scholarly journals Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks

2017 ◽  
Vol 11 ◽  
Author(s):  
Anke Meyer-Bäse ◽  
Rodney G. Roberts ◽  
Ignacio A. Illan ◽  
Uwe Meyer-Bäse ◽  
Marc Lobbes ◽  
...  
Keyword(s):  
Graph Theory ◽  
Functional Connectivity ◽  
Brain Networks

22 Whole-brain functional connectivity based on the graph theory analysiisn Psychogenic Non-Epileptic Seizures (PNES)

2020 ◽  
Vol 91 (8) ◽  
pp. e17.1-e17
Author(s):  
M Arbabi ◽  
S Amiri ◽  
F Badragheh ◽  
MM Mirbagheri ◽  
AA Asadi-Pooya
Keyword(s):  
Graph Theory ◽  
Functional Connectivity ◽  
Brain Networks ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Control Subjects ◽  
Wide Range ◽  
Healthy Control ◽  
Left And Right ◽  
Brain Functional Connectivity

ObjectiveDespite being the subject of many studies over the past two decades, mechanisms underlying psychogenic non-epileptic seizures (PNES) are still poorly understood. We tried to address this issue by utilizing brain functional connectivity analysis to identify brain regions with abnormal activities in patients with PNES. In a case-control study, we performed graph based network analysis, a robust technique that determines the organization of brain connectivity and characterizes topological properties of the brain networks.MethodsTwelve individuals with PNES and twenty-one healthy control subjects were examined. Resting state functional magnetic resonance imaging (rsfMRI) was acquired. All subjects were asked to keep their eyes open during the scanning process. The rsfMRI analysis consisted of pre-processing, extracting the functional connectivity matrix (FCM) based on the AAL atlas, threshold for binary FCM, constructing a graph network from FCM and extracting graph features, and finally statistical analysis. For all cortical and subcortical regions of the AAL atlas, we calculated measures of ‘degree,’ which is one of the features of the graph theory. Results: Our results revealed that, as compared to the healthy control subjects, patients with PNES had a significantly lower degree in some brain regions including their left and right insula (INS), right Putamen (PUT), left and right Supramarginal gyrus (SMG), right Middle occipital gyrus (MOG), and left and right Rolandic operculum (ROL). In contrast, degree was significantly greater in two regions [i.e., right Caudate (CAU) and left Inferior frontal gyrus orbital part (ORBinf)] in patients with PNES compared to that in controls.ConclusionOur findings suggest that functional connectivity of several major brain regions are different in patients with PNES compared with that in healthy individuals. While there is hypoactivity in regions important in perception, motor control, self- awareness, and cognitive functioning (e.g., insula) and also movement regulation (e.g., putamen), there is hyperactivity in areas involved in feedback processing (i.e., using information from past experiences to influence future actions and decisions) (e.g., caudate) in patients with PNES. The observation that individuals with PNES suffer from a wide range of abnormal activities in functional connectivity of their brain networks is consistent with the fact that PNES occur in a heterogeneous patient population; no single mechanism or contributing factor could explain PNES in all patients.

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 Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain

2018 ◽  
Vol 29 (10) ◽  
pp. 4208-4222 ◽  
Author(s):  
Yuehua Xu ◽  
Miao Cao ◽  
Xuhong Liao ◽  
Mingrui Xia ◽  
Xindi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Network Architecture ◽  
Brain Networks ◽  
Individual Variability ◽  
Distance Constraint ◽  
Behavioral Differences ◽  
Sensorimotor Network ◽  
Middle Range ◽  
Brain Functional Connectivity

Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

scholarly journals Reorganization of brain networks in aging: a review of functional connectivity studies

2015 ◽  
Vol 6 ◽  
Author(s):  
Roser Sala-Llonch ◽  
David Bartrés-Faz ◽  
Carme Junqué
Keyword(s):  
Functional Connectivity ◽  
Brain Networks

scholarly journals Brain networks for visual creativity: a functional connectivity study of planning a visual artwork

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Nicola De Pisapia ◽  
Francesca Bacci ◽  
Danielle Parrott ◽  
David Melcher
Keyword(s):  
Functional Connectivity ◽  
Brain Networks

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.

Sign in / Sign up

Export Citation Format

Share Document