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Journal Article•DOI•

PANDA: a pipeline toolbox for analyzing brain diffusion images

Zaixu Cui1, Suyu Zhong1, Pengfei Xu1, Yong He1, Gaolang Gong1 •
Beijing Normal University1
21 Feb 2013-Frontiers in Human Neuroscience (Frontiers Media SA)-Vol. 7, pp 42-42
TL;DR: A MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) is developed, expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.
Abstract: Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics (e.g., FA and MD) that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.
Citations
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Journal Article•DOI•
BrainNet Viewer: a network visualization tool for human brain connectomics.

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Mingrui Xia1, Jinhui Wang1, Yong He1•
Beijing Normal University1
04 Jul 2013-PLOS ONE
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/).

3,048 citations

Cites methods from "PANDA: a pipeline toolbox for analy..."

  • ...[59], eConnectome [60], Graph-Analysis Toolbox (GAT) [61], Pipeline for Analyzing braiN Diffusion imAges (PANDA) [62], NetworkX (http://networkx....

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  • ...With the advent of brain connectome studies, a number of toolboxes were developed to construct and analyze macro-scale brain networks, including PANDA, BCT, GAT, GRETNA, Brainwaver and eConnectome....

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  • ...Recently, several freely available toolkits for extracting brain network topological properties have emerged, including Brain Connectivity Toolbox (BCT) [59], eConnectome [60], Graph-Analysis Toolbox (GAT) [61], Pipeline for Analyzing braiN Diffusion imAges (PANDA) [62], NetworkX (http://networkx.lanl.gov/index.html), Brainwaver (http://cran.r-project.org/web/packages/brainwaver/index. html) and Graph-theoRETical Network Analysis toolkit (GRETNA, http://www.nitrc.org/projects/gretna/), which have greatly assisted with the investigation of the brain connectome....

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Journal Article•DOI•
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

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Jinhui Wang1, Jinhui Wang2, Xindi Wang1, Mingrui Xia1, Xuhong Liao1, Alan C. Evans3, Yong-Min He1, Yong-Min He3 •
McGovern Institute for Brain Research1, Hangzhou Normal University2, Montreal Neurological Institute and Hospital3
30 Jun 2015-Frontiers in Human Neuroscience
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 "PANDA: a pipeline toolbox for analy..."

  • ...Note: flexibility is determined according to whether a toolbox provides options regarding at least three of the following factors: network node, network connectivity, network connectivity member, network type and thresholding procedure. connectivity networks that are derived from various toolboxes (e.g., PANDA), data modalities (e.g., EEG/MEG, fNIRS and MRI), species (e.g., humans, monkey and cat) and research fields (e.g., social networks and transportation networks)....

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  • ...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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  • ...For example, the structural brain connectivity matrices in humans or macaques that are obtained from the PANDA software (Cui et al., 2013) or the CoCoMac database4 can be entered into the module for graph analysis....

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Journal Article•DOI•
White matter structural connectivity underlying semantic processing: evidence from brain damaged patients

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Zaizhu Han1, Yujun Ma1, Gaolang Gong1, Yong He2, Alfonso Caramazza3, Alfonso Caramazza4, Yanchao Bi1 •
Beijing Normal University1, McGovern Institute for Brain Research2, University of Trento3, Harvard University4
01 Oct 2013-Brain
TL;DR: The lesion volume and fractional anisotropy value of the left inferior fronto-occipital fasciculus, left anterior thalamic radiation, and left uncinate Fasciculus significantly correlated with severity of impairment in all three semantic tasks, providing direct evidence for the anatomical skeleton of the semantic network.
Abstract: Widely distributed brain regions in temporal, parietal and frontal cortex have been found to be involved in semantic processing, but the anatomical connections supporting the semantic system are not well understood. In a group of 76 right-handed brain-damaged patients, we tested the relationship between the integrity of major white matter tracts and the presence of semantic deficits. The integrity of white matter tracts was measured by percentage of lesion voxels obtained in structural imaging and mean fractional anisotropy values obtained in diffusion tensor imaging. Semantic deficits were assessed by jointly considering the performance on three semantic tasks that vary in the modalities of input (visual and auditory stimuli) and output (oral naming and associative judgement). We found that the lesion volume and fractional anisotropy value of the left inferior fronto-occipital fasciculus, left anterior thalamic radiation, and left uncinate fasciculus significantly correlated with severity of impairment in all three semantic tasks. These associations remained significant even when we controlled for a wide range of potential confounding variables, including overall cognitive state, whole lesion volume, or type of brain damage. The effects of these three white matter tracts could not be explained by potential involvement of relevant grey matter, and were (relatively) specific to object semantic processing, as no correlation with performance on non-object semantic control tasks (oral repetition and number processing tasks) was observed. These results underscore the causal role of left inferior fronto-occipital fasciculus, left anterior thalamic radiation, and left uncinate fasciculus in semantic processing, providing direct evidence for (part of) the anatomical skeleton of the semantic network.

145 citations

Cites methods from "PANDA: a pipeline toolbox for analy..."

  • ...We then executed the following steps using a pipeline tool, PANDA (Cui et al., 2013) (http://www. at B eijing N orm al U niversity L ibrary on Septem ber 8, 2013 http://brain.oxfordjournals.org/ D ow nloaded from nitrc.org/projects/panda/), BET: skull removal; Eddycorrect: correction of eddy current distortion; DTIFIT, build diffusion tensor models....

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  • ...We then executed the following steps using a pipeline tool, PANDA (Cui et al., 2013) (http://www. at B eijing N orm al U niversity L ibrary on Septem ber 8, 2013 http://brain.oxfordjournals.org/ D ow nloaded from nitrc.org/projects/panda/), BET: skull removal; Eddycorrect: correction of eddy…...

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Atlas-based analysis of neurodevelopment from infancy to adulthood using Diffusion Tensor Imaging

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Andreia V. Faria, Jia Zhang, Kenichi Oishi, Hangyi Jiang, Kazi Akhter, Koji Sakai, Laurent Hermoye, Michael I. Miller, P.C.M. van Zijl, Susumu Mori, Kennedy Krieger 
01 Jan 2009
TL;DR: Atlas-based tools for time-dependent quantitative image analysis are developed to characterize the anatomical changes that occur from 2years of age to adulthood, and the brainstem anatomy of cerebral palsy patients was evaluated and the altered anatomy was delineated.
Abstract: METHODS: 26 children and 9 healthy adult were included in this study. DTI were acquired using a 1.5 T scanner with b=700 s/mm 2 . After linear affine transformation, dual-contrast LDDMM was performed to register each subject’s data to the JHU atlas using FA and b0 images simultaneously. Using the parcellation map in the atlas, the brain was automatically segmented to 176 regions. In each region, following parameters were measured: size, fractional anisotropy (FA), apparent coefficient diffusion (ADC), parallel diffusivity ( ||, the major eigenvalue) and perpendicular diffusivity ( the average of the smallest eigenvalues). Relative volume of each region was obtained by dividing the volume of each region by the total brain volume. Age related changes in each region were investigated using linear and polynomial regression. We considered significant p value less than 0.05 after correction for multiple comparisons by setting the false discovery ratio (FDR). RESULTS and DISCUSSION: Whole brain analysis: We observed linear increase of the whole brain, white matter and CSF volumes with age, and a tendency to decrease cortex volume in agreement with previous studies. Regional analysis: In the Atlas-based analysis, most regions have relationship with age. However the extent of the change varies for relative size, FA and ADC and they have different ‘velocities’ of changes, represented by the slopes (Fig. 1) The relative size of white matter and subcortical gray matter increased with age while most part of cortical areas decreased (except for the amygdala and the hippocampal cortex), although not significant. Fig. 2 shows the results of the statistical analysis of the regional size changes and time-dependency plots for two representative areas. The change in the cortex is modulated by an inverted U-shape. This type of curve was described before in higher-level association cortical areas, recognized by slow maturation. Positive relation between FA and age was found in the subcortical WM as well as the thalamus and the anterior part of corona radiata (Fig. 3). Both perpendicular and parallel diffusion decreased in many white matter regions, indicating ongoing myelinization process and/or increasing compactness of axonal bundles. In the brainstem, there were clear increases in the relative size and FA and decrease in perpendicular diffusivity of the corticospinal tract (CST). Significant shortening of the central conduction time during childhood and adolescence has been observed, functionally supporting that myelination of CST fibers in this phase. The decrease of the perpendicular diffusivity, thus, may reflect the extension of the myelination.

142 citations

Journal Article•DOI•
Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.

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Zaixu Cui1, Zhichao Xia1, Mengmeng Su1, Hua Shu1, Gaolang Gong1 •
McGovern Institute for Brain Research1
01 Apr 2016-Human Brain Mapping
TL;DR: Findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well‐recognized reading system in Dyslexia.
Abstract: Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been observed in dyslexic children. However, it remains unknown if developmental dyslexia affects the human brain WM in a multidimensional manner. Being a natural tool for evaluating this hypothesis, the multivariate machine learning approach was applied in this study to compare 28 school-aged dyslexic children with 33 age-matched controls. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging were acquired to extract five multitype WM features at a regional level: white matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) classifier achieved an accuracy of 83.61% using these MRI features to distinguish dyslexic children from controls. Notably, the most discriminative features that contributed to the classification were primarily associated with WM regions within the putative reading network/system (e.g., the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, thalamocortical projections, and corpus callosum), the limbic system (e.g., the cingulum and fornix), and the motor system (e.g., the cerebellar peduncle, corona radiata, and corticospinal tract). These results were well replicated using a logistic regression classifier. These findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well-recognized reading system in dyslexia. Finally, the discriminating results demonstrated a potential of WM neuroimaging features as imaging markers for identifying dyslexic individuals.

137 citations

Cites methods from "PANDA: a pipeline toolbox for analy..."

  • ...Processing of the diffusion MRI dataset was implemented using PANDA (http://www.nitrc.org/projects/ panda/), which is a pipeline toolbox for diffusion MRI analysis [Cui et al., 2013]....

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  • ...org/projects/ panda/), which is a pipeline toolbox for diffusion MRI analysis [Cui et al., 2013]....

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References
More filters
Journal Article•DOI•
Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain

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Nathalie Tzourio-Mazoyer1, B. Landeau1, D. Papathanassiou1, Fabrice Crivello1, Octave Etard1, Nicolas Delcroix1, Bernard Mazoyer2, Marc Joliot1 •
University of Paris1, Institut Universitaire de France2
01 Jan 2002-NeuroImage
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

"PANDA: a pipeline toolbox for analy..." refers background in this paper

  • ...Currently, PANDA provides two well-defined atlases: the Automated Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002) atlas and the Harvard-Oxford atlas (HOA) (http://www. cma.mgh.harvard.edu/fslatlas.html)....

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  • ...Each row or column of the matrix represents a cortical region of the AAL template (Gong et al., 2009a,b)....

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Journal Article•DOI•
Advances in functional and structural MR image analysis and implementation as FSL.

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Stephen M. Smith1, Mark Jenkinson1, Mark W. Woolrich1, Mark W. Woolrich2, Christian F. Beckmann1, Behrens Tej.1, Heidi Johansen-Berg1, Peter R. Bannister1, M De Luca1, Ivana Drobnjak1, D E Flitney1, Rami K. Niazy1, J Saunders1, J Vickers1, Yongyue Zhang1, N. De Stefano2, J M Brady3, Paul M. Matthews1 •
John Radcliffe Hospital1, University of Siena2, University of Oxford3
01 Jan 2004-NeuroImage
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

"PANDA: a pipeline toolbox for analy..." refers methods in this paper

  • ...A number of processing functions from FSL (Smith et al., 2004), Pipeline System for Octave and Matlab (PSOM) (Bellec et al....

    [...]

  • ...Currently, a number of packages, such as FMRIB Software Library (FSL) (Smith et al., 2004) and DTI-Studio (Jiang et al....

    [...]

  • ...A number of processing functions from FSL (Smith et al., 2004), Pipeline System for Octave and Matlab (PSOM) (Bellec et al., 2012), Diffusion Toolkit (Wang et al., 2007), and MRIcron (http://www.mccauslandcenter. sc.edu/mricro/mricron/) were called by PANDA....

    [...]

  • ...Currently, a number of packages, such as FMRIB Software Library (FSL) (Smith et al., 2004) and DTI-Studio (Jiang et al., 2006), provide a set of functions that can carry out these jobs....

    [...]

Journal Article•DOI•
Fast robust automated brain extraction

[...]

Stephen M. Smith1•
John Radcliffe Hospital1
01 Nov 2002-Human Brain Mapping
TL;DR: An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed and described and examples of results and the results of extensive quantitative testing against “gold‐standard” hand segmentations, and two other popular automated methods.
Abstract: An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods.

9,887 citations

"PANDA: a pipeline toolbox for analy..." refers methods in this paper

  • ...This step yields the brain mask by using the bet command of FSL (Smith, 2002)....

    [...]

Journal Article•DOI•
Complex brain networks: graph theoretical analysis of structural and functional systems

[...]

Edward T. Bullmore1, Olaf Sporns2•
University of Cambridge1, Indiana University2
04 Feb 2009-Nature Reviews Neuroscience
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

"PANDA: a pipeline toolbox for analy..." refers background or methods in this paper

  • ...The resultant matrices were saved as a MATLAB data file and can be directly used for topological analysis with graph theoretic approaches (Bullmore and Sporns, 2009; Bullmore and Bassett, 2011)....

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  • ...Typically, the entire brain is divided into multiple regions using a prior gray matter (GM) atlas, where each region represents a network node (Bullmore and Sporns, 2009)....

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  • ...Graph theoretical approaches have been applied to characterize the topology of brain networks that are derived from neuroimaging data (Bullmore and Sporns, 2009)....

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Journal Article•DOI•
Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

[...]

Stephen M. Smith1, Mark Jenkinson1, Heidi Johansen-Berg1, Daniel Rueckert2, Thomas E. Nichols3, Clare E. Mackay1, Kate E. Watkins1, Olga Ciccarelli4, M Z Cader1, Paul M. Matthews1, Timothy E.J. Behrens1 •
University of Oxford1, Imperial College London2, University of Michigan3, University College London4
15 Jul 2006-NeuroImage
TL;DR: TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies by solving the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis.

5,959 citations

"PANDA: a pipeline toolbox for analy..." refers methods in this paper

  • ...The TBSS framework avoids the necessity of choosing a spatial smoothing procedure during voxel-based analysis and may provide better sensitivity and interpretability when it is applied to multi-subjects dMRI datasets (Smith et al., 2006)....

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