scholarly journals Monitoring Effective Connectivity in the Preterm Brain: A Graph Approach to Study Maturation

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
M. Lavanga ◽  
O. De Wel ◽  
A. Caicedo ◽  
K. Jansen ◽  
A. Dereymaeker ◽  
...  
Keyword(s):  
Graph Theory ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Effective Connectivity ◽  
Small World ◽  
Clustering Coefficient ◽  
Postmenstrual Age ◽  
Eeg Connectivity ◽  
Preterm Babies ◽  
Age Prediction

In recent years, functional connectivity in the developmental science received increasing attention. Although it has been reported that the anatomical connectivity in the preterm brain develops dramatically during the last months of pregnancy, little is known about how functional and effective connectivity change with maturation. The present study investigated how effective connectivity in premature infants evolves. To assess it, we use EEG measurements and graph-theory methodologies. We recorded data from 25 preterm babies, who underwent long-EEG monitoring at least twice during their stay in the NICU. The recordings took place from 27 weeks postmenstrual age (PMA) until 42 weeks PMA. Results showed that the EEG-connectivity, assessed using graph-theory indices, moved from a small-world network to a random one, since the clustering coefficient increases and the path length decreases. This shift can be due to the development of the thalamocortical connections and long-range cortical connections. Based on the network indices, we developed different age-prediction models. The best result showed that it is possible to predict the age of the infant with a root mean-squared error (MSE) equal to 2.11 weeks. These results are similar to the ones reported in the literature for age prediction in preterm babies.

scholarly journals Abnormal Low-Gamma Small-World Response After Visual Stimulation in Schizophrenia

2021 ◽  
Author(s):  
Lucas Galdino ◽  
Thiago Fernandes ◽  
Kerstin Erika Schmidt ◽  
Natanael Antonio dos Santos
Keyword(s):  
Graph Theory ◽  
Evoked Potentials ◽  
Visual Processing ◽  
Visual Stimulation ◽  
Network Connectivity ◽  
Small World ◽  
Sensory Stimulation ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Eeg Recordings

Abstract Schizophrenia can be described as a functional dysconnectivity syndrome that affects the brain’s circuits in a generalized way. Global disconnection in schizophrenia has been manifold described by applying graph theory and quantifying parameters of network connectivity. However, little is known about how sensory stimulation modulates networks in schizophrenia, such as small-worldness during visual processing. In order to address this question, we applied graph theory algorithms to EEG recordings and classified the functional network in the alpha (8–13 Hz) and low-gamma (36–55 Hz) bands of 13 patients with schizophrenia (SCZ) and 13 healthy controls (HC) during the presentation of a visual stimulus. We measured the amplitude of visual-evoked potentials and the number of nodes, edges, mean degree centrality, clustering coefficient, characteristic path length (L), and small-worldness (SW). As expected, patients presented smaller peak amplitudes of evoked-potentials than HC. Interestingly, in contrast to the controls, SCZ did not change their small worldness index during visual stimulation. This implies that schizophrenia-related dysconnectivity has an impact on the ability of the low-gamma network to react to new sensory input. These results provide evidence about a possible electrophysiological signature of the global deficits revealed by the application of graph theory onto the EEG in schizophrenia.

scholarly journals Neural Correlation of Faradarmani Consciousness Field Mind Mediation: A Comparative Functional Connectivity and Graph Analysis

2021 ◽  
Author(s):  
Mohammad Ali Taheri ◽  
Fatemeh Modarresi-Asem ◽  
Noushin Nabavi ◽  
Parisa Maftoun ◽  
Farid Semsarha
Keyword(s):  
Graph Theory ◽  
Functional Connectivity ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Temporal Lobes ◽  
Neural Correlation ◽  
Graph Theory Analysis ◽  
The Brain

The study of the brain networks using analysis of electroencephalography (EEG) data based on statistical dependencies (functional connectivity) and mathematical graph theory concepts is common in neuroscience and cognitive sciences for examinations of patient and healthy individuals. The Consciousness Fields according to Taheri theory and applications in the optimization of system under study have been investigated in various studies. In this study, we examine the results of working with Faradarmani Consciousness Field (FCF) in the brain of Faradarmangars. Faradarmangars are one of the necessary components in mind mediation of the function of Faradarmani Consciousness Fields according to Taheri. For this purpose, the functional and effective connectivity and the corresponding brain graphs of EEG from the brain of Faradarmangars is compared with that of non Faradarmangar groups during FCF connection. According to the results of the present study, the brain of the Faradarmangars shows significant decreased activity in delta (BA8), beta2 (BA4/6/8/9/10/11/32/44/47) and beta3 (in 34 of 52 BA) frequency bands mainly in frontal lobe and after that in parietal and temporal lobes in the comparison with the non Faradarmangars. Moreover, the functional and effective connectivity analysis in the frontal network shows dominant multiple decreased connectivity mainly in the case of beta3 frequency band in all parts of the frontal network. On the other hand, the graph theory analysis of the Faradarmangar brain shows an increase in the activity of the O2-T5-F4-F3-FP2-F8 areas and significant decrease in the characteristic path length and increases in global efficiency, clustering coefficient and transitivity. In conclusion, the unique higher graph function efficiency and the reduction in the brain activity and connectivity during the Faradarmani Consciousness Field mind mediation, shown the passive and detector like function of the human brain in this task.

scholarly journals Graph theoretical analysis based on EEG effective connectivity in ADHD children

2021 ◽  
Author(s):  
Mingkan Shen ◽  
Paul Wen ◽  
Yan Li ◽  
Bo Song
Keyword(s):  
Graph Theory ◽  
Effective Connectivity ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Eeg Signals ◽  
Phase Slope ◽  
Delta Band ◽  
Eeg Data ◽  
Adhd Children ◽  
The Mean

Abstract This paper reports a new method to identify the ADHD children using EEG signals and effective connectivity techniques. In this study, the original EEG data is pre-filtered and divided into Delta, Theta, Alpha and Beta bands. And then, the effective connectivity graphs are constructed by applying independent component analysis, multivariate regression model and phase slope index. The measures of clustering coefficient, nodal efficiency and degree centrality in graph theory are used to extract features from these graphs. Statistical analysis based on the standard error of the mean are employed to evaluate the graph theory measures in each frequency band. The results show a decreased average clustering coefficient in delta band for ADHD subjects. Also, in delta band, the ADHD subjects have increased nodal efficiency and degree centrality in left forehead part and decreased in forehead middle.

CELLULAR NEURAL NETWORKS CAN MIMIC SMALL-WORLD NETWORKS

1999 ◽  
Vol 09 (10) ◽  
pp. 2105-2126 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Coupling Strength ◽  
Initial Conditions ◽  
Computing Time ◽  
Small World ◽  
Cellular Neural Networks ◽  
Clustering Coefficient ◽  
High Survival ◽  
Game Of Life ◽  
Hole Detection

Small-world phenomenon can occur in coupled dynamical systems which are highly clustered at a local level and yet strongly coupled at the global level. We show that cellular neural networks (CNN's) can exhibit "small-world phenomenon". We generalize the "characteristic path length" from previous works on "small-world phenomenon" into a "characteristic coupling strength" for measuring the average coupling strength of the outputs of CNN's. We also provide a simplified algorithm for calculating the "characteristic coupling strength" with a reasonable amount of computing time. We define a "clustering coefficient" and show how it can be calculated by a horizontal "hole detection" CNN, followed by a vertical "hole detection" CNN. Evolutions of the game-of-life CNN with different initial conditions are used to illustrate the emergence of a "small-world phenomenon". Our results show that the well-known game-of-life CNN is not a small-world network. However, generalized CNN life games whose individuals have strong mobility and high survival rate can exhibit small-world phenomenon in a robust way. Our simulations confirm the conjecture that a population with a strong mobility is more likely to qualify as a small world. CNN games whose individuals have weak mobility can also exhibit a small-world phenomenon under a proper choice of initial conditions. However, the resulting small worlds depend strongly on the initial conditions, and are therefore not robust.

Preterm newborn adaptive responses to daily nursing, during NICU stay, associate with the neurodevelopment, two years later

2021 ◽  
Author(s):  
Stefano Bembich ◽  
Francesco Maria Risso ◽  
Nicoleta Stan ◽  
Domitilla Lamba ◽  
Carolina Banova ◽  
...  
Keyword(s):  
Preterm Newborn ◽  
Adaptive Responses ◽  
Very Preterm ◽  
Autonomic Motor ◽  
Postmenstrual Age ◽  
Preterm Babies ◽  
Positive Correlations ◽  
Spearman’S Correlation ◽  
Preterm Newborns ◽  
Bayley Scales

Objective: To evaluate if adaptive responses of very preterm newborns to NICU daily nursing, specifically bathing and weighing procedures, are associated with their neurodevelopment two years later. Study design: Twenty-six very preterm newborns, with a gestational age < 32 weeks, were enrolled. Infants' adaptive responses to daily nursing were evaluated, at 30-32-35 postmenstrual age (PMA) weeks, by an observational sheet. Neurodevelopment was assessed, at 24 months of corrected age, by the Bayley Scales of Infant and Toddler Development, third edition. Autonomic, motor, and self-regulatory responses to NICU nursing were analyzed, by Spearman's correlation coefficient and multivariate linear regression, with Bayley’s cognitive, language, and motor scales. Results: Significant (P < 0.05) positive correlations of self-regulatory and autonomic responses to nursing with all Bailey’s scales were found at 30- and 32-weeks PMA. At 35 weeks PMA, only self-regulatory responses had significant positive correlations with all Bailey’s scales. When adjusted for birth weight and sex, the significant associations were confirmed only at 30- and 32-weeks PMA. Conclusion: Very preterm newborn adaptive responses to NICU daily nursing reveal to be positively related to forthcoming neurodevelopment two years later, as early as the 30th week PMA. Helping preterm babies to adapt to daily NICU nursing may promote their future neurobeahaviour.

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.

SCALE-FREE AND SMALL-WORLD PROPERTIES OF A SPECIAL HIERARCHICAL NETWORK

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950010
Author(s):  
DAOHUA WANG ◽  
YUMEI XUE ◽  
QIAN ZHANG ◽  
MIN NIU
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Hierarchical Network ◽  
Scale Free

Many real systems behave similarly with scale-free and small-world structures. In this paper, we generate a special hierarchical network and based on the particular construction of the graph, we aim to present a study on some properties, such as the clustering coefficient, average path length and degree distribution of it, which shows the scale-free and small-world effects of this network.

scholarly journals A study on small-world characteristics of Chinaäó»s high-speed railway networks

2016 ◽  
Author(s):  
Xin Yuan ◽  
Guo Liu ◽  
Kun Hui Ye
Keyword(s):  
High Speed ◽  
Small World ◽  
Clustering Coefficient ◽  
High Speed Rail ◽  
Attribute Data ◽  
The Social ◽  
The Difference ◽  
Global And Local ◽  
The Relationship ◽  
Space Method

The small-world model provides a useful perspective and method to study the topological structure and intrinsic characteristics of high-speed rail networks (HRNs). In this paper, the P-space method is used to examine global and local HRNs in China, meanwhile the adjacency matrix is developed, then the social network analysis and visualization tool UCINET is used to calculate the spatial and attribute data of HRNs at national and local levels in China. The small-world characteristics of whole HRNs are discussed, three networks which have different properties are determined, and a comparative analysis of the small-world effect is detected. Then, the relationship between the construction of high-speed rail and regional development of China is analysed. The results show that: 1) China's HRNs have small average path length ( L ) and large clustering coefficient (C ), representing a typical small-world network; 2) Local HRNs have a certain correlation with economic development. The reasons for the difference of HRNs with respect to characteristics among regions are eventually discussed.

scholarly journals Altered effective connectivity in migraine patients during emotional stimuli: a multi-frequency magnetoencephalography study

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Jing Ren ◽  
Qun Yao ◽  
Minjie Tian ◽  
Feng Li ◽  
Yueqiu Chen ◽  
...  
Keyword(s):  
Brain Activity ◽  
Effective Connectivity ◽  
Temporal Cortex ◽  
Primary Headache ◽  
Clustering Coefficient ◽  
Frequency Range ◽  
Emotional Stimuli ◽  
Emotional Modulation ◽  
Gamma Frequency ◽  
Wide Range

Abstract Background Migraine is a common and disabling primary headache, which is associated with a wide range of psychiatric comorbidities. However, the mechanisms of emotion processing in migraine are not fully understood yet. The present study aimed to investigate the neural network during neutral, positive, and negative emotional stimuli in the migraine patients. Methods A total of 24 migraine patients and 24 age- and sex-matching healthy controls were enrolled in this study. Neuromagnetic brain activity was recorded using a whole-head magnetoencephalography (MEG) system upon exposure to human facial expression stimuli. MEG data were analyzed in multi-frequency ranges from 1 to 100 Hz. Results The migraine patients exhibited a significant enhancement in the effective connectivity from the prefrontal lobe to the temporal cortex during the negative emotional stimuli in the gamma frequency (30–90 Hz). Graph theory analysis revealed that the migraine patients had an increased degree and clustering coefficient of connectivity in the delta frequency range (1–4 Hz) upon exposure to positive emotional stimuli and an increased degree of connectivity in the delta frequency range (1–4 Hz) upon exposure to negative emotional stimuli. Clinical correlation analysis showed that the history, attack frequency, duration, and neuropsychological scales of the migraine patients had a negative correlation with the network parameters in certain frequency ranges. Conclusions The results suggested that the individuals with migraine showed deviant effective connectivity in viewing the human facial expressions in multi-frequencies. The prefrontal-temporal pathway might be related to the altered negative emotional modulation in migraine. These findings suggested that migraine might be characterized by more universal altered cerebral processing of negative stimuli. Since the significant result in this study was frequency-specific, more independent replicative studies are needed to confirm these results, and to elucidate the neurocircuitry underlying the association between migraine and emotional conditions.

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