scholarly journals Characterisation of Resting Brain Network Topologies across the Human Lifespan with Magnetoencephalogram Recordings: A Phase Slope Index and Granger Causality Comparison Study

2017 ◽  
Author(s):  
Elizabeth Shumbayawonda ◽  
Alberto Fernández ◽  
Javier Escudero ◽  
Michael Pycraft Hughes ◽  
Daniel Abásolo
Keyword(s):  
Granger Causality ◽  
Brain Network ◽  
Comparison Study ◽  
Phase Slope ◽  
Human Lifespan ◽  
Network Topologies

Brain network for passive word listening as evaluated with ICA and Granger causality

2007 ◽  
Vol 72 (4-6) ◽  
pp. 284-292 ◽  
Author(s):  
A. Londei ◽  
A. D’Ausilio ◽  
D. Basso ◽  
C. Sestieri ◽  
C. Del Gratta ◽  
...  
Keyword(s):  
Granger Causality ◽  
Brain Network

scholarly journals Identifying Brain Network Topology Changes in Task Processes and Psychiatric Disorders

2019 ◽  
Author(s):  
Paria Rezaeinia ◽  
Kim Fairley ◽  
Piya Pal ◽  
François G. Meyer ◽  
R. McKell Carter
Keyword(s):  
Psychiatric Disorders ◽  
Task Performance ◽  
Network Topology ◽  
Brain Network ◽  
Imaging Data ◽  
Hierarchical Processing ◽  
Functional Brain Network ◽  
Topology Changes ◽  
The World ◽  
Network Topologies

ABSTRACTA central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting-state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting-scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders.AUTHOR SUMMARYOur mental lives are made up of a series of predictions about the world calculated by our brains. The calculations that produce these predictions are a result of how areas in our brain interact. Measures based on graph representations can make it clear what information can be combined and therefore help us better understand the computations the brain is performing. We make use of cutting-edge techniques that overcome a number of previous limitations to identify specific shapes in the functional brain network. These shapes are similar to hierarchical processing streams which play a fundamental role in cognitive neuroscience. The importance of these structures and the technique is highlighted by how they change under different task constraints and in individuals diagnosed with psychiatric disorders.

scholarly journals Assessing The Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher’s Choice Paths

2021 ◽  
Author(s):  
Stavros I. Dimitriadis
Keyword(s):  
Spatial Scales ◽  
Single Layer ◽  
Brain Network ◽  
Brain Disorders ◽  
Sources Of Information ◽  
Multilayer Networks ◽  
Network Graph ◽  
Graph Theoretic ◽  
Functional Relationships ◽  
Network Topologies

AbstractThere is a growing interest in the neuroscience community on the advantages of multimodal neuroimaging modalities. Functional and structural interactions between brain areas can be represented as a network (graph) allowing us to employ graph-theoretic tools in multiple research directions. Researchers usually treated brain networks acquired from different modalities or different frequencies separately. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. We can incorporate this information from different modalities (multi-modal case), from different frequencies (multi-frequency case), or a single modality following a dynamic functional connectivity analysis (multi-layer,dynamic case). Researchers already used multi-layer networks to model brain disorders, to detect key hubs related to a specific function, to reveal structural-functional relationships, and to define more precise connectomic biomarkers related to brain disorders. However, the construction of a multilayer network depends on the selection of multiple preprocessing steps that can affect the final network topology. Here, we analyzed the fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total). We focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, we untangled specific combinations of researchers’ choices that yield repeatable topologies, giving us the chance to recommend best practices over consistent topologies.

scholarly journals A single-mode associates global patterns of brain network structure and behavior across the human lifespan

2020 ◽  
Author(s):  
Brent McPherson ◽  
Franco Pestilli
Keyword(s):  
Single Mode ◽  
Brain Network ◽  
Individual Variability ◽  
Brain Functions ◽  
Human Lifespan ◽  
Structural Connections ◽  
Global Patterns ◽  
Brain And Behavior ◽  
And Behavior ◽  
Behavioral Domains

AbstractMultiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r=0.5808). Individual variability within the single association-axis well predicted the subjects age (r=0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain networks systems and behavioral domains. Results show that a global process of human aging is well captured by multivariate data fusion approach. [147]

scholarly journals Detecting brain connectivity changes during seizures using Granger causality measures on scalp EEG

2015 ◽  
Author(s):  
Christos Koutlis
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Window ◽  
Brain Connectivity ◽  
Characteristic Curve ◽  
Multivariate Time Series ◽  
Brain Regions ◽  
Brain Network ◽  
Network Feature ◽  
The Brain

In this work the objective is to detect brain connectivity changes during epileptic seizures using methods of multivariate time series analysis on scalp multi-channel EEG. Different brain regions represented by the electrode positions interact in terms of Granger causality and these directed connections formulate the brain network at a certain time window. The numerous proposed network features are believed to capture the information of many network characteristics. The ability of a single network feature of the brain network to detect the transition of brain activity from preictal to ictal is examined. The connectivity of the brain is estimated by 13 Granger causality indices on 7 epochs from multivariate time series (19 channels per epoch) at 15 time windows of 20 seconds (5 min in total) before seizure and during the seizure. The characteristics of the networks are estimated by 379 network features. Finally, the discrimination task (preictal vs. ictal) for each network feature is evaluated by the area under receiver operating characteristic curve (AUROC).

Brain network analysis of hand motor execution and imagination based on Granger causality

2019 ◽  
Author(s):  
Jiaxin Zhang ◽  
Rui Xu ◽  
Abdelkader Nasreddine Belkacem ◽  
Duk Shin ◽  
Kun Wang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Granger Causality ◽  
Brain Network ◽  
Motor Execution

scholarly journals Quantifying the Variability in Resting-State Networks

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 882 ◽  
Author(s):  
Isaura Oliver ◽  
Jaroslav Hlinka ◽  
Jakub Kopal ◽  
Jörn Davidsen
Keyword(s):  
Resting State ◽  
Similarity Measures ◽  
Brain Network ◽  
Brain Atlas ◽  
Resting State Networks ◽  
Brain Organization ◽  
Hyperactivity Disorder ◽  
Network Topologies ◽  
Open Question ◽  
Human Brains

Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long—even if the whole brain network topology between different individuals is variable.

scholarly journals Characterising brain network topologies: A dynamic analysis approach using heat kernels

NeuroImage ◽  
2016 ◽  
Vol 141 ◽  
pp. 490-501 ◽  
Author(s):  
A.W. Chung ◽  
M.D. Schirmer ◽  
M.L. Krishnan ◽  
G. Ball ◽  
P. Aljabar ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Brain Network ◽  
Heat Kernels ◽  
Analysis Approach ◽  
Network Topologies

Acrophobia Quantified by EEG Based on CNN Incorporating Granger Causality

2020 ◽  
pp. 2050069
Author(s):  
Fo Hu ◽  
Hong Wang ◽  
Qiaoxiu Wang ◽  
Naishi Feng ◽  
Jichi Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Altitude ◽  
Granger Causality ◽  
Brain Network ◽  
Classification Performance ◽  
Classification Task ◽  
Detection Methods ◽  
Practical Applications ◽  
Functional Brain Network

The aim of this study is to quantify acrophobia and provide safety advices for high-altitude workers. Considering that acrophobia is a fuzzy quantity that cannot be accurately evaluated by conventional detection methods, we propose a comprehensive solution to quantify acrophobia. Specifically, this study simulates a virtual reality environment called High-altitude Plank Walking Challenge, which provides a safe and controlled experimental environment for subjects. Besides, a method named Granger Causality Convolutional Neural Network (GCCNN) combining convolutional neural network and Granger causality functional brain network is proposed to analyze the subjects’ noninvasive scalp EEG signals. Here, the GCCNN method is used to distinguish the subjects with severe acrophobia, moderate acrophobia, and no acrophobia in a three-class classification task or no acrophobia and acrophobia in a two-class classification task. Compared with the mainstream methods, the GCCNN method achieves better classification performance, with an accuracy of 98.74% for the two-class classification task (no acrophobia versus acrophobia) and of 98.47% for the three-class classification task (no acrophobia versus moderate acrophobia versus severe acrophobia). Consequently, our proposed GCCNN method can provide more accurate quantitative results than the comparative methods, making it to be more competitive in further practical applications.

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