scholarly journals Research on Differential Brain Networks before and after WM Training under Different Frequency Band Oscillations

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yin Tian ◽  
Huishu Zhou ◽  
Huiling Zhang ◽  
Tianhao Li
Keyword(s):  
Graph Theory ◽  
Cognitive Processing ◽  
Frequency Band ◽  
Optimal Path ◽  
Brain Regions ◽  
Brain Network ◽  
Eeg Coherence ◽  
Whole Brain ◽  
Functional Connections ◽  
Before And After

Previous studies have shown that different frequency band oscillations are associated with cognitive processing such as working memory (WM). Electroencephalogram (EEG) coherence and graph theory can be used to measure functional connections between different brain regions and information interaction between different clusters of neurons. At the same time, it was found that better cognitive performance of individuals indicated stronger small-world characteristics of resting-state WM networks. However, little is known about the neural synchronization of the retention stage during ongoing WM tasks (i.e., online WM) by training on the whole-brain network level. Therefore, combining EEG coherence and graph theory analysis, the present study examined the topological changes of WM networks before and after training based on the whole brain and constructed differential networks with different frequency band oscillations (i.e., theta, alpha, and beta). The results showed that after WM training, the subjects’ WM networks had higher clustering coefficients and shorter optimal path lengths than before training during the retention period. Moreover, the increased synchronization of the frontal theta oscillations seemed to reflect the improved executive ability of WM and the more mature resource deployment; the enhanced alpha oscillatory synchronization in the frontoparietal and fronto-occipital regions may reflect the enhanced ability to suppress irrelevant information during the delay and pay attention to memory guidance; the enhanced beta oscillatory synchronization in the temporoparietal and frontoparietal regions may indicate active memory maintenance and preparation for memory-guided attention. The findings may add new evidence to understand the neural mechanisms of WM on the changes of network topological attributes in the task-related mode.

scholarly journals Dynamic reorganization of brain functional networks during cognition

2014 ◽  
Author(s):  
Michał Bola ◽  
Bernhard Sabel
Keyword(s):  
Graph Theory ◽  
Cognitive Processing ◽  
Phase Synchronization ◽  
Brain Regions ◽  
Cognitive Networks ◽  
Strong Interactions ◽  
Functional Networks ◽  
Global Integration ◽  
Functional Connections ◽  
Cognitive Operations

How cognition emerges from neural dynamics? The dominant hypothesis states that interactions among distributed brain regions through phase synchronization give basis for cognitive processing. Such phase-synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to perform specific cognitive operations. But unlike resting-state networks, the complex organization of transient cognitive networks is typically not characterized within the graph theory framework. Thus, it is not known whether cognitive processing merely changes strength of functional connections or, conversely, requires qualitatively new topological arrangements of functional networks. To address this question, we recorded high-density EEG when subjects performed a visual discrimination task and conducted and event-related network analysis (ERNA) where source-space weighted functional networks were characterized with graph measures. We revealed rapid, transient, and frequency-specific reorganization of the network?s topology during cognition. Specifically, cognitive networks were characterized by strong clustering, low modularity, and strong interactions between hub-nodes. Our findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the ?network fingerprint? of cognition. Such reorganization patterns might facilitate global integration of information and provide a substrate for a ?global workspace? necessary for cognition and consciousness to occur. Thus, characterizing topology of the event-related networks opens new vistas to interpret cognitive dynamics in the broader conceptual framework of graph theory.

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.

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

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.

scholarly journals The dynamic connectome of speech control

2021 ◽  
Vol 376 (1836) ◽  
Author(s):  
Davide Valeriani ◽  
Kristina Simonyan
Keyword(s):  
Neural Network ◽  
Effective Connectivity ◽  
Vocal Learning ◽  
Brain Regions ◽  
Brain Network ◽  
Motor Output ◽  
Network Activity ◽  
Imaging Data ◽  
Whole Brain ◽  
Cost Efficient

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output. This article is part of the theme issue ‘Vocal learning in animals and humans’.

scholarly journals Heterogeneity of EEG resting-state brain networks in absolute pitch

2020 ◽  
Author(s):  
Marielle Greber ◽  
Carina Klein ◽  
Simon Leipold ◽  
Silvano Sele ◽  
Lutz Jäncke
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Resting State ◽  
Large Scale ◽  
Absolute Pitch ◽  
Brain Regions ◽  
Brain Network ◽  
Higher Order ◽  
Neural Basis ◽  
Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

scholarly journals neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

2021 ◽  
Author(s):  
Caglar Cakan ◽  
Nikola Jajcay ◽  
Klaus Obermayer
Keyword(s):  
Empirical Data ◽  
Brain Region ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain ◽  
Neural Mass ◽  
Set Up ◽  
Function Relationship ◽  
Relationship Of

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

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

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.

scholarly journals Paired test of matrix graphs and brain connectivity analysis

Biostatistics ◽  
2019 ◽  
Author(s):  
Yuting Ye ◽  
Yin Xia ◽  
Lexin Li
Keyword(s):  
Multiple Testing ◽  
Brain Connectivity ◽  
Estimation Error ◽  
Testing Procedure ◽  
Brain Regions ◽  
Brain Network ◽  
Test Statistic ◽  
Multiple Testing Procedure ◽  
Before And After ◽  
Graph Inference

Summary Inferring brain connectivity network and quantifying the significance of interactions between brain regions are of paramount importance in neuroscience. Although there have recently emerged some tests for graph inference based on independent samples, there is no readily available solution to test the change of brain network for paired and correlated samples. In this article, we develop a paired test of matrix graphs to infer brain connectivity network when the groups of samples are correlated. The proposed test statistic is both bias corrected and variance corrected, and achieves a small estimation error rate. The subsequent multiple testing procedure built on this test statistic is guaranteed to asymptotically control the false discovery rate at the pre-specified level. Both the methodology and theory of the new test are considerably different from the two independent samples framework, owing to the strong correlations of measurements on the same subjects before and after the stimulus activity. We illustrate the efficacy of our proposal through simulations and an analysis of an Alzheimer’s Disease Neuroimaging Initiative dataset.

EEG frequency band sloreta sources during mental arithmetic compared to resting

2011 ◽  
Vol 26 (S2) ◽  
pp. 945-945 ◽  
Author(s):  
A. Theodoropoulou ◽  
S. Tei ◽  
D. Lehmann ◽  
P.L. Faber ◽  
F. Schlegel ◽  
...  
Keyword(s):  
Frequency Band ◽  
Current Density ◽  
Mental Arithmetic ◽  
Brain Regions ◽  
Gamma Activity ◽  
Simple Arithmetic ◽  
Frequency Bands ◽  
Head Surface ◽  
Before And After ◽  
Surface Eeg

IntroductionArithmetic reportedly involves left parietal areas.ObjectivesTo test this in independent groups of healthy persons.AimsWhich brain regions are activated / inhibited during mental arithmetic compared to task-free resting?MethodsWe examined four independent groups of healthy adults (N = 15, 14, 14, 23, respectively) during simple arithmetic (continuous subtraction of 7) and task-free resting before and after arithmetic, all with closed eyes. Multichannel head surface EEG (19–58 channels) was continually recorded, then recomputed (using sLORETA functional tomography) into current density for 6239 cortical voxels, for each of the eight EEG frequency bands (delta through gamma, 1.5–44 Hz). Pre- and post-arithmetic resting was averaged. Using paired t-tests, frequency band-wise normalized and log-transformed current density was compared between arithmetic and resting for each group. The resulting p-values were combined across groups using Fisher’s combination procedure. For each frequency band, sLORETA voxels differing between conditions at Fisher’s (across groups) p < 0.05 were computed into centers of gravity separately for increased and decreased activation.ResultsActivity that was stronger during arithmetic compared to resting had gravity centers in midline anterior regions for slow frequency bands (delta, theta, alpha-1) and in right posterior regions for fast frequency bands (alpha-2 through gamma). Activity that was weaker during arithmetic compared to resting was centered around left parietal areas for all eight frequency bands.ConclusionsThe results suggest that arithmetic compared to resting involves frontal inhibition coupled with increased right parietal activation, and left parietal reduced facilitatory and reduced inhibitory activity.

scholarly journals Large-scale exploration of whole-brain structural connectivity in anorexia nervosa: alterations in the connectivity of frontal and subcortical networks

2021 ◽  
Author(s):  
E. Caitlin Lloyd ◽  
Karin E. Foerde ◽  
Alexandra F. Muratore ◽  
Natalie Aw ◽  
David Semanek ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Graph Theory ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Cross Sectional ◽  
Whole Brain ◽  
Subcortical Regions

AbstractBackgroundAnorexia nervosa (AN) is characterised by disturbances in cognition and behaviour surrounding eating and weight, which may relate to the structural connectivity of the brain that supports effective information processing and transfer.MethodsDiffusion-weighted MRI data acquired from female patients with AN (n = 148) and female healthy controls (HC; n = 119), aged 12-40 years, were combined across five cross-sectional studies. Probabilistic tractography was completed, and full cortex connectomes describing streamline counts between 84 brain regions generated and harmonised. The network-based statistic tested between-group differences in connectivity strength of brain subnetworks. Whole-brain connectivity of brain regions was indexed using graph theory tools, and compared between groups using multiple linear regression. Associations between structural connectivity variables that differed between groups, and illness severity markers, were explored amongst AN patients using multiple linear regression. Statistical models included age, motion, and study as covariates.OutcomesThe network-based statistic indicated AN patients, relative to HC, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < 0.05, FWE corrected). Graph theory analyses supported reduced connectivity of subcortical regions, and greater connectivity of left occipital cortex, in patients relative to HC (p < 0.05, permutation corrected). Reduced subcortical network connectivity was associated with lower BMI among the AN group.InterpretationStructural differences in subcortical and cortical networks are present in AN, and may reflect illness mechanisms.FundingGlobal Foundation for Eating Disorders; Klarman Family Foundation; Translating Duke Health Initiative; NIMH (MH099388, MH076195, MH110445, MH105452, MH079397, MH113737).

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