scholarly journals Spontaneous brain network activity: Analysis of its temporal complexity

2017 ◽  
Vol 1 (2) ◽  
pp. 100-115 ◽  
Author(s):  
Mangor Pedersen ◽  
Amir Omidvarnia ◽  
Jennifer M. Walz ◽  
Andrew Zalesky ◽  
Graeme D. Jackson
Keyword(s):  
Brain Network ◽  
Clustering Coefficient ◽  
Network Activity ◽  
Functional Magnetic Resonance ◽  
High Entropy ◽  
Temporal Complexity ◽  
Independent Replication ◽  
Network Metrics ◽  
The Brain ◽  
Complex Entropy

The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy.

scholarly journals Energy constraints on brain network formation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kosuke Takagi
Keyword(s):  
Network Formation ◽  
Cost Minimization ◽  
Brain Network ◽  
Network Activity ◽  
Rich Functionality ◽  
Energy Constraints ◽  
Dependent Form ◽  
Statistical Similarity ◽  
Simple Ratio ◽  
The Brain

AbstractEnergy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

scholarly journals BrainNetVis: An Open-Access Tool to Effectively Quantify and Visualize Brain Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Eleni G. Christodoulou ◽  
Vangelis Sakkalis ◽  
Vassilis Tsiaras ◽  
Ioannis G. Tollis
Keyword(s):  
Open Access ◽  
Brain Network ◽  
Global Network ◽  
Control Group ◽  
Network Modelling ◽  
Network Information ◽  
Network Measures ◽  
Graph Metrics ◽  
Network Metrics ◽  
The Brain

This paper presents BrainNetVis, a tool which serves brain network modelling and visualization, by providing both quantitative and qualitative network measures of brain interconnectivity. It emphasizes the needs that led to the creation of this tool by presenting similar works in the field and by describing how our tool contributes to the existing scenery. It also describes the methods used for the calculation of the graph metrics (global network metrics and vertex metrics), which carry the brain network information. To make the methods clear and understandable, we use an exemplar dataset throughout the paper, on which the calculations and the visualizations are performed. This dataset consists of an alcoholic and a control group of subjects.

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.

Analysis of the Brain Network Activity of Left- and Right-Handed Persons Performing Imaginary Hand Motions

2017 ◽  
Vol 7 (7) ◽  
pp. 1594-1601
Author(s):  
Juan Li ◽  
Li Zhang ◽  
Hao Meng ◽  
Ping Li ◽  
Guozghong Liu
Keyword(s):  
Brain Network ◽  
Network Activity ◽  
Left And Right ◽  
The Brain

scholarly journals Writing affects the brain network of reading in Chinese: A functional magnetic resonance imaging study

2012 ◽  
Vol 34 (7) ◽  
pp. 1670-1684 ◽  
Author(s):  
Fan Cao ◽  
Marianne Vu ◽  
Derek Ho Lung Chan ◽  
Jason M. Lawrence ◽  
Lindsay N. Harris ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Imaging Study ◽  
Brain Network ◽  
Magnetic Resonance Imaging Study ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Brain

scholarly journals A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hao Liu ◽  
Haimeng Hu ◽  
Huiying Wang ◽  
Jiahui Han ◽  
Yunfei Li ◽  
...  
Keyword(s):  
Regression Model ◽  
Pearson Correlation ◽  
Brain Networks ◽  
Small World ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Elderly Subjects ◽  
Small World Network ◽  
L1 Norm ◽  
The Brain

Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.

scholarly journals Abnormal Topological Network in Parkinson’s Disease With Impulse Control Disorders: A Resting-State Functional Magnetic Resonance Imaging Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiaopeng Zhu ◽  
Langsha Liu ◽  
Yan Xiao ◽  
Fan Li ◽  
Yongkai Huang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Impulse Control ◽  
Small World ◽  
Brain Network ◽  
Impulse Control Disorders ◽  
Clustering Coefficient ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

In recent years, neuroimaging evidence shows that the brains of Parkinson disease (PD) with impulse control disorders (ICDs) patients have functional disconnection changes. However, so far, it is still unclear whether the topological organization is damaged in PD patients with ICD. In this study, we aimed to explore the functional brain network in 18 patients with PD with ICDs (PD-ICD) and 18 patients with PD without ICDs (PD-nICD) by using functional magnetic resonance imaging and graph theory approach. We found that the PD-ICD patients had increased clustering coefficient and characteristic path length, while decreased small-world index compared with PD-nICD patients. Furthermore, we explored the hypothesis whether the abnormality of the small-world network parameters of PD-ICD patients is accompanied by the change of nodal centrality. As we hypothesized, the nodal centralities of the default mode network, control network, and dorsal attention network were found to be significantly damaged in the PD-ICD group compared with the PD-nICD group. Our study provides more evidence for PD-ICD patients’ brain network abnormalities from the perspective of information exchange, which may be the underlying pathophysiological basis of brain abnormalities in PD-ICD patients.

scholarly journals Brain-wide electrical dynamics encode an appetitive socioemotional state

2020 ◽  
Author(s):  
Stephen D. Mague ◽  
Austin Talbot ◽  
Cameron Blount ◽  
Lara J. Duffney ◽  
Kathryn K. Walder-Christensen ◽  
...  
Keyword(s):  
Social Preference ◽  
Brain Regions ◽  
Brain Network ◽  
Network Activity ◽  
Brain State ◽  
Genetic Mouse Model ◽  
Spatially Distributed ◽  
Subcortical Regions ◽  
Complex Social Behavior ◽  
The Brain

AbstractMany cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a ‘high confidence’ genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

scholarly journals Altered Small-World Functional Network Topology in Patients with Optic Neuritis: A Resting-State fMRI Study

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ke Song ◽  
Juan Li ◽  
Yuanqiang Zhu ◽  
Fang Ren ◽  
Lingcan Cao ◽  
...  
Keyword(s):  
Resting State ◽  
Path Length ◽  
Small World ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Control Group ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Brain Functional Connectivity ◽  
The Brain

Aim. This study investigated changes in small-world topology and brain functional connectivity in patients with optic neuritis (ON) by resting-state functional magnetic resonance imaging (rs-fMRI) and based on graph theory. Methods. A total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled and underwent rs-fMRI. Data were preprocessed and the brain was divided into 116 regions of interest. Small-world network parameters and area under the integral curve (AUC) were calculated from pairwise brain interval correlation coefficients. Differences in brain network parameter AUCs between the 2 groups were evaluated with the independent sample t -test, and changes in brain connection strength between ON patients and control subjects were assessed by network-based statistical analysis. Results. In the sparsity range from 0.08 to 0.48, both groups exhibited small-world attributes. Compared to the control group, global network efficiency, normalized clustering coefficient, and small-world value were higher whereas the clustering coefficient value was lower in ON patients. There were no differences in characteristic path length, local network efficiency, and normalized characteristic path length between groups. In addition, ON patients had lower brain functional connectivity strength among the rolandic operculum, medial superior frontal gyrus, insula, median cingulate and paracingulate gyri, amygdala, superior parietal gyrus, inferior parietal gyrus, supramarginal gyrus, angular gyrus, lenticular nucleus, pallidum, superior temporal gyrus, and cerebellum compared to the control group ( P < 0.05 ). Conclusion. Patients with ON show typical “small world” topology that differed from that detected in HC brain networks. The brain network in ON has a small-world attribute but shows reduced and abnormal connectivity compared to normal subjects and likely causes symptoms of cognitive impairment.

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