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.

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.

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 Graph Theory Analysis of Functional Brain Networks and Mobility Disability in Older Adults

2014 ◽  
Vol 69 (11) ◽  
pp. 1399-1406 ◽  
Author(s):  
C. E. Hugenschmidt ◽  
J. H. Burdette ◽  
A. R. Morgan ◽  
J. D. Williamson ◽  
S. B. Kritchevsky ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Brain Networks ◽  
Theory Analysis ◽  
Mobility Disability ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Graph Theory Analysis

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 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 The older adult brain is less modular, more integrated and less efficient at rest: A systematic review of large-scale resting-state functional brain networks across the adult lifespan.

2021 ◽  
Author(s):  
Hamish Deery ◽  
Rober Di Paolo ◽  
Chris Moran ◽  
Gary F. Egan ◽  
Sharna Jamadar
Keyword(s):  
Older Adults ◽  
Age Differences ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Adult Brain ◽  
Functional Brain ◽  
Adult Lifespan ◽  
Age Related ◽  
Functional Brain Networks

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2,938 records, 144 studies were included. Results on 11 network measures were summarised and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function. Higher-order networks reliably showed age differences, whereas basic processing and control networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for increased hemispheric lateralisation at rest, reduced BOLD signal variability within-subjects and altered patterns and speed of dynamic connectivity. Research on connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

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 Spontaneous Activity in Primary Visual Cortex Relates to Visual Creativity

2021 ◽  
Vol 15 ◽  
Author(s):  
Yibo Wang ◽  
Junchao Li ◽  
Zengjian Wang ◽  
Bishan Liang ◽  
Bingqing Jiao ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Brain Networks ◽  
Critical Time ◽  
Functional Brain ◽  
Time Points ◽  
Functional Brain Networks ◽  
The Brain

Cognitive and neural processes underlying visual creativity have attracted substantial attention. The current research uses a critical time point analysis (CTPA) to examine how spontaneous activity in the primary visual area (PVA) is related to visual creativity. We acquired the functional magnetic resonance imaging (fMRI) data of 16 participants at the resting state and during performing a visual creative synthesis task. According to the CTPA, we then classified spontaneous activity in the PVA into critical time points (CTPs), which reflect the most useful and important functional meaning of the entire resting-state condition, and the remaining time points (RTPs). We constructed functional brain networks based on the brain activity at two different time points and then subsequently based on the brain activity at the task state in a separate manner. We explore the relationship between resting-state and task-fMRI (T-fMRI) functional brain networks. Our results found that: (1) the pattern of spontaneous activity in the PVA may associate with mental imagery, which plays an important role in visual creativity; (2) in comparison with the RTPs-based brain network, the CTP-network showed an increase in global efficiency and a decrease in local efficiency; (3) the regional integrated properties of the CTP-network could predict the integrated properties of the creative-network while the RTP-network could not. Thus, our findings indicated that spontaneous activity in the PVA at CTPs was associated with a visual creative task-evoked brain response. Our findings may provide an insight into how the visual cortex is related to visual creativity.

scholarly journals Graph analysis of functional brain networks: practical issues in translational neuroscience

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130521 ◽  
Author(s):  
Fabrizio De Vico Fallani ◽  
Jonas Richiardi ◽  
Mario Chavez ◽  
Sophie Achard
Keyword(s):  
Brain Networks ◽  
Brain Network ◽  
Graph Analysis ◽  
Translational Neuroscience ◽  
Functional Brain ◽  
Functional Perspective ◽  
Brain Signals ◽  
Functional Brain Networks ◽  
The One ◽  
The Brain

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

scholarly journals Hyperbolic disc embedding of functional human brain connectomes using resting state fMRI

2021 ◽  
Author(s):  
Wonseok Whi ◽  
Seunggyun Ha ◽  
Hyejin Kang ◽  
Dong Soo Lee
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Resting State Fmri ◽  
Hyperbolic Distance ◽  
Functional Brain ◽  
Scale Free ◽  
Functional Brain Networks ◽  
Individual Scale ◽  
The Brain

The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging (rs-fMRI), we constructed a scale-free binary graph for each subject, using internodal time-series correlation of regions-of-interest (ROIs) as a proximity measure. The resulted network could be embedded onto manifolds of various curvature and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Using a popularity-similarity optimization model (PSOM) on the hyperbolic plane, we reduced the dimension of the network into 2-D hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the PSOM discs. Each individual PSOM disc revealed decentralized nature of information flow and anatomic relevance. Using the hyperbolic distance on the PSOM disc, we could detect the anomaly of network in autistic spectrum disorder (ASD) subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale.

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