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 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.

scholarly journals Functional Connectivity of EEG in Encephalitis during Slow Biphasic Complexes

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2978
Author(s):  
Giovanni Chiarion ◽  
Luca Mesin
Keyword(s):  
Functional Connectivity ◽  
Path Length ◽  
Inflammatory Diseases ◽  
Clustering Coefficient ◽  
Eeg Rhythms ◽  
Characteristic Path Length ◽  
Slowing Down ◽  
Eeg Functional Connectivity ◽  
Network Patterns ◽  
The Brain

The electroencephalogram (EEG) of patients suffering from inflammatory diseases of the brain may show specific waveforms called slow biphasic complexes (SBC). Recent studies indicated a correlation between the severity of encephalitis and some features of SBCs, such as location, amplitude and frequency of appearance. Moreover, EEG rhythms were found to vary before the onset of an SBC, as if the brain was preparing to the discharge (actually with a slowing down of the EEG oscillation). Here, we investigate possible variations of EEG functional connectivity (FC) in EEGs from pediatric patients with different levels of severity of encephalitis. FC was measured by the maximal crosscorrelation of EEG rhythms in different bipolar channels. Then, the indexes of network patterns (namely strength, clustering coefficient, efficiency and characteristic path length) were estimated to characterize the global behavior when they are measured during SBCs or far from them. EEG traces showed statistical differences in the two conditions: clustering coefficient, efficiency and strength are higher close to an SBC, whereas the characteristic path length is lower. Moreover, for more severe conditions, an increase in clustering coefficient, efficiency and strength and a decrease in characteristic path length were observed in the delta–theta band. These outcomes support the hypothesis that SBCs result from the anomalous coordination of neurons in different brain areas affected by the inflammation process and indicate FC as an additional key for interpreting the EEG in encephalitis patients.

scholarly journals CORRELATION BETWEEN PHYSICAL ACTIVITY AND FUNCTIONAL CONNECTIVITY OF THE BRAIN

2020 ◽  
Vol 19 (4) ◽  
pp. 50-59
Author(s):  
I Feklicheva ◽  
N Chipeeva ◽  
I Zakharov ◽  
E Maslennikova ◽  
V Ismatullina
Keyword(s):  
Physical Activity ◽  
Functional Connectivity ◽  
Effect Size ◽  
Statistical Significance ◽  
Test Procedure ◽  
Clustering Coefficient ◽  
Wilcoxon Test ◽  
Characteristic Path Length ◽  
Graph Metrics ◽  
The Brain

Aim. The purpose of the article is to study the correlation between physical activity and functional connectivity (FC) of the brain based on EEG data. Materials and methods. The study sample included 43 healthy persons aged from 17 to 35 years (26 women). The participants were divided into two groups. The first group (21 persons) was engaged in physical activity for more than 3 hours a week, the second (22 persons) group was not engaged in physical activity. In all participants, 10-minute EEG recording at rest was performed. To assess the differences in the global characteristics of functional connectivity, such graph metrics as the characteristic path length, clustering coefficient, small world index, and modularity were chosen. Results. Significant differences between the two groups in terms of the cluster coefficient were obtained using the Wilcoxon test (W = 201, p < 0.001). To compare the intergroup differences, the DOT (double one-sided test) procedure was used, which allowed assessing the equivalence of groups based on a pre-selected effect size. When comparing the two groups, statistically significant differences are observed for two one-sided Student’s tests, while the effect size exceeds the pre-selected effect size (d = 0.05), both for the upper and lower reference values, which indicates not only statistical significance, but also the inequality of the samples. Conclusion. Young people who regularly engage in physical activity for more than 3 hours per week have higher functional connectivity of the brain than those of the same age who do not engage in physical activity, which is expressed in the clustering coefficient. In general, the results of this study show that physical activity increases functional connectivity of the brain in the alpha range. The connectivity increases due to the emergence of new functional clusters within existing associations of brain regions.

scholarly journals Altered Effective Connectivity in Migraine Patients During Emotional Stimuli: a Multi-frequency Magnetoencephalography Study.

2021 ◽  
Author(s):  
Jing Ren ◽  
Qun Yao ◽  
Minjie Tian ◽  
Feng Li ◽  
Yueqiu Chen ◽  
...  
Keyword(s):  
Negative Emotion ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Temporal Cortex ◽  
Primary Headache ◽  
Clustering Coefficient ◽  
Emotional Stimuli ◽  
Delta Band ◽  
Wide Range ◽  
Graph Theory Analysis

Abstract Background: Migraine is a common and disabling primary headache associated with a wide range of psychiatric comorbidities. However, the mechanisms of emotion processing in migraine are not fully understood yet. The present study was designed to investigate the neural network during neutral, positive，and negative emotional stimuli in migraine suffers.Methods: We enrolled 24 migraine suffers and 24 age- and sex-matched controls in this study. Neuromagnetic brain activity was recorded by using a whole-head magnetoencephalography (MEG) system towards human faces expression pictures. MEG data were analyzed in the multi-frequency band of 1–100 Hz.Results: Migraine patients exhibited significantly enhanced effective connectivity from the prefrontal lobe to the temporal cortex during negative emotional stimuli in the gamma band(30-90Hz). Graph theory analysis revealed that patients had (1) an increased degree and clustering coefficient of connectivity in the delta band(1-4Hz) during positive emotional stimuli; (2) an increased degree of connectivity in the delta band(1-4Hz) during negative emotional stimuli.Conclusion: The results suggested individuals with migraine showed deviant effective connectivity when viewing human facial expressions in multi-frequency. The prefrontal-temporal pathway might be related to the altered negative emotion modulation in migraine. These findings may contribute to understanding the mechanism of the comorbidity of depression and anxiety in migraine and provide references for the comprehensive therapeutic plan.

scholarly journals Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

2020 ◽  
pp. 1-21
Author(s):  
Alexandra Anagnostopoulou ◽  
Charis Styliadis ◽  
Panagiotis Kartsidis ◽  
Evangelia Romanopoulou ◽  
Vasiliki Zilidou ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Functional Connectivity ◽  
Cognitive Training ◽  
Resting State ◽  
Network Architecture ◽  
Right Hemisphere ◽  
Random Network ◽  
Brain Activity ◽  
Hierarchical Organization ◽  
The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

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.

scholarly journals FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

2021 ◽  
Author(s):  
Hessam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Permutation Test ◽  
Pearson Correlation ◽  
Functional Integration ◽  
Clustering Coefficient ◽  
Kernel Trick ◽  
Non Linear ◽  
The Brain

Abstract Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.

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.

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

2020 ◽  
Vol 52 (1) ◽  
pp. 52-60
Author(s):  
Yousef Mohammadi ◽  
Mohammad Hassan Moradi
Keyword(s):  
Functional Connectivity ◽  
Regression Model ◽  
Brain Activity ◽  
Clustering Coefficient ◽  
Eeg Signal ◽  
Alpha Band ◽  
Depression Severity ◽  
Severity Scores ◽  
Severity Prediction ◽  
Severity Of Depression

Background Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory–II scores). Methods Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. Results Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band ( r = 0.839; P < .0001, root mean square error score of 7.69). Conclusion We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

scholarly journals Brain Network Modeling Based on Mutual Information and Graph Theory for Predicting the Connection Mechanism in the Progression of Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 300 ◽  
Author(s):  
Shuaizong Si ◽  
Bin Wang ◽  
Xiao Liu ◽  
Chong Yu ◽  
Chao Ding ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Mutual Information ◽  
Brain Networks ◽  
Brain Network ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Anatomical Space ◽  
The Brain

Alzheimer’s disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiments show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

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