scholarly journals Reconfiguration of Network Hub Structure after Propofol-induced Unconsciousness

2013 ◽  
Vol 119 (6) ◽  
pp. 1347-1359 ◽  
Author(s):  
Heonsoo Lee ◽  
George A. Mashour ◽  
Gyu-Jeong Noh ◽  
Seunghwan Kim ◽  
UnCheol Lee
Keyword(s):  
General Anesthesia ◽  
Brain Networks ◽  
Phase Relationship ◽  
Clustering Coefficient ◽  
Phase Lag ◽  
Hub Location ◽  
Functional Brain ◽  
Functional Changes ◽  
States Of Consciousness ◽  
Functional Brain Network

Abstract Introduction: General anesthesia induces unconsciousness along with functional changes in brain networks. Considering the essential role of hub structures for efficient information transmission, the authors hypothesized that anesthetics have an effect on the hub structure of functional brain networks. Methods: Graph theoretical network analysis was carried out to study the network properties of 21-channel electroencephalogram data from 10 human volunteers anesthetized on two occasions. The functional brain network was defined by Phase Lag Index, a coherence measure, for three states: wakefulness, loss of consciousness induced by the anesthetic propofol, and recovery of consciousness. The hub nodes were determined by the largest centralities. The correlation between the altered hub organization and the phase relationship between electroencephalographic channels was investigated. Results: Topology rather than connection strength of functional networks correlated with states of consciousness. The average path length, clustering coefficient, and modularity significantly increased after administration of propofol, which disrupted long-range connections. In particular, the strength of hub nodes significantly decreased. The primary hub location shifted from the parietal to frontal region, in association with propofol-induced unconsciousness. The phase lead of frontal to parietal regions in the α frequency band (8–13 Hz) observed during wakefulness reversed direction after propofol and returned during recovery. Conclusions: Propofol reconfigures network hub structure in the brain and reverses the phase relationship between frontal and parietal regions. Changes in network topology are more closely associated with states of consciousness than connectivity and may be the primary mechanism for the observed loss of frontal to parietal feedback during general anesthesia.

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 Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

2021 ◽  
Vol 11 (8) ◽  
pp. 1066
Author(s):  
Han Li ◽  
Qizhong Zhang ◽  
Ziying Lin ◽  
Farong Gao
Keyword(s):  
Graph Theory ◽  
Brain Networks ◽  
Tensor Decomposition ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Dynamic Methods ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Elm Classifier

Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

2020 ◽  
Vol 30 (10) ◽  
pp. 2050051
Author(s):  
Feng Fang ◽  
Thomas Potter ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Emotion Regulation ◽  
Large Scale ◽  
Brain Networks ◽  
Cognitive Reappraisal ◽  
Brain Network ◽  
Affective Processing ◽  
Functional Brain ◽  
Affective Stimuli ◽  
Functional Brain Network ◽  
Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

scholarly journals A Generative Network Model of the Human Brain Normal Aging Process

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Xiao Liu ◽  
Shuaizong Si ◽  
Bo Hu ◽  
Hai Zhao ◽  
Jian Zhu
Keyword(s):  
Human Brain ◽  
Network Model ◽  
Aging Process ◽  
Brain Aging ◽  
Brain Networks ◽  
Normal Aging ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Generative Mechanisms

The human brain is approximately a symmetric structure, although the functional brain does not exhibit symmetry. Functional brain aging process modelling is essential for the understanding of hypothesized generative mechanisms for human brain networks throughout one’s lifespan. We present a novel generative network model of the human functional brain network, which is the hybrid of the local naïve Bayes model and the anatomical similarity correction (LNBE). We use LNBE, as well as published generative network models to simulate the aging process of the functional brain network, to construct artificial brain networks and to reveal the generative mechanisms and evolutionary patterns of human functional brain across human lifespans. It is suggested that the idea of classifying common neighbours while considering anatomical distances during network formation can provide a much more similar generative mechanism of the human fMRI brain aging process as well as a more practical generative network model of it. We hold that studies on brain normal aging process modelling have the potential to improve the way in which early warnings for latent injury or disease are practised today and advance healthcare.

scholarly journals P14.53 Deconstructing pathologically increased MEG network clustering in glioma patients

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii79-iii79
Author(s):  
S D Kulik ◽  
J Derks ◽  
T Numan ◽  
A Hillebrand ◽  
P C de Witt Hamer ◽  
...  
Keyword(s):  
Brain Network ◽  
Global Network ◽  
Tumor Localization ◽  
Network Clustering ◽  
Phase Lag ◽  
Functional Brain ◽  
Global Average ◽  
Entire Brain ◽  
Functional Brain Network ◽  
Local Clustering

Abstract Introduction Functional brain networks in glioma patients are characterized by higher global clustering than healthy controls, indicating stronger connectivity in triads of brain regions when averaging across the entire brain. However, this could be due to either primary increased local clustering of (peri)tumor regions or higher local clustering throughout the entire brain. METHODS Magnetoencephalography recordings of 71 glioma patients and 53 HCs were analyzed by calculating functional connectivity with the phase lag index between source-localized time series of 78 cortical regions of the automated anatomical labelling atlas. Per participant, we calculated (1) global average clustering, (2) local clustering of tumor and non-tumor regions, and (3) Euclidean distance between tumor centroids and of all other region centroids. RESULTS Glioma patients had higher global average clustering (p=0.002) than HCs. This increase was indeed global: there was no difference between tumor and non-tumor regions (p=0.154) and no association between distance and local clustering (p=0.759). When splitting patients into high (top 25%, n=18) and normal global clustering (other 75%, n=53) to more specifically pick up on the determinants of pathological global average clustering, again no localized or distance-dependent effects were found. High clustering patients were younger than patients with normal global clustering (p=0.027). Posthoc analysis into tumor localization preference for particular network regions in the entire patient cohort revealed greater tumor occurrence in regions with high clustering in HC (p<0.001), while patients with high global clustering showed tumors localized in regions with lower clustering in HC (p=0.032). CONCLUSION The functional brain network of a subset of (relatively young) glioma patients is disturbed on a global level, suggesting that treatment thereof might benefit patients. Moreover, our exploratory analyses suggest that gliomas occur more often in normally highly clustered regions, but that tumors occurring in less clustered regions are associated with more extensive global network alterations. These findings may speculatively indicate that patients with and without such pathologically altered global clustering represent distinct phenotypes (both in terms of age and tumor localization) and may also need to be treated as such.

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.

Longitudinal study of functional brain network reorganization in clinically isolated syndrome

2018 ◽  
Vol 26 (2) ◽  
pp. 188-200 ◽  
Author(s):  
Ismail Koubiyr ◽  
Mathilde Deloire ◽  
Pierre Besson ◽  
Pierrick Coupé ◽  
Cécile Dulau ◽  
...  
Keyword(s):  
Betweenness Centrality ◽  
Early Stage ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Early Stages ◽  
Global Efficiency ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Brain Network Reorganization

Background: There is a lack of longitudinal studies exploring the topological organization of functional brain networks at the early stages of multiple sclerosis (MS). Objective: This study aims to assess potential brain functional reorganization at rest in patients with CIS (PwCIS) after 1 year of evolution and to characterize the dynamics of functional brain networks at the early stage of the disease. Methods: We prospectively included 41 PwCIS and 19 matched healthy controls (HCs). They were scanned at baseline and after 1 year. Using graph theory, topological metrics were calculated for each region. Hub disruption index was computed for each metric. Results: Hub disruption indexes of degree and betweenness centrality were negative at baseline in patients ( p < 0.05), suggesting brain reorganization. After 1 year, hub disruption indexes for degree and betweenness centrality were still negative ( p < 0.00001), but such reorganization appeared more pronounced than at baseline. Different brain regions were driving these alterations. No global efficiency differences were observed between PwCIS and HCs either at baseline or at 1 year. Conclusion: Dynamic changes in functional brain networks appear at the early stages of MS and are associated with the maintenance of normal global efficiency in the brain, suggesting a compensatory effect.

scholarly journals F37. CACNA1C HYPOFUNCTION IN KETAMINE-ACTIVATED BRAIN NETWORKS IMPAIRS MEMORY CONSOLIDATION AND FUNCTIONAL BRAIN NETWORK CONNECTIVITY

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S233-S233
Author(s):  
Rebecca Hughes ◽  
Cosima Willi ◽  
Jayde Whittingham-Dowd ◽  
Susan Broughton ◽  
Greg Bristow ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Brain Network Connectivity

scholarly journals The Insula Is a Hub for Functional Brain Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis

2021 ◽  
Vol 15 ◽  
Author(s):  
Chunyan Li ◽  
Xiaomin Pang ◽  
Ke Shi ◽  
Qijia Long ◽  
Jinping Liu ◽  
...  
Keyword(s):  
Parietal Lobe ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Hamilton Depression Scale ◽  
Healthy Controls ◽  
Functional Brain ◽  
Aspartate Receptor ◽  
Nmdar Encephalitis ◽  
Functional Brain Network ◽  
Left Insula

BackgroundIn recent years, imaging technologies have been rapidly evolving, with an emphasis on the characterization of brain structure changes and functional imaging in patients with autoimmune encephalitis. However, the neural basis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and its linked cognitive decline is unclear. Our research aimed to assess changes in the functional brain network in patients with anti-NMDAR encephalitis and whether these changes lead to cognitive impairment.MethodsTwenty-one anti-NMDAR encephalitis patients and 22 age-, gender-, and education status-matched healthy controls were assessed using resting functional magnetic resonance imaging (fMRI) scanning and neuropsychological tests, including the Hamilton Depression Scale (HAMD24), the Montreal Cognitive Assessment (MoCA), and the Hamilton Anxiety Scale (HAMA). A functional brain network was constructed using fMRI, and the topology of the network parameters was analyzed using graph theory. Next, we extracted the aberrant topological parameters of the functional network as seeds and compared causal connectivity with the whole brain. Lastly, we explored the correlation of aberrant topological structures with deficits in cognitive performance.ResultsRelative to healthy controls, anti-NMDAR encephalitis patients exhibited decreased MoCA scores and increased HAMA and HAMD24 scores (p &lt; 0.05). The nodal clustering coefficient and nodal local efficiency of the left insula (Insula_L) were significantly decreased in anti-NMDAR encephalitis patients (p &lt; 0.05 following Bonferroni correction). Moreover, anti-NMDAR encephalitis patients showed a weakened causal connectivity from the left insula to the left inferior parietal lobe (Parietal_Inf_L) compared to healthy controls. Conversely, the left superior parietal lobe (Parietal_sup_L) exhibited an enhanced causal connectivity to the left insula in anti-NMDAR encephalitis patients compared to controls. Unexpectedly, these alterations were not correlated with any neuropsychological test scores.ConclusionThis research describes topological abnormalities in the functional brain network in anti-NMDAR encephalitis. These results will be conducive to understand the structure and function of the brain network of patients with anti-NMDAR encephalitis and further explore the neuropathophysiological mechanisms.

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.

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