Frequency-based brain networks: From a multiplex framework to a full multilayer description

We explore how to study dynamical interactions between brain regions by using functional multilayer networks whose layers represent different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as (i) a multilayer network, in which all brain regions can interact with each other at different frequency bands; and as (ii) a multiplex network, in which interactions between different frequency bands are allowed only within each brain region and not between them. We study the second-smallest eigenvalue λ2 of the combinatorial supra-Laplacian matrix of both the multiplex and multilayer networks, as λ2 has been used previously as an indicator of network synchronizability and as a biomarker for several brain diseases. We show that the heterogeneity of interlayer edge weights and, especially, the fraction of missing edges crucially modify the value of λ2, and we illustrate our results with both synthetic network models and real data obtained from resting-state magnetoencephalography. Our work highlights the differences between using a multiplex approach and a full multilayer approach when studying frequency-based multilayer brain networks.

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On the structural connectivity of large-scale models of brain networks at cellular level

Scientific Reports ◽

10.1038/s41598-021-83759-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Giuseppe Giacopelli ◽

Domenico Tegolo ◽

Emiliano Spera ◽

Michele Migliore

Keyword(s):

Large Scale ◽

Brain Networks ◽

Structural Connectivity ◽

Network Models ◽

Brain Regions ◽

Cellular Level ◽

Scale Model ◽

Mathematical Framework ◽

Underlying Mechanisms ◽

Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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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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Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

Computational Intelligence and Neuroscience ◽

10.1155/2013/149329 ◽

2013 ◽

Vol 2013 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Seth A. Herd ◽

Kai A. Krueger ◽

Trenton E. Kriete ◽

Tsung-Ren Huang ◽

Thomas E. Hazy ◽

...

Keyword(s):

Cognitive Process ◽

Network Models ◽

Brain Regions ◽

Human Cognition ◽

Neural Network Models ◽

Future Directions ◽

Error Learning ◽

Cognitive Actions ◽

Self Instruction ◽

The Brain

We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

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Quantification and selection of ictogenic zones in epilepsy surgery

10.1101/602490 ◽

2019 ◽

Author(s):

Petroula Laiou ◽

Eleftherios Avramidis ◽

Marinho A. Lopes ◽

Eugenio Abela ◽

Michael Müller ◽

...

Keyword(s):

Computational Models ◽

Brain Networks ◽

Network Models ◽

Combinatorial Problem ◽

Valuable Insight ◽

Potential Applicability ◽

Healthy Functioning ◽

The Brain ◽

Insight Into ◽

Selection Of

AbstractNetwork models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy.Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we develop a global optimisation approach to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy.

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Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

Frontiers in Neurology ◽

10.3389/fneur.2021.715455 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhibao Li ◽

Chong Liu ◽

Qiao Wang ◽

Kun Liang ◽

Chunlei Han ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Functional Connectivity ◽

Frequency Band ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Functional Brain ◽

Frequency Bands ◽

The Brain ◽

Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

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Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

Sensors ◽

10.3390/s21248305 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8305

Author(s):

César Covantes-Osuna ◽

Jhonatan B. López ◽

Omar Paredes ◽

Hugo Vélez-Pérez ◽

Rebeca Romo-Vázquez

Keyword(s):

Motor Imagery ◽

Brain Connectivity ◽

Single Layer ◽

Network Models ◽

Brain Regions ◽

Adaptive Threshold ◽

Brain Network ◽

Multilayer Network ◽

Brain Models ◽

The Brain

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

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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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Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

10.1101/421883 ◽

2018 ◽

Author(s):

Matthieu Gilson ◽

Nikos E. Kouvaris ◽

Gustavo Deco ◽

Jean-François Mangin ◽

Cyril Poupon ◽

...

Keyword(s):

Network Analysis ◽

Network Theory ◽

Random Network ◽

Effective Connectivity ◽

Structural Connectivity ◽

Real Data ◽

Brain Regions ◽

Network Activity ◽

Whole Brain ◽

The Brain

AbstractNeuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

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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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Dynamics of coherent activity between cortical areas defines a two-stage process of selective attention

10.1101/2020.02.09.940791 ◽

2020 ◽

Cited By ~ 1

Author(s):

E. Levichkina ◽

M. Kermani ◽

Y.B. Saalmann ◽

T.R. Vidyasagar

Keyword(s):

Low Frequency ◽

Brain Regions ◽

Saliency Map ◽

Top Down ◽

Frequency Bands ◽

Gamma Coherence ◽

Stage Process ◽

Frequency Coherence ◽

Object Features ◽

The Brain

ABSTRACTAnalysing a visual scene requires the brain to briefly keep in memory potentially relevant parts and then direct attention to their locations for detailed processing. To reveal the neuronal basis of the underlying working memory and top-down attention processes, we trained macaques to match two patterns presented with a delay between them. As the above processes are likely to require communication between brain regions, and the parietal cortex is involved in spatial attention, we simultaneously recorded neuronal activities from the interconnected parietal and middle temporal areas. We found that mnemonic information about the first pattern was retained in coherent oscillating activity between the areas in high-frequency bands, followed by coherent activity in low-frequency bands that mediate top-down attention on the relevant location.ONE SENTENCE SUMMARYGamma coherence allows retaining object features in a saliency map while lower frequency coherence facilitates attention.

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