scholarly journals Detecting large-scale networks in the human brain using high-density electroencephalography

2016 ◽  
Author(s):  
Quanying Liu ◽  
Seyedehrezvan Farahibozorg ◽  
Camillo Porcaro ◽  
Nicole Wenderoth ◽  
Dante Mantini
Keyword(s):  
Human Brain ◽  
Frequency Band ◽  
Source Localization ◽  
Large Scale ◽  
Brain Research ◽  
Brain Networks ◽  
Brain Network ◽  
High Density ◽  
Robust Detection ◽  
Realistic Information

AbstractHigh-density electroencephalography (hdEEG) is an emerging brain imaging technique that can permit investigating fast dynamics of cortical electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from showing brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and eLORETA source localization, together with ICA for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the cortex as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research.

scholarly journals Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130531 ◽  
Author(s):  
Petra E. Vértes ◽  
Aaron Alexander-Bloch ◽  
Edward T. Bullmore
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Large Scale ◽  
Hebbian Learning ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Generative Models ◽  
Adaptive Behaviour ◽  
Topological Term

Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.

scholarly journals Decreased integration of EEG source-space networks in disorders of consciousness

2018 ◽  
Author(s):  
Jennifer Rizkallah ◽  
Jitka Annen ◽  
Julien Modolo ◽  
Olivia Gosseries ◽  
Pascal Benquet ◽  
...  
Keyword(s):  
Information Processing ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
High Density ◽  
Disorders Of Consciousness ◽  
Functional Brain ◽  
Space Networks ◽  
Functional Brain Networks ◽  
Processing Network

AbstractIncreasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients’ functional brain networks.Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS- (n=17) and UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate group differences between healthy volunteers and patient groups.Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

scholarly journals Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

2015 ◽  
Vol 9 ◽  
Author(s):  
Xerxes D. Arsiwalla ◽  
Riccardo Zucca ◽  
Alberto Betella ◽  
Enrique Martinez ◽  
David Dalmazzo ◽  
...  
Keyword(s):  
Human Brain ◽  
Real Time ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Large Scale Simulation ◽  
Time Interaction

scholarly journals Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

2018 ◽  
Author(s):  
RL van den Brink ◽  
S Nieuwenhuis ◽  
TH Donner
Keyword(s):  
Spatial Distribution ◽  
Human Brain ◽  
Spatial Patterns ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Heterogeneous Distribution ◽  
Neurotransmitter Systems ◽  
The Brain ◽  
Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

scholarly journals Comparing the temporal relationship of structural and functional connectivity changes in different adult human brain networks: a single-case study

2018 ◽  
Vol 3 ◽  
pp. 50 ◽  
Author(s):  
Takamitsu Watanabe ◽  
Geraint Rees
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Single Case ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Time Lags ◽  
Single Case Study ◽  
Adult Human

Background: Despite accumulated evidence for adult brain plasticity, the temporal relationships between large-scale functional and structural connectivity changes in human brain networks remain unclear. Methods: By analysing a unique richly detailed 19-week longitudinal neuroimaging dataset, we tested whether macroscopic functional connectivity changes lead to the corresponding structural alterations in the adult human brain, and examined whether such time lags between functional and structural connectivity changes are affected by functional differences between different large-scale brain networks. Results: In this single-case study, we report that, compared to attention-related networks, functional connectivity changes in default-mode, fronto-parietal, and sensory-related networks occurred in advance of modulations of the corresponding structural connectivity with significantly longer time lags. In particular, the longest time lags were observed in sensory-related networks. In contrast, such significant temporal differences in connectivity change were not seen in comparisons between anatomically categorised different brain areas, such as frontal and occipital lobes. These observations survived even after multiple validation analyses using different connectivity definitions or using parts of the datasets. Conclusions: Although the current findings should be examined in independent datasets with different demographic background and by experimental manipulation, this single-case study indicates the possibility that plasticity of macroscopic brain networks could be affected by cognitive and perceptual functions implemented in the networks, and implies a hierarchy in the plasticity of functionally different brain systems.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Advances in Electrical Neuroimaging, Brain Networks and Neurofeedback Protocols

2020 ◽  
Author(s):  
Robert W. Thatcher ◽  
Carl J. Biver ◽  
Ernesto Palermero Soler ◽  
Joel Lubar ◽  
J. Lucas Koberda
Keyword(s):  
Human Brain ◽  
Real Time ◽  
Brain Mapping ◽  
Brain Networks ◽  
Three Dimensional ◽  
Brain Network ◽  
Eeg Biofeedback ◽  
Reference Database ◽  
Z Score ◽  
Electrical Neuroimaging

Human EEG biofeedback (neurofeedback) started in the 1940s using 1 EEG recording channel, then to 4 channels in the 1990s. New advancements in electrical neuroimaging expanded EEG biofeedback to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) three-dimensional current sources of the EEG. In 2004–2006 the concept of a “real-time” comparison of the EEG to a healthy reference database was developed and tested using surface EEG z-score neurofeedback based on a statistical bell curve called “real-time” z-scores. The “real-time” or “live” normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value, i.e., the distance from the mean of an age matched reference sample. In 2009 LORETA z-score neurofeedback further increased the specificity by targeting brain network hubs referred to as Brodmann areas. A symptom check list program to help link symptoms to dysregulation of brain networks based on fMRI and PET and neurology was created in 2009. The symptom checklist and NIH based networks linking symptoms to brain networks grew out of the human brain mapping program starting in 1990 which is continuing today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patient’s symptoms. New advancements in electrical neuroimaging introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala and habenula. Future applications of swLORETA z-score neurofeedback represents another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present and future predictions of z-score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback.

scholarly journals Early alterations of large-scale brain networks temporal dynamics in young children with autism

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Aurélie Bochet ◽  
Holger Franz Sperdin ◽  
Tonia Anahi Rihs ◽  
Nada Kojovic ◽  
Martina Franchini ◽  
...  
Keyword(s):  
Large Scale ◽  
Developmental Stages ◽  
Temporal Dynamics ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Clustering Methods ◽  
Early Developmental Stages ◽  
Spatio Temporal ◽  
Early Developmental

AbstractAutism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A–E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain–behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

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.

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