scholarly journals Multiple Brain Sources Are Differentially Engaged in the Inhibition of Distinct Action Types

2021 ◽  
pp. 1-15
Author(s):  
Mario Hervault ◽  
Pier-Giorgio Zanone ◽  
Jean-Christophe Buisson ◽  
Raoul Huys
Keyword(s):  
Inhibitory Control ◽  
Cingulate Cortex ◽  
Brain Network ◽  
Continuous Action ◽  
Inhibitory Network ◽  
Eeg Data ◽  
Discrete Action ◽  
Continuous Actions ◽  
Increased Activity ◽  
The Brain

Abstract Most studies contributing to identify the brain network for inhibitory control have investigated the cancelation of prepared–discrete actions, thus focusing on an isolated and short-lived chunk of human behavior. Aborting ongoing–continuous actions is an equally crucial ability but remains little explored. Although discrete and ongoing–continuous rhythmic actions are associated with partially overlapping yet largely distinct brain activations, it is unknown whether the inhibitory network operates similarly in both situations. Thus, distinguishing between action types constitutes a powerful means to investigate whether inhibition is a generic function. We, therefore, used independent component analysis (ICA) of EEG data and show that canceling a discrete action and aborting a rhythmic action rely on independent brain components. The ICA showed that a delta/theta power increase generically indexed inhibitory activity, whereas N2 and P3 ERP waves did so in an action-specific fashion. The action-specific components were generated by partially distinct brain sources, which indicates that the inhibitory network is engaged differently when canceling a prepared–discrete action versus aborting an ongoing–continuous action. In particular, increased activity was estimated in precentral gyri and posterior parts of the cingulate cortex for action canceling, whereas an enhanced activity was found in more frontal gyri and anterior parts of the cingulate cortex for action aborting. Overall, the present findings support the idea that inhibitory control is differentially implemented according to the type of action to revise.

scholarly journals Dissociable brain components for inhibitory control: A source separation analysis of cancelling and stopping actions

2021 ◽  
Author(s):  
Mario Hervault
Keyword(s):  
Inhibitory Control ◽  
Event Related Potentials ◽  
Cingulate Cortex ◽  
Brain Network ◽  
Continuous Action ◽  
Inhibitory Network ◽  
Discrete Action ◽  
Related Potentials ◽  
Continuous Actions ◽  
The Brain

Previous work has drawn an inhibitory brain network by investigating the cancellation of prepared–discrete actions, thus focusing on an isolated aspect of human behavior. Indeed, ongoing–continuous actions, which have been associated with distinct brain mechanisms for generation, are also crucial to be stopped. Still, it is unknown whether the same inhibitory network generalizes to various situations, including cancelling a prepared–discrete action and stopping an ongoing–continuous action. We used independent component analysis of EEG data to show that cancelling and stopping action rely on independent brain components for inhibition. Indeed, the brain component which best explained inhibition when cancelling action was not involved in stopping action, and reciprocally. The analysis of these components showed that a Delta/Theta power increase generically indexes inhibitory activity, while N2 and P3 event-related potentials do it in a specific way. Sources reconstruction further identified dissociations in the brain areas generating the two inhibitory components. The differences identified between the two source generators underlie some specific activities of the inhibitory network involved in cancelling a prepared–discrete action and stopping an ongoing–continuous action. Thus, increased activity was observed in precentral gyri and posterior parts of the cingulate cortex when action cancelling, while a higher activity was found in more frontal gyri and anterior parts of the cingulate cortex when action stopping. Overall, the present findings support the idea that inhibitory control is differentially implemented according to the type of action to revise.

Study of Loss of Alertness and Driver Fatigue Using Visibility Graph Synchronization

2016 ◽  
pp. 171-193 ◽  
Author(s):  
Anwesha Sengupta ◽  
Sibsambhu Kar ◽  
Aurobinda Routray
Keyword(s):  
Sleep Deprivation ◽  
Complex Network ◽  
Human Subjects ◽  
Small World ◽  
Brain Network ◽  
Visibility Graph ◽  
Eeg Data ◽  
Lack Of Sleep ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is widely used to predict performance degradation of human subjects due to mental or physical fatigue. Lack of sleep or insufficient quality or quantity of sleep is one of the major reasons of fatigue. Analysis of fatigue due to sleep deprivation using EEG synchronization is a promising field of research. The present chapter analyses advancing levels of fatigue in human drivers in a sleep-deprivation experiment by studying the synchronization between EEG data. A Visibility Graph Similarity-based method has been employed to quantify the synchronization, which has been formulated in terms of a complex network. The change in the parameters of the network has been analyzed to find the variation of connectivity between brain areas and hence to trace the increase in fatigue levels of the subjects. The parameters of the brain network have been compared with those of a complex network with a random degree of connectivity to establish the small-world nature of the brain network.

Plasticity in auditory categorization is supported by differential engagement of the auditory-linguistic network

2019 ◽  
Author(s):  
Gavin M. Bidelman ◽  
Breya Walker
Keyword(s):  
Inferior Frontal Gyrus ◽  
Primary Auditory Cortex ◽  
Brain Regions ◽  
Brain Network ◽  
Eeg Data ◽  
Level Information ◽  
Auditory Experience ◽  
Musical Sounds ◽  
The Brain ◽  
Information Functional

ABSTRACTTo construct our perceptual world, the brain categorizes variable sensory cues into behaviorally-relevant groupings. Categorical representations are apparent within a distributed fronto-temporo-parietal brain network but how this neural circuitry is shaped by experience remains undefined. Here, we asked whether speech (and music) categories might be formed within different auditory-linguistic brain regions depending on listeners’ auditory expertise. We recorded EEG in highly skilled (musicians) vs. novice (nonmusicians) perceivers as they rapidly categorized speech and musical sounds. Musicians showed perceptual enhancements across domains, yet source EEG data revealed a double dissociation in the neurobiological mechanisms supporting categorization between groups. Whereas musicians coded categories in primary auditory cortex (PAC), nonmusicians recruited non-auditory regions (e.g., inferior frontal gyrus, IFG) to generate category-level information. Functional connectivity confirmed nonmusicians’ increased left IFG involvement reflects stronger routing of signal from PAC directed to IFG, presumably because sensory coding is insufficient to construct categories in less experienced listeners. Our findings establish auditory experience modulates specific engagement and inter-regional communication in the auditory-linguistic network supporting CP. Whereas early canonical PAC representations are sufficient to generate categories in highly trained ears, less experienced perceivers broadcast information downstream to higher-order linguistic brain areas (IFG) to construct abstract sound labels.

scholarly journals Synchronization Stability Model of Complex Brain Networks: An EEG Study

2020 ◽  
Vol 11 ◽  
Author(s):  
Guimei Yin ◽  
Haifang Li ◽  
Shuping Tan ◽  
Rong Yao ◽  
Xiaohong Cui ◽  
...  
Keyword(s):  
Steady State ◽  
Brain Networks ◽  
Stable State ◽  
Lyapunov Stability Theory ◽  
Brain Network ◽  
State Model ◽  
Complex Dynamic ◽  
Eeg Data ◽  
Steady State Model ◽  
The Brain

In this paper, from the perspective of complex network dynamics we investigated the formation of the synchronization state of the brain networks. Based on the Lyapunov stability theory of complex networks, a synchronous steady-state model suitable for application to complex dynamic brain networks was proposed. The synchronization stability problem of brain network state equation was transformed into a convex optimization problem with Block Coordinate Descent (BCD) method. By using Random Apollo Network (RAN) method as a node selection rule, the brain network constructs its subnet work dynamically. We also analyzes the change of the synchronous stable state of the subnet work constructed by this method with the increase of the size of the network. Simulation EEG data from alcohol addicts patients and Real experiment EEG data from schizophrenia patients were used to verify the robustness and validity of the proposed model. Differences in the synchronization characteristics of the brain networks between normal and alcoholic patients were analyzed, so as differences between normal and schizophrenia patients. The experimental results indicated that the establishment of a synchronous steady state model in this paper could be used to verify the synchronization of complex dynamic brain networks and potentially be of great value in the further study of the pathogenic mechanisms of mental illness.

scholarly journals Spatiotemporal evolution of epileptic seizure based on mutual information and dynamic brain network

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Mengnan Ma ◽  
Xiaoyan Wei ◽  
Yinlin Cheng ◽  
Ziyi Chen ◽  
Yi Zhou
Keyword(s):  
Mutual Information ◽  
Complex Network ◽  
Epileptic Seizures ◽  
Brain Network ◽  
The State ◽  
Propagation Path ◽  
Network Function ◽  
Eeg Data ◽  
The Brain ◽  
Abnormal Brain

Abstract Background Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. Methods In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network. Results Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly. Conclusion In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.

scholarly journals Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity

2021 ◽  
pp. 101000
Author(s):  
Moriah E. Thomason ◽  
Ava C. Palopoli ◽  
Nicki N. Jariwala ◽  
Denise M. Werchan ◽  
Alan Chen ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Brain Network Connectivity ◽  
The Brain

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

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