scholarly journals Meal replacement and functional connectivity in the brain network for appetite: connecting the dots

2015 ◽  
Vol 6 ◽  
Author(s):  
Tanya Zilberter
Keyword(s):  
Functional Connectivity ◽  
Brain Network ◽  
Meal Replacement ◽  
Connecting The Dots ◽  
The Brain

scholarly journals The brain network reflecting bodily self-consciousness: a functional connectivity study

2014 ◽  
Vol 9 (12) ◽  
pp. 1904-1913 ◽  
Author(s):  
Silvio Ionta ◽  
Roberto Martuzzi ◽  
Roy Salomon ◽  
Olaf Blanke
Keyword(s):  
Functional Connectivity ◽  
Brain Network ◽  
The Brain

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.

scholarly journals Abnormal EEG Complexity and Functional Connectivity of Brain in Patients with Acute Thalamic Ischemic Stroke

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Shuang Liu ◽  
Jie Guo ◽  
Jiayuan Meng ◽  
Zhijun Wang ◽  
Yang Yao ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Functional Connectivity ◽  
Healthy Subjects ◽  
Brain Network ◽  
Control Group ◽  
Stroke Patients ◽  
Cerebral Disease ◽  
Essential Information ◽  
Stroke Group ◽  
The Brain

Ischemic thalamus stroke has become a serious cardiovascular and cerebral disease in recent years. To date the existing researches mostly concentrated on the power spectral density (PSD) in several frequency bands. In this paper, we investigated the nonlinear features of EEG and brain functional connectivity in patients with acute thalamic ischemic stroke and healthy subjects. Electroencephalography (EEG) in resting condition with eyes closed was recorded for 12 stroke patients and 11 healthy subjects as control group. Lempel-Ziv complexity (LZC), Sample Entropy (SampEn), and brain network using partial directed coherence (PDC) were calculated for feature extraction. Results showed that patients had increased mean LZC and SampEn than the controls, which implied the stroke group has higher EEG complexity. For the brain network, the stroke group displayed a trend of weaker cortical connectivity, which suggests a functional impairment of information transmission in cortical connections in stroke patients. These findings suggest that nonlinear analysis and brain network could provide essential information for better understanding the brain dysfunction in the stroke and assisting monitoring or prognostication of stroke evolution.

scholarly journals Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

2020 ◽  
Author(s):  
Paul Triebkorn ◽  
Joelle Zimmermann ◽  
Leon Stefanovski ◽  
Dipanjan Roy ◽  
Ana Solodkin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Model Parameters ◽  
Network Modelling ◽  
Neural Populations ◽  
Global Coupling ◽  
The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

scholarly journals Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhibao Li ◽  
Chong Liu ◽  
Qiao Wang ◽  
Kun Liang ◽  
Chunlei Han ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Functional Connectivity ◽  
Frequency Band ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Frequency Bands ◽  
The Brain ◽  
Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

scholarly journals Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults

2020 ◽  
Vol 30 (12) ◽  
pp. 6206-6223
Author(s):  
Cheryl L Grady ◽  
Jenny R Rieck ◽  
Daniel Nichol ◽  
Douglas D Garrett
Keyword(s):  
Older Adults ◽  
Functional Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Whole Brain ◽  
The Face ◽  
Brain Correlates ◽  
Brain And Behavior ◽  
Face Stimuli ◽  
The Brain

Abstract Degrading face stimuli reduces face discrimination in both young and older adults, but the brain correlates of this decline in performance are not fully understood. We used functional magnetic resonance imaging to examine the effects of degraded face stimuli on face and nonface brain networks and tested whether these changes would predict the linear declines seen in performance. We found decreased activity in the face network (FN) and a decrease in the similarity of functional connectivity (FC) in the FN across conditions as degradation increased but no effect of age. FC in whole-brain networks also changed with increasing degradation, including increasing FC between the visual network and cognitive control networks. Older adults showed reduced modulation of this whole-brain FC pattern. The strongest predictors of within-participant decline in accuracy were changes in whole-brain network FC and FC similarity of the FN. There was no influence of age on these brain-behavior relations. These results suggest that a systems-level approach beyond the FN is required to understand the brain correlates of performance decline when faces are obscured with noise. In addition, the association between brain and behavior changes was maintained into older age, despite the dampened FC response to face degradation seen in older adults.

scholarly journals Brain Network Regional Synchrony Analysis in Deafness

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Xu ◽  
Chang-Dong Wang ◽  
Mao-Jin Liang ◽  
Yue-Xin Cai ◽  
Yi-Qing Zheng
Keyword(s):  
Functional Connectivity ◽  
Cochlear Implantation ◽  
Brain Network ◽  
Svm Classifier ◽  
Long Time ◽  
Major Treatment ◽  
Network Synchrony ◽  
Partition Method ◽  
The Brain ◽  
Regional Synchrony

Deafness, the most common auditory disease, has greatly affected people for a long time. The major treatment for deafness is cochlear implantation (CI). However, till today, there is still a lack of objective and precise indicator serving as evaluation of the effectiveness of the cochlear implantation. The goal of this EEG-based study is to effectively distinguish CI children from those prelingual deafened children without cochlear implantation. The proposed method is based on the functional connectivity analysis, which focuses on the brain network regional synchrony. Specifically, we compute the functional connectivity between each channel pair first. Then, we quantify the brain network synchrony among regions of interests (ROIs), where both intraregional synchrony and interregional synchrony are computed. And finally the synchrony values are concatenated to form the feature vector for the SVM classifier. What is more, we develop a new ROI partition method of 128-channel EEG recording system. That is, both the existing ROI partition method and the proposed ROI partition method are used in the experiments. Compared with the existing EEG signal classification methods, our proposed method has achieved significant improvements as large as 87.20% and 86.30% when the existing ROI partition method and the proposed ROI partition method are used, respectively. It further demonstrates that the new ROI partition method is comparable to the existing ROI partition method.

scholarly journals Comparing Community Detection Methods in Brain Functional Connectivity Networks

2020 ◽  
Author(s):  
Reddy Rani Vangimalla ◽  
Jaya Sreevalsan-Nair
Keyword(s):  
Functional Connectivity ◽  
Community Detection ◽  
Brain Network ◽  
Detection Methods ◽  
Modular Organization ◽  
Test Bed ◽  
Brain Functional Connectivity ◽  
Analytical Approaches ◽  
The Brain

AbstractBrain functional networks are essential for understanding functional connectome. Computing the temporal dependencies between the regions of brain activities of functional magnetic resonance imaging (fMRI) gives us the functional connectivity between the regions. The pairwise connectivities in matrix form correspond to the functional network (fNet), also referred to as a functional connectivity network (FCN). We start with analyzing a correlation matrix, which is an adjacency matrix of the FCN. In this work, we perform a case study of comparison of different analytical approaches in finding node-communities of the brain network. We use five different methods of community detection, out of which two methods are implemented on the network after filtering out the edges with weight below a predetermined threshold. We additionally compute and observe the following characteristics of the outcomes: (i) modularity of the communities, (ii) symmetrical node-partition between the left and right hemispheres of the brain, i.e., hemispheric symmetry, and (iii) hierarchical modular organization. Our contribution is in identifying an appropriate test-bed for comparison of outcomes of approaches using different semantics, such as network science, information theory, multivariate analysis, and data mining.

scholarly journals Computerized physical exercise improves the functional architecture of the brain in patients with Parkinson’s Disease: a network science resting-state EEG study

2020 ◽  
Author(s):  
Vasileios Rafail Xefteris ◽  
Charis Styliadis ◽  
Alexandra Anagnostopoulou ◽  
Panagiotis Kartsidis ◽  
Evangelos Paraskevopoulos ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Physical Exercise ◽  
Resting State ◽  
Network Science ◽  
Brain Network ◽  
Motor Execution ◽  
The Brain

AbstractPhysical exercise is an effective non-pharmaceutical treatment for Parkinson’s disease (PD) symptoms, both motor and non-motor. Despite the numerous reports on the neuroplastic role of physical exercise in patients with PD (PwPD), its effects have not been thoroughly explored via brain network science, which can provide a coherent framework for understanding brain functioning. We used resting-state EEG data to investigate the functional connectivity changes of the brain’s intrinsic cortical networks due to physical exercise. The brain activity of 14 PwPD before and after a ten-week protocol of computerized physical training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychometric and somatometric assessments. PwPD showed a significant reorganization of the post-training brain network along with increases in their physical capacity. Specifically, our results revealed significant adjustments in clustering, increased characteristic path length, and decreased global efficiency, in correlation to the improved physical capacity. Our results go beyond previous findings by indicating a transition to a reparative network architecture of enhanced connectivity. We present a meaningful relationship between network characteristics and motor execution capacity which support the use of motor treatment in tandem with medication. This trial is registered with ClinicalTrials.gov Identifier NCT04426903.Impact StatementThe effects of physical training (PT) on the neuroplasticity attributes of patients with Parkinson’s Disease (PwPD) have been well documented via neurophysiological evaluations. However, there is a knowledge gap on the role of training-induced neuroplasticity in whole-brain network organization. We investigated the PT effects on the brain network organization of 14 PwPD, using EEG and network indices coupled with psychosomatometric tests. We report evidence of reparative functional reorganization of the brain with more balanced integration and segregation abilities, in correlation to improved motor performance. The PD brain can repair and reestablish a better level of motor execution and control due to computer-empowered physical stimulation.

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