BrainNet Viewer: a network visualization tool for human brain connectomics.
TL;DR: This work has developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models, and helps researchers to visualize brain networks in an easy, flexible and quick manner.
Abstract: The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).
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TL;DR: The newly developed toolbox, DPABI, which was evolved from REST and DPARSF is introduced, designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies.
Abstract: Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.
2,179 citations
Cites methods from "BrainNet Viewer: a network visualiz..."
...The viewer also provides an interface to the surface displaying tool, BrainNet Viewer (Xia et al., 2013), thus producing a volume map and a corresponding surface map with the same color and range settings....
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...The surface maps were produced by BrainNet Viewer (Xia et al., 2013), through an interface in the DPABI Viewer....
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...The viewer also provides an interface to the surface displaying tool, BrainNet Viewer (Xia et al. 2013), thus producing a volume map and a corresponding surface map with the same color and range settings....
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...5 (surface maps were produced by BrainNet Viewer (Xia et al. 2013), through an interface in the DPABI Viewer), ALFF without standardization showed very low TRT reliability, which would prevent its usage....
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TL;DR: It is demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies.
Abstract: Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface; (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website (http://www.nitrc.org/projects/gretna/).
884 citations
Cites methods from "BrainNet Viewer: a network visualiz..."
...Specifically, we have previously developed PANDA (Cui et al., 2013) for the construction of structural brain networks based on diffusion imaging data and BrainNet Viewer (Xia et al., 2013) toolkits for the visualization of brain networks....
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...Software R-fMRI Preprocessing Network construction (static) Network construction (dynamic) Graph analysis Statistics Fle GUI Parallel computing Vis Website GRETNA X X X X X X X X × http//www.nitrc.org/projects/gretna/ BCT × × × X × × × × × https://sites.google.com/site/bctnet/ GAT × X × X X × X × X Not available PANDA × X × × × × X X × http//www.nitrc.org/projects/panda/ CONN X X × X X × X × X http//www.nitrc.org/projects/conn eConnectome × X × × × × X × X http://econnectome.umn.edu/ BrainNet Viewer × × × × × × X × X http://www.nitrc.org/projects/bnv/ GraphVar × X X X X X X × X http://www.nitrc.org/projects/graphvar/ Brainwaver × X × X × × × × X http://cran.r-project.org/web/packages/ brainwaver/ Fle, flexibility; GUI, graphical user interface; Vis, visualization....
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TL;DR: A systematic evaluation of 14 participant‐level confound regression methods for functional connectivity highlights the heterogeneous efficacy of existing methods, and suggests that different confounding regression strategies may be appropriate in the context of specific scientific goals.
790 citations
Cites methods from "BrainNet Viewer: a network visualiz..."
...3 (Wickham, 2009); brain renderings were prepared in BrainNet Viewer (Xia et al., 2013)....
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...All graphs were generated using ggplot2 in R version 3.2.3 (Wickham, 2009); brain renderings were prepared in BrainNet Viewer (Xia et al., 2013)....
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TL;DR: It is demonstrated that brain activity between multiple pairs of spatially distributed regions spontaneously fluctuates in and out of correlation over time in a globally coordinated manner, giving rise to sporadic intervals during which information can be efficiently exchanged between neuronal populations.
Abstract: Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.
699 citations
TL;DR: It is confirmed that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition.
Abstract: A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition. SIGNIFICANCE STATEMENT The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain9s ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.
524 citations
References
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
39,297 citations
TL;DR: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute was performed and it is believed that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain.
13,678 citations
"BrainNet Viewer: a network visualiz..." refers methods in this paper
...2) The brain network files, including node and edge files, are generated from various brain parcellations, such as Automated Anatomical Labeling (AAL, 90 regions, only cerebrum) [65], Brodmann areas (82 regions) [66], Harvard-Oxford Atlas (HOA, 112 regions) [67], regions of interest (ROIs) defined by Dosenbach et al....
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TL;DR: A review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB) on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data.
12,097 citations
"BrainNet Viewer: a network visualiz..." refers background or methods in this paper
...Notably, other atlases are generated from anatomical [66,69] or functional [26,67,68] parcellations....
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...2) The brain network files, including node and edge files, are generated from various brain parcellations, such as Automated Anatomical Labeling (AAL, 90 regions, only cerebrum) [65], Brodmann areas (82 regions) [66], Harvard-Oxford Atlas (HOA, 112 regions) [67], regions of interest (ROIs) defined by Dosenbach et al....
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TL;DR: In this article, the modularity of a network is expressed in terms of the eigenvectors of a characteristic matrix for the network, which is then used for community detection.
Abstract: Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
10,137 citations
TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
9,700 citations