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 Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Satoru Hiwa ◽  
Shogo Obuchi ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Support Vector ◽  
Neural Basis ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

scholarly journals Brain network connectivity differs in early-onset neurodegenerative dementia

Neurology ◽  
2017 ◽  
Vol 89 (17) ◽  
pp. 1764-1772 ◽  
Author(s):  
Massimo Filippi ◽  
Silvia Basaia ◽  
Elisa Canu ◽  
Francesca Imperiale ◽  
Alessandro Meani ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Early Onset ◽  
Network Architecture ◽  
Parietal Lobe ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Network ◽  
Graph Analysis ◽  
Functional Brain ◽  
Functional Brain Network

Objective:To investigate functional brain network architecture in early-onset Alzheimer disease (EOAD) and behavioral variant frontotemporal dementia (bvFTD).Methods:Thirty-eight patients with bvFTD, 37 patients with EOAD, and 32 age-matched healthy controls underwent 3D T1-weighted and resting-state fMRI. Graph analysis and connectomics assessed global and local functional topologic network properties, regional functional connectivity, and intrahemispheric and interhemispheric between-lobe connectivity.Results:Despite similarly extensive cognitive impairment relative to controls, patients with EOAD showed severe global functional network alterations (lower mean nodal strength, local efficiency, clustering coefficient, and longer path length), while patients with bvFTD showed relatively preserved global functional brain architecture. Patients with bvFTD demonstrated reduced nodal strength in the frontoinsular lobe and a relatively focal altered functional connectivity of frontoinsular and temporal regions. Functional connectivity breakdown in the posterior brain nodes, particularly in the parietal lobe, differentiated patients with EOAD from those with bvFTD. While EOAD was associated with widespread loss of both intrahemispheric and interhemispheric functional correlations, bvFTD showed a preferential disruption of the intrahemispheric connectivity.Conclusions:Disease-specific patterns of functional network topology and connectivity alterations were observed in patients with EOAD and bvFTD. Graph analysis and connectomics may aid clinical diagnosis and help elucidate pathophysiologic differences between neurodegenerative dementias.

Structural constraints of functional connectivity drive cognitive impairment in the early stages of multiple sclerosis

2020 ◽  
pp. 135245852097180
Author(s):  
Ismail Koubiyr ◽  
Mathilde Deloire ◽  
Bruno Brochet ◽  
Pierre Besson ◽  
Julie Charré-Morin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Structure Function ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Coupling ◽  
Structural Constraints ◽  
Characteristic Path Length ◽  
Functional Connections

Background: The relationship between structural and functional deficits in multiple sclerosis (MS) is unclear. Objective: This study explored structure-function relationships during the 5 years following a clinically isolated syndrome and their role in cognitive performance. Methods: Thirty-two patients were enrolled after their first neurological episode suggestive of MS and followed for 5 years, along with 10 matched healthy controls. We assessed structural (using diffusion tensor imaging) and functional (using resting-state functional magnetic resonance imaging (fMRI)) brain network metrics, clinical and cognitive scores at each follow-up visit. Structural–functional coupling, calculated as the correlation coefficient between strengths of structural and functional networks, was used to assess structure–function relationships. Results: Structural clustering coefficient was significantly increased after 5 years, whereas characteristic path length decreased. Structural connections decreased after 1 year and increased after 5 years. Functional connections and related path lengths were decreased after 5 years. Structural–functional coupling had increased significantly after 5 years. This structural–functional coupling was associated with cognitive and clinical evolution, with stronger coupling associated with a decline in both domains. Conclusion: Our findings provide novel biological evidence that MS leads to a more constrained anatomical-dependant functional connectivity. The collapse of this network seems to lead to both cognitive worsening and clinical disability.

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 Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome

2019 ◽  
Vol 8 (3) ◽  
pp. 306 ◽  
Author(s):  
Alberto Cacciola ◽  
Antonino Naro ◽  
Demetrio Milardi ◽  
Alessia Bramanti ◽  
Leonardo Malatacca ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Topology ◽  
Minimally Conscious State ◽  
Conscious State ◽  
Clustering Coefficient ◽  
Unresponsive Wakefulness Syndrome ◽  
Topological Network ◽  
Functional Brain Network ◽  
Minimally Conscious ◽  
Nodal Level

Consciousness arises from the functional interaction of multiple brain structures and their ability to integrate different complex patterns of internal communication. Although several studies demonstrated that the fronto-parietal and functional default mode networks play a key role in conscious processes, it is still not clear which topological network measures (that quantifies different features of whole-brain functional network organization) are altered in patients with disorders of consciousness. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) patients from a topological network perspective, by using resting-state EEG recording. Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to in the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of local-community-paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to in MCS patients. At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. Taken together, our results highlight (i) the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and (ii) an aberrant connectome organization both at the network topology level and at the nodal level in UWS patients compared to in the MCS patients.

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.

scholarly journals Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

2020 ◽  
Author(s):  
Uzma Nawaz ◽  
Ivy Lee ◽  
Adam Beermann ◽  
Shaun Eack ◽  
Matcheri Keshavan ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Negative Symptom ◽  
Individual Variation ◽  
Symptom Severity ◽  
Spatial Organization ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Organization ◽  
Functional Brain Network

Abstract Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P &lt; .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).

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 Longitudinal Brain Functional Connectivity Changes of the Cortical Motor-Related Network in Subcortical Stroke Patients with Acupuncture Treatment

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yongxin Li ◽  
Ya Wang ◽  
Chenxi Liao ◽  
Wenhua Huang ◽  
Ping Wu
Keyword(s):  
Functional Connectivity ◽  
Western Medicine ◽  
Primary Motor Cortex ◽  
Brain Connectivity ◽  
Effective Means ◽  
Brain Network ◽  
Stroke Patients ◽  
Rehabilitation Therapy ◽  
Cortical Motor ◽  
Before And After

In clinical practice, the effectiveness of the rehabilitation therapy such as acupuncture combining conventional Western medicine (AG) on stroke people’s motor-related brain network and their behaviors has not been systematically studied. In the present study, seventeen adult ischemic patients were collected and divided into two groups: the conventional Western medicine treatment group (CG) and the AG. The neurological deficit scores (NDS) and resting-state functional MRI data were collected before and after treatment. Compared with the CG patients, AG patients exhibited a significant enhancement of the percent changes of NDS from pre- to posttreatment intervention. All patients showed significant changes of functional connectivity (FC) between the pair of cortical motor-related regions. After treatment, both patient groups showed a recovery of brain connectivity to the nearly normal level compared with the controls in these pairs. Moreover, a significant correlation between the percent changes of NDS and the pretreatment FC values of bilateral primary motor cortex (M1) in all patients was found. In conclusion, our results showed that AG therapy can be an effective means for ischemic stroke patients to recover their motor function ability. The FC strengths between bilateral M1 of stroke patients can predict stroke patients’ treatment outcome after rehabilitation therapy.

scholarly journals Analysis of Epileptic Seizures with Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yan Ni ◽  
Yinghua Wang ◽  
Tao Yu ◽  
Xiaoli Li
Keyword(s):  
Network Models ◽  
Small World ◽  
Epileptic Seizures ◽  
Neural Model ◽  
Brain Network ◽  
Large Area ◽  
Cortical Networks ◽  
Scale Free ◽  
Multiple Channel ◽  
Functional Brain Network

Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple channel EEG recordings based on regular, small world, random, and scale-free network models. Whether the connection patterns of cortical networks are directly associated with the epileptic seizures was investigated. The results showed that small world and scale-free cortical networks are highly correlated with the occurrence of epileptic seizures. In particular, the property of small world network is more significant during the epileptic seizures.

Sign in / Sign up

Export Citation Format

Share Document