Mapping epileptic directional brain networks using intracranial EEG data

Summary The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another as directional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. As such, effective epilepsy diagnosis and treatment require accurate identification of directional connections among regions, i.e., mapping of epileptic patients’ brain networks. This article aims to understand the epileptic brain network using intracranial electroencephalographic data—recordings of epileptic patients’ brain activities in many regions. The most popular models for directional connectivity use ordinary differential equations (ODE). However, ODE models are sensitive to data noise and computationally costly. To address these issues, we propose a high-dimensional state-space multivariate autoregression (SSMAR) model for the brain’s directional connectivity. Different from standard multivariate autoregression and SSMAR models, the proposed SSMAR features a cluster structure, where the brain network consists of several clusters of densely connected brain regions. We develop an expectation–maximization algorithm to estimate the proposed model and use it to map the interregional networks of epileptic patients in different seizure stages. Our method reveals the evolution of brain networks during seizure development.

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

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

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.

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Psychological resilience correlates with EEG source-space brain network flexibility

Network Neuroscience ◽

10.1162/netn_a_00079 ◽

2019 ◽

Vol 3 (2) ◽

pp. 539-550 ◽

Cited By ~ 5

Author(s):

Véronique Paban ◽

Julien Modolo ◽

Ahmad Mheich ◽

Mahmoud Hassan

Keyword(s):

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Personality Testing ◽

Functional Networks ◽

Psychological Resilience ◽

Full Spectrum ◽

Sliding Windows ◽

Resilience Scale ◽

Network Flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

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Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

International Journal of Image and Graphics ◽

10.1142/s021946782240006x ◽

2021 ◽

Author(s):

A. Thushara ◽

C. Ushadevi Amma ◽

Ansamma John

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Graph Theory ◽

Neurodegenerative Disorder ◽

Brain Networks ◽

Network Connectivity ◽

Brain Regions ◽

Brain Network ◽

Fiber Tracts ◽

The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

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Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

Complexity ◽

10.1155/2017/8362741 ◽

2017 ◽

Vol 2017 ◽

pp. 1-27 ◽

Cited By ~ 23

Author(s):

Jin Liu ◽

Min Li ◽

Yi Pan ◽

Wei Lan ◽

Ruiqing Zheng ◽

...

Keyword(s):

Network Analysis ◽

Brain Networks ◽

Small World ◽

Brain Regions ◽

Brain Network ◽

Brain Diseases ◽

Brain Disorders ◽

Comprehensive Overview ◽

Rich Club ◽

New Perspective

It is well known that most brain disorders are complex diseases, such as Alzheimer’s disease (AD) and schizophrenia (SCZ). In general, brain regions and their interactions can be modeled as complex brain network, which describe highly efficient information transmission in a brain. Therefore, complex brain network analysis plays an important role in the study of complex brain diseases. With the development of noninvasive neuroimaging and electrophysiological techniques, experimental data can be produced for constructing complex brain networks. In recent years, researchers have found that brain networks constructed by using neuroimaging data and electrophysiological data have many important topological properties, such as small-world property, modularity, and rich club. More importantly, many brain disorders have been found to be associated with the abnormal topological structures of brain networks. These findings provide not only a new perspective to explore the pathological mechanisms of brain disorders, but also guidance for early diagnosis and treatment of brain disorders. The purpose of this survey is to provide a comprehensive overview for complex brain network analysis and its applications to brain disorders.

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Intracranial brain stimulation modulates fMRI-based network switching

10.1101/2021.01.12.426446 ◽

2021 ◽

Author(s):

Mangor Pedersen ◽

Andrew Zalesky

Keyword(s):

Clinical Efficacy ◽

Brain Stimulation ◽

Large Scale ◽

Brain Networks ◽

Network Models ◽

Brain Regions ◽

Brain Network ◽

Intracranial Stimulation ◽

List Type ◽

Network Modelling

SummaryThe extent to which resting-state fMRI (rsfMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous rsfMRI and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced during intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is typically increased in brain disorders, we hypothesised that intracranial stimulation would reduce the brain’s switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved rsfMRI connectivity. Network switching was significantly decreased during epochs with brain stimulation compared to epochs with no brain stimulation. The initial stimulation onset of brain stimulation was associated with the greatest decrease in network switching, followed by a more consistent reduction in network switching throughout the scans. These changes were most commonly observed in cortical networks spatially distant from the stimulation targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in neurological disease.HighlightsrsfMRI network switching is attenuated during intracranial brain stimulationStimulation-induced switching is observed distant from electrode targetsOur results are validated across a range of network parametersNetwork models may inform clinical efficacy of brain stimulation

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Behavioural relevance of spontaneous, transient brain network interactions in fMRI

10.1101/779736 ◽

2019 ◽

Cited By ~ 2

Author(s):

D. Vidaurre ◽

A. Llera ◽

S.M. Smith ◽

M.W. Woolrich

Keyword(s):

Functional Connectivity ◽

Network Architecture ◽

Brain Networks ◽

Structural Data ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Human Connectome Project ◽

Brain Data ◽

Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

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Interacting Brain Networks Underlying Creative Cognition and Artistic Performance

10.1093/oxfordhb/9780190464745.013.10 ◽

2018 ◽

Author(s):

Roger E. Beaty ◽

Rex E. Jung

Keyword(s):

Brain Networks ◽

Region Of Interest ◽

Network Connectivity ◽

Brain Regions ◽

Brain Network ◽

Potential Interaction ◽

Significant Advance ◽

Dynamic Interactions ◽

Creative Cognition ◽

Brain Network Connectivity

Cognitive neuroscience research has begun to address the potential interaction of brain networks supporting creativity by employing new methods in brain network science. Network methods offer a significant advance compared to individual region of interest studies due to their ability to account for the complex and dynamic interactions among discrete brain regions. As this chapter demonstrates, several recent studies have reported a remarkably similar pattern of brain network connectivity across a range of creative tasks and domains. In general, such work suggests that creative thought may involve dynamic interactions, primarily between the default and control networks, providing key insights into the roles of spontaneous and controlled processes in creative cognition. The chapter summarizes this emerging body of research and proposes a framework designed to account for the joint influence of controlled and spontaneous thought processes in creativity.

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Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

10.1101/2021.01.14.426756 ◽

2021 ◽

Author(s):

Guoqiang Hu ◽

Huanjie Li ◽

Wei Zhao ◽

Yuxing Hao ◽

Zonglei Bai ◽

...

Keyword(s):

Spectral Clustering ◽

Large Scale ◽

Brain Networks ◽

Motor Task ◽

Component Analysis ◽

Brain Regions ◽

Brain Network ◽

Tensor Component ◽

Fmri Data ◽

Neural Responses

AbstractThe study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

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Multi-Subject Stochastic Blockmodels for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure

10.1101/672071 ◽

2019 ◽

Cited By ~ 2

Author(s):

Dragana M. Pavlović ◽

Bryan R. L. Guillaume ◽

Emma K. Towlson ◽

Nicole M. Y. Kuek ◽

Soroosh Afyouni ◽

...

Keyword(s):

Individual Differences ◽

Human Brain ◽

Cluster Structure ◽

Permutation Tests ◽

Brain Regions ◽

Brain Network ◽

Type I ◽

Network Cluster ◽

Diagnostic Status ◽

Stochastic Blockmodel

AbstractThere is great interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of a group average network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two novel extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects on cluster structure of individual differences on subject-level covariates. Multi-subject Stochastic Blockmodels (MS-SBM) can flexibly account for between-subject variability in terms of a homogenous or heterogeneous effect on connectivity of covariates such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on Wald, likelihood ratio and Monte Carlo permutation tests. We show that multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition. Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; N = 268 brain regions), we show that the Heterogeneous Stochastic Blockmodel estimates ‘core-on-modules’ architecture. The intra-block and inter-block connection weights vary between individual participants and can be modelled as a logistic function of subject-level covariates like age or diagnostic status. Multi-subject Stochastic Blockmodels are likely to be useful tools for statistical analysis of individual differences in human brain graphs and other networks whose prior cluster structure needs to be estimated from the data.

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Towards an information theoretical description of communication in brain networks

Network Neuroscience ◽

10.1162/netn_a_00185 ◽

2021 ◽

pp. 1-23

Author(s):

Enrico Amico ◽

Kausar Abbas ◽

Duy Anh Duong-Tran ◽

Uttara Tipnis ◽

Meenusree Rajapandian ◽

...

Keyword(s):

Information Transfer ◽

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Description ◽

Structural Topology ◽

Communication Dynamics ◽

The Brain ◽

The Way

Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: Path Processing Score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); Path Broadcasting Strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main “communication regimes” of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; Visual and somato-motor cortices act as multi-channel transducted broadcasters. This work paves the way towards the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.

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