph Theory ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Spectral Graph Theory ◽  
Theory Approach ◽  
Topological Properties ◽  
Functional Brain ◽  
Spectral Graph ◽  
Functional Brain Networks

AbstractTo characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations.Author SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network. Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks. However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks). We found that SGT measures can clearly distinguish between distinct state-related brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of random-generated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

scholarly journals Auditory Verbal Hallucination-Specific Functional Brain Networks in Schizophrenia: A Study Based on Graph Theory

2021 ◽  
Author(s):  
Weiliang Yang ◽  
Yan li ◽  
Haiyan Cao ◽  
Wen Qin ◽  
Yongying Cheng ◽  
...  
Keyword(s):  
Graph Theory ◽  
Clinical Features ◽  
Brain Networks ◽  
Brain Network ◽  
Local Network ◽  
Topological Properties ◽  
Network Efficiency ◽  
Functional Brain ◽  
Auditory Verbal Hallucinations ◽  
Functional Brain Networks

Abstract Background Although mounting previous studies have characterized auditory verbal hallucinations (AVH) related brain network abnormalities in the patients with schizophrenia, AVH related brain network alterations based on graph theory was rarely reported. In addition, the relationship between the features of AVH related brain networks based on graph theory and clinical features of schizophrenia patients with AVH is unclear. Our study to explore associations among network metrics, and clinical features in schizophrenia patients with AVH. Method Thirty-one schizophrenia patients without AVH, 17 patients with AVH, and 31 healthy controls were examined by functional magnetic resonance imaging. Graph theory method was performed to analyze the topological properties of functional network in three groups. Results Our results showed that schizophrenia patients with AVH displayed decreased local network efficiency, clustering coefficients, and nodal efficiency of the right dorsolateral prefrontal cortex. Local network efficiency was positively correlated with AVH characteristics. Conclusion The topological properties of brain functional networks are disrupted in schizophrenia patients with AVH, suggesting a role of functional brain networks in the pathogenesis of AVH.

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

2021 ◽  
pp. 1-11
Author(s):  
Yi Liu ◽  
Zhuoyuan Li ◽  
Xueyan Jiang ◽  
Wenying Du ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Decline ◽  
Path Length ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Subjective Cognitive Decline ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p <  0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

scholarly journals Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

2021 ◽  
Vol 11 (8) ◽  
pp. 1066
Author(s):  
Han Li ◽  
Qizhong Zhang ◽  
Ziying Lin ◽  
Farong Gao
Keyword(s):  
Graph Theory ◽  
Brain Networks ◽  
Tensor Decomposition ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Dynamic Methods ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Elm Classifier

Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

scholarly journals ADHD symptoms are associated with the modular structure of intrinsic brain networks in a representative sample of healthy adults

2019 ◽  
Vol 3 (2) ◽  
pp. 567-588 ◽  
Author(s):  
Kirsten Hilger ◽  
Christian J. Fiebach
Keyword(s):  
Representative Sample ◽  
Information Transfer ◽  
Adult Population ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Adhd Symptoms ◽  
Functional Brain ◽  
Default Mode ◽  
Functional Brain Networks

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only 8 out of 291 participants scored in the clinical range. Subject-specific brain network graphs were modeled from functional MRI resting-state data and revealed significant associations between (nonclinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.

scholarly journals A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Li ◽  
Miao Wang ◽  
Yapeng Li ◽  
Yue Huang ◽  
Xi Chen
Keyword(s):  
Brain Networks ◽  
Brain Network ◽  
Support Vector ◽  
Elderly Adults ◽  
Topological Properties ◽  
Network Construction ◽  
Functional Brain ◽  
Classic Method ◽  
Age Related ◽  
Functional Brain Networks

The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the “intrinsic edges” to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV) method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

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