scholarly journals Alterations in brain network organization in adults with obesity as compared to healthy-weight individuals and seniors

2019 ◽  
Author(s):  
J. Ottino-González ◽  
H.C. Baggio ◽  
M.A. Jurado ◽  
B. Segura ◽  
X. Caldú ◽  
...  
Keyword(s):  
Small World ◽  
Brain Network ◽  
Premature Aging ◽  
Clustering Coefficient ◽  
Healthy Weight ◽  
Degree Relative ◽  
Characteristic Path Length ◽  
Centrality Metrics ◽  
Network Composition ◽  
Unhealthy Weight

AbstractLife expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting biological deregulations that might compromise the normal course of aging. The aim of the current study was to test whether the network composition of young adults with obesity would show signs of premature aging. To this end, subjects with obesity (N = 30, mean age 32.8 ± 5.68), healthy-weight controls (N = 33, mean age 30.9 ± 6.24) as well as non-demented seniors (N = 30, mean age 67.1 ± 6.65) all underwent a resting-state MRI acquisition. Functional connectivity was studied by means of graph-theory measurements (i.e., small-world index, clustering coefficient, characteristic path length, and mean degree). Contrary to what expected, obesity in adults was related to disruptions in small-world properties driven by increases in network segregation (i.e., clustering coefficient) as compared to elders. Also, this group showed alterations in global and regional centrality metrics (i.e., degree) relative to controls and seniors. Despite not mimicking what was here shown by seniors, the topological organization linked to an obesity status may represent a flaw for cognitive functions depending on the rapid combination between different modular communities.

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.

scholarly journals High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1031 ◽  
Author(s):  
Fabio La Foresta ◽  
Francesco Carlo Morabito ◽  
Silvia Marino ◽  
Serena Dattola
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small World ◽  
Brain Network ◽  
High Density ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Brain Functions ◽  
Network Parameters ◽  
Brain Functional Connectivity

Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network.

scholarly journals Altered small-world functional network topology in patients with optic neuritis: A resting- state fMRI study

2020 ◽  
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

AbstractPurposeThis 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.MethodsA total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled at the First Affiliated Hospital of Nanchang University 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.ResultsIn 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, cerebellum_Crus1_L, and left cerebellum_Crus6_L compared to the control group (P < 0.05).ConclusionThe brain network in ON has a small-world attributes but shows reduced and abnormal connectivity compared to normal subjects. These findings provide a further insight into the neural pathogenesis of ON and reveal specific fMRI findings that can serve as diagnostic and prognostic indices.

scholarly journals Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yi Liang ◽  
Chunli Chen ◽  
Fali Li ◽  
Dezhong Yao ◽  
Peng Xu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Local Area ◽  
Brain Network ◽  
Fuzzy Entropy ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Functional Brain Network ◽  
Before And After

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.

SYNCHRONIZATION IN THE FAMILY OF CHAOTIC SMALL-WORLD NETWORKS

2008 ◽  
Vol 19 (01) ◽  
pp. 111-123 ◽  
Author(s):  
LIANGMING HE ◽  
DUANWEN SHI
Keyword(s):  
Chaotic System ◽  
Average Distance ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Characteristic Path Length ◽  
Unified Chaotic System ◽  
The Family ◽  
Regular Networks ◽  
High Degree

In this paper we investigate by computer simulation the synchronizability of the family of small-world networks, which consists of identical chaotic units, such as the Lorenz chaotic system, the Chen chaotic system, Lü chaotic system, and the unified chaotic system (unit). It is shown that for weak coupling, synchronization clusters emerge in the networks whose disorder probabilities p are large but do not emerge in the networks whose disorder probabilities p are small; while for strong coupling under which the regular networks do not exhibit synchronization, all dynamical nodes, behaving as in the random networks, mutually synchronize in the networks which own very small disorder probability p and have both high degree of clustering and small average distance. Based on the concepts of clustering coefficient C(p), characteristic path length L(p) and global efficiency E(G), these phenomena are discussed briefly.

OPTIMAL INTERCONNECTION OF AD HOC INJECTION NETWORKS

2008 ◽  
Vol 09 (03) ◽  
pp. 277-297 ◽  
Author(s):  
GREGOIRE DANOY ◽  
ENRIQUE ALBA ◽  
PASCAL BOUVRY
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Small World ◽  
Access Point ◽  
Clustering Coefficient ◽  
Mobility Patterns ◽  
Characteristic Path Length ◽  
Network Properties ◽  
Hoc Networks ◽  
Topology Control Problem

Multi-hop ad hoc networks allow establishing local groups of communicating devices in a self-organizing way. However, when considering realistic mobility patterns, such networks most often get divided in a set of disjoint partitions. This presence of partitions is an obstacle to communication within these networks. Ad hoc networks are generally composed of devices capable of communicating in a geographical neighborhood for free (e.g. using Wi-Fi or Bluetooth). In most cases a communication infrastructure is available. It can be a set of access point as well as a GSM/UMTS network. The use of such an infrastructure is billed, but it permits to interconnect distant nodes, through what we call “bypass links”. The objective of our work is to optimize the placement of these long-range links. To this end we rely on small-world network properties, which consist in a high clustering coefficient and a low characteristic path length. In this article we investigate the use of three genetic algorithms (generational, steady-state, and cooperative coevolutionary) to optimize three instances of this topology control problem and present initial evidence of their capacity to solve it.

A NOVEL SMALL-WORLD NETWORK MODEL: KEEPING CONNECTIVITY WITHOUT ADDING EDGES

2008 ◽  
Vol 22 (29) ◽  
pp. 5229-5234 ◽  
Author(s):  
XUHUA YANG ◽  
BO WANG ◽  
WANLIANG WANG ◽  
YOUXIAN SUN
Keyword(s):  
Network Model ◽  
Regular Ring ◽  
Nearest Neighbor ◽  
Small World ◽  
Clustering Coefficient ◽  
The Novel ◽  
Small World Network ◽  
Characteristic Path Length ◽  
Large N ◽  
Novel Model

Considering the problems of potentially generating a disconnected network in the WS small-world network model [Watts and Strogatz, Nature393, 440 (1998)] and of adding edges in the NW small-world network model [Newman and Watts, Phys. Lett. A263, 341 (1999)], we propose a novel small-world network model. First, generate a regular ring lattice of N vertices. Second, randomly rewire each edge of the lattice with probability p. During the random rewiring procedure, keep the edges between the two nearest neighbor vertices, namely, always keep a connected ring. This model need not add edges and can maintain connectivity of the network at all times in the random rewiring procedure. Simulation results show that the novel model has the typical small-world properties which are small characteristic path length and high clustering coefficient. For large N, the model is approximately equal to the WS model. For large N and small p, the model is approximately equal to the WS model or the NW model.

scholarly journals Hemispheric Asymmetry of Functional Brain Networks under Different Emotions Using EEG Data

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 939
Author(s):  
Rui Cao ◽  
Huiyu Shi ◽  
Xin Wang ◽  
Shoujun Huo ◽  
Yan Hao ◽  
...  
Keyword(s):  
Hemispheric Asymmetry ◽  
Right Hemisphere ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Phase Lag ◽  
Characteristic Path Length ◽  
High Arousal ◽  
Eeg Data ◽  
The Right

Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants watched different emotional videos. According to the videos’ emotional categories, the data were divided into four categories: high arousal high valence (HAHV), low arousal high valence (LAHV), low arousal low valence (LALV) and high arousal low valence (HALV). The phase lag index as a connectivity index was calculated in theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz) and gamma (31–45 Hz) bands. Hemispheric networks were constructed for each trial, and graph theory was applied to quantify the hemispheric networks’ topological properties. Statistical analyses showed significant topological differences in the gamma band. The left hemispheric network showed significantly higher clustering coefficient (Cp), global efficiency (Eg) and local efficiency (Eloc) and lower characteristic path length (Lp) under HAHV emotion. The right hemispheric network showed significantly higher Cp and Eloc and lower Lp under HALV emotion. The results showed that the left hemisphere was dominant for HAHV emotion, while the right hemisphere was dominant for HALV emotion. The research revealed the relationship between emotion and hemispheric asymmetry from the perspective of brain networks.

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.

IDENTIFYING NEURAL NETWORK TOPOLOGIES THAT FOSTER DYNAMICAL COMPLEXITY

2013 ◽  
Vol 16 (02n03) ◽  
pp. 1350032 ◽  
Author(s):  
LARRY S. YAEGER
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Complex Dynamics ◽  
Small World ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Physical Constraints ◽  
Network Function ◽  
Network Topologies ◽  
The Relationship

We use an ecosystem simulator capable of evolving arbitrary neural network topologies to explore the relationship between an information theoretic measure of the complexity of neural dynamics and several graph theoretical metrics calculated for the underlying network topologies. Evolutionary trends confirm and extend previous results demonstrating an evolutionary selection for complexity and small-world network properties during periods of behavioral adaptation. The resultant mapping of the space of network topologies occupied by the most complex networks yields new insights into the relationship between network structure and function. The highest complexity networks are found within limited numerical ranges of clustering coefficient, characteristic path length, small-world index, and global efficiency. The widths of these ranges vary from quite narrow to modest, and provide a guide to the most productive regions of the space of neural topologies in which to search for complexity. Our demonstration that evolution selects for complex dynamics and small-world networks helps explain biological evidence for these trends and provides evidence for selection of these characteristics based purely on network function—with no physical constraints on network structure—thus suggesting that functional and structural evolutionary pressures cooperate to produce brains optimized for adaptation to a complex, variable world.

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