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Journal ArticleDOI

Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

06 Aug 2012-Brain connectivity (Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA)-Vol. 2, Iss: 3, pp 125-141
TL;DR: The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fc MRI measures.
Abstract: Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...
Citations
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Journal ArticleDOI
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 "Conn: A Functional Connectivity Too..."

  • ...Although DPARSF has been widely used (including more recent R-fMRI pipelines CONN (Whitfield-Gabrieli and Nieto-Castanon, 2012) and FATCAT (Taylor and Saad, 2013)), the original DPARSF has fallen behind with some recent developments of R-fMRI methodologies, particularly in relation to R-fMRI methodological challenges....

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  • ...Although DPARSF has been widely used (including more recent R-fMRI pipelines CONN (Whitfield-Gabrieli and Nieto-Castanon, 2012) and FATCAT (Taylor and Saad, 2013)), the original DPARSF has fallen behind with some recent developments of R-fMRI methodologies, particularly in relation to R-fMRI…...

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Journal ArticleDOI
TL;DR: The results suggest that anticor Relations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.

922 citations

Journal ArticleDOI
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 "Conn: A Functional Connectivity Too..."

  • ...…graph-based topological analyses of brain networks, such as the Brain Connectivity Toolbox (BCT; Rubinov and Sporns, 2010), eConnectome (He et al., 2011), CONN (Whitfield-Gabrieli and Nieto-Castanon, 2012), Graph-Analysis Toolbox (GAT; Hosseini et al., 2012) and GraphVar (Kruschwitz et al., 2015)....

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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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  • ...In response, several freely available toolboxes have been developed to implement and visualize graph-based topological analyses of brain networks, such as the Brain Connectivity Toolbox (BCT; Rubinov and Sporns, 2010), eConnectome (He et al., 2011), CONN (Whitfield-Gabrieli and Nieto-Castanon, 2012), Graph-Analysis Toolbox (GAT; Hosseini et al., 2012) and GraphVar (Kruschwitz et al., 2015)....

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Journal ArticleDOI
TL;DR: A whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition is identified, suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.
Abstract: People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

490 citations

Journal ArticleDOI
TL;DR: It is found that although different types of brain stimulation are applied in different locations, targets used to treat the same disease most often are nodes within the same brain network as defined by resting-state functional-connectivity MRI.
Abstract: Brain stimulation, a therapy increasingly used for neurological and psychiatric disease, traditionally is divided into invasive approaches, such as deep brain stimulation (DBS), and noninvasive approaches, such as transcranial magnetic stimulation. The relationship between these approaches is unknown, therapeutic mechanisms remain unclear, and the ideal stimulation site for a given technique is often ambiguous, limiting optimization of the stimulation and its application in further disorders. In this article, we identify diseases treated with both types of stimulation, list the stimulation sites thought to be most effective in each disease, and test the hypothesis that these sites are different nodes within the same brain network as defined by resting-state functional-connectivity MRI. Sites where DBS was effective were functionally connected to sites where noninvasive brain stimulation was effective across diseases including depression, Parkinson9s disease, obsessive-compulsive disorder, essential tremor, addiction, pain, minimally conscious states, and Alzheimer’s disease. A lack of functional connectivity identified sites where stimulation was ineffective, and the sign of the correlation related to whether excitatory or inhibitory noninvasive stimulation was found clinically effective. These results suggest that resting-state functional connectivity may be useful for translating therapy between stimulation modalities, optimizing treatment, and identifying new stimulation targets. More broadly, this work supports a network perspective toward understanding and treating neuropsychiatric disease, highlighting the therapeutic potential of targeted brain network modulation.

477 citations

References
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Journal ArticleDOI
04 Jun 1998-Nature
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


Additional excerpts

  • ...The toolbox also computes several graph theoretical measures (Achard and Bullmore, 2007; Bullmore and Sporns, 2009; Latora and Marchiori, 2001; Watts and Strogatz, 1998) characterizing structural properties of the estimated ROI-toROI functional connectivity networks, and allows users to perform…...

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Journal ArticleDOI
TL;DR: A baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF is identified, suggesting the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.
Abstract: A baseline or control state is fundamental to the understanding of most complex systems. Defining a baseline state in the human brain, arguably our most complex system, poses a particular challenge. Many suspect that left unconstrained, its activity will vary unpredictably. Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF. The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered by flowing blood and is remarkably uniform in the awake but resting state (e.g., lying quietly with eyes closed). Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors. We used quantitative metabolic and circulatory measurements from positron-emission tomography to obtain the OEF regionally throughout the brain. Areas of activation were conspicuous by their absence. All significant deviations from the mean hemisphere OEF were increases, signifying deactivations, and resided almost exclusively in the visual system. Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this baseline, during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI. These decreases suggest the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.

10,708 citations


"Conn: A Functional Connectivity Too..." refers background or methods in this paper

  • ...We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures....

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  • ...Such networks were initially discovered for the motor system (Biswal et al., 1995), but have also been discovered for both task-positive and task-negative (i.e., default, Raichle et al., 2001) neural systems (Fox et al., 2005; Fransson, 2005; Kelly et al., 2008; Uddin et al., 2009)....

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Journal ArticleDOI
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


"Conn: A Functional Connectivity Too..." refers methods in this paper

  • ...The toolbox also computes several graph theoretical measures (Achard and Bullmore, 2007; Bullmore and Sporns, 2009; Latora and Marchiori, 2001; Watts and Strogatz, 1998) characterizing structural properties of the estimated ROI-toROI functional connectivity networks, and allows users to perform…...

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  • ...This analysis uses the same PCC seed area as the previous seed-to-voxel analysis, and estimates the ROI-to-ROI functional connectivity (bivariate correlation measure) between this seed and a set of 84 ROIs defining the Brodmann areas (talairach atlas; Lancaster et al., 2000)....

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Journal ArticleDOI
TL;DR: It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
Abstract: An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.

8,766 citations


"Conn: A Functional Connectivity Too..." refers background or methods in this paper

  • ...…(ICA) (e.g., Beckmann et al., 2005; Calhoun et al., 2001, 2004), seed-driven functional connectivity magnetic resonance imaging (fcMRI) (e.g., Biswal et al., 1995; Fox et al., 2005; Greicius et al., 2003), and psychophysiological interactions used to characterize activation in a particular…...

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  • ...Such networks were initially discovered for the motor system (Biswal et al., 1995), but have also been discovered for both task-positive and task-negative (i.e., default, Raichle et al., 2001) neural systems (Fox et al., 2005; Fransson, 2005; Kelly et al., 2008; Uddin et al., 2009)....

    [...]

  • ...…connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood…...

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  • ...…analytical approaches toward analyzing resting state functional connectivity (RSFC) data are ICA (e.g., Beckmann et al., 2005, 2009; Greicius et al., 2007; Stevens et al., 2009) and seed-driven RSFC (e.g., Biswal et al., 1995; Castellanos et al., 2008; Greicius et al., 2003; Fox et al., 2005)....

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  • ...Low-frequency resting state networks ( < 0.1 Hz) reveal coherent, spontaneous fluctuations that delineate the functional architecture of the human brain (Biswal et al., 1995, 2010; Buckner et al., 2008; Fox et al., 2005; Fox and Raichle, 2007)....

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Journal ArticleDOI
TL;DR: Past observations are synthesized to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment, and for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.
Abstract: Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations These two subsystems converge on important nodes of integration including the posterior cingulate cortex The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer’s disease

8,448 citations

Trending Questions (1)
What are the uses of CONN toolbox in ICA analysis when studying clinical populations?

The paper does not specifically mention the use of the CONN toolbox in ICA analysis when studying clinical populations.