Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99

Paper Submission

Views	Downloads

Relative Articles
Visualization in Connectomics
Chromosome Visualization Tool: A Whole Genome Viewer
Visualization of documents and concepts in neuroinformatics with the 3D-SE viewer
A case study in connectomics: the history, mapping, and connectivity of the claustrum
An Algorithm Visualization Tool on the Reconfigurable Mesh
aCGHViewer: A Generic Visualization Tool For aCGH data
Statistical Viewer: a tool to upload and integrate linkage and association data as plots displayed within the Ensembl genome browser
Developing Visualization Tool for Teaching AI Searching Algorithms
Virtual Reality: A Tool for Cartographic Visualization
MindSeer: a portable and extensible tool for visualization of structural and functional neuroimaging data
More...

PLOS ONE 2013

BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

DOI: 10.1371/journal.pone.0068910

Mingrui Xia, Jinhui Wang, Yong He

Full-Text Cite this paper Add to My Lib

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/).

References

[1]	Sporns O, Tononi G, Kotter R (2005) The human connectome: A structural description of the human brain. PLoS Comput Biol 1: e42.
[2]	Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, et al. (2010) Toward discovery science of human brain function. Proc Natl Acad Sci U S A 107: 4734–4739.
[3]	Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10: 186–198.
[4]	Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13: 336–349.
[5]	He Y, Evans A (2010) Graph theoretical modeling of brain connectivity. Curr Opin Neurol 23: 341–350.
[6]	He Y, Chen ZJ, Evans AC (2007) Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 17: 2407–2419.
[7]	Chen ZJ, He Y, Rosa-Neto P, Germann J, Evans AC (2008) Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb Cortex 18: 2374–2381.
[8]	Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, et al. (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6: e159.
[9]	Gong G, He Y, Concha L, Lebel C, Gross DW, et al. (2009) Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19: 524–536.
[10]	He Y, Wang J, Wang L, Chen ZJ, Yan C, et al. (2009) Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One 4: e5226.
[11]	Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, et al. (2005) Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 15: 1332–1342.
[12]	Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26: 63–72.
[13]	Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442.
[14]	Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103: 8577–8582.
[15]	Meunier D, Lambiotte R, Bullmore ET (2010) Modular and hierarchically modular organization of brain networks. Front Neurosci 4: 200.
[16]	Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, et al. (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 29: 1860–1873.
[17]	van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31: 15775–15786.
[18]	van den Heuvel MP, Kahn RS, Goni J, Sporns O (2012) High-cost, high-capacity backbone for global brain communication. Proc Natl Acad Sci U S A 109: 11372–11377.
[19]	Gong G, Rosa-Neto P, Carbonell F, Chen ZJ, He Y, et al. (2009) Age- and gender-related differences in the cortical anatomical network. J Neurosci 29: 15684–15693.
[20]	Meunier D, Achard S, Morcom A, Bullmore E (2009) Age-related changes in modular organization of human brain functional networks. Neuroimage 44: 715–723.
[21]	Tomasi D, Volkow ND (2012) Aging and functional brain networks. Mol Psychiatry 17: 471, 549–458.
[22]	Wang L, Li Y, Metzak P, He Y, Woodward TS (2010) Age-related changes in topological patterns of large-scale brain functional networks during memory encoding and recognition. Neuroimage 50: 862–872.
[23]	Wen W, Zhu W, He Y, Kochan NA, Reppermund S, et al. (2011) Discrete neuroanatomical networks are associated with specific cognitive abilities in old age. J Neurosci 31: 1204–1212.
[24]	Wu K, Taki Y, Sato K, Kinomura S, Goto R, et al. (2012) Age-related changes in topological organization of structural brain networks in healthy individuals. Hum Brain Mapp 33: 552–568.
[25]	Fair DA, Dosenbach NU, Church JA, Cohen AL, Brahmbhatt S, et al. (2007) Development of distinct control networks through segregation and integration. Proc Natl Acad Sci U S A 104: 13507–13512.
[26]	Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, et al. (2009) Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 5: e1000381.
[27]	Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM, et al. (2008) The maturing architecture of the brain’s default network. Proc Natl Acad Sci U S A 105: 4028–4032.
[28]	Fair DA, Bathula D, Nikolas MA, Nigg JT (2012) Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proc Natl Acad Sci U S A 109: 6769–6774.
[29]	Batalle D, Eixarch E, Figueras F, Munoz-Moreno E, Bargallo N, et al. (2012) Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome. Neuroimage 60: 1352–1366.
[30]	Hwang K, Hallquist MN, Luna B (2012) The Development of Hub Architecture in the Human Functional Brain Network. Cereb Cortex.
[31]	Khundrakpam BS, Reid A, Brauer J, Carbonell F, Lewis J, et al.. (2012) Developmental Changes in Organization of Structural Brain Networks. Cereb Cortex.
[32]	Supekar K, Musen M, Menon V (2009) Development of large-scale functional brain networks in children. PLoS Biol 7: e1000157.
[33]	Yap PT, Fan Y, Chen Y, Gilmore JH, Lin W, et al. (2011) Development trends of white matter connectivity in the first years of life. PLoS One 6: e24678.
[34]	Wu K, Taki Y, Sato K, Hashizume H, Sassa Y, et al. (2013) Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence. PLoS One 8: e55347.
[35]	Tian L, Wang J, Yan C, He Y (2011) Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study. Neuroimage 54: 191–202.
[36]	Liu J, Qin W, Nan J, Li J, Yuan K, et al. (2011) Gender-related differences in the dysfunctional resting networks of migraine suffers. PLoS One 6: e27049.
[37]	Yan C, Gong G, Wang J, Wang D, Liu D, et al. (2011) Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. Cereb Cortex 21: 449–458.
[38]	Li Y, Liu Y, Li J, Qin W, Li K, et al. (2009) Brain anatomical network and intelligence. PLoS Comput Biol 5: e1000395.
[39]	van den Heuvel MP, Stam CJ, Kahn RS, Hulshoff Pol HE (2009) Efficiency of functional brain networks and intellectual performance. J Neurosci 29: 7619–7624.
[40]	Schmitt JE, Lenroot RK, Wallace GL, Ordaz S, Taylor KN, et al. (2008) Identification of genetically mediated cortical networks: a multivariate study of pediatric twins and siblings. Cereb Cortex 18: 1737–1747.
[41]	van den Heuvel MP, van Soelen IL, Stam CJ, Kahn RS, Boomsma DI, et al. (2013) Genetic control of functional brain network efficiency in children. Eur Neuropsychopharmacol 23: 19–23.
[42]	Brown JA, Terashima KH, Burggren AC, Ercoli LM, Miller KJ, et al. (2011) Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci U S A 108: 20760–20765.
[43]	Fornito A, Zalesky A, Bassett DS, Meunier D, Ellison-Wright I, et al. (2011) Genetic influences on cost-efficient organization of human cortical functional networks. J Neurosci 31: 3261–3270.
[44]	Bassett DS, Bullmore ET (2009) Human brain networks in health and disease. Curr Opin Neurol 22: 340–347.
[45]	Guye M, Bettus G, Bartolomei F, Cozzone PJ (2010) Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks. MAGMA 23: 409–421.
[46]	Xia M, He Y (2011) Magnetic resonance imaging and graph theoretical analysis of complex brain networks in neuropsychiatric disorders. Brain Connect 1: 349–365.
[47]	Wen W, He Y, Sachdev P (2011) Structural brain networks and neuropsychiatric disorders. Curr Opin Psychiatry 24: 219–225.
[48]	Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens P (2007) Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 17: 92–99.
[49]	He Y, Chen Z, Evans A (2008) Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J Neurosci 28: 4756–4766.
[50]	Lo CY, Wang PN, Chou KH, Wang J, He Y, et al. (2010) Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J Neurosci 30: 16876–16885.
[51]	Wang J, Zuo X, Dai Z, Xia M, Zhao Z, et al. (2013) Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease. Biol Psychiatry 73: 472–481.
[52]	Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, et al. (2010) Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comput Biol 6: e1001006.
[53]	Seo EH, Lee DY, Lee JM, Park JS, Sohn BK, et al. (2013) Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer’s disease. PLoS One 8: e53922.
[54]	Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, et al. (2008) Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 28: 9239–9248.
[55]	Liu Y, Liang M, Zhou Y, He Y, Hao Y, et al. (2008) Disrupted small-world networks in schizophrenia. Brain 131: 945–961.
[56]	Zalesky A, Fornito A, Seal ML, Cocchi L, Westin CF, et al. (2011) Disrupted axonal fiber connectivity in schizophrenia. Biol Psychiatry 69: 80–89.
[57]	Liao W, Zhang Z, Pan Z, Mantini D, Ding J, et al. (2010) Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One 5: e8525.
[58]	Zhang Z, Liao W, Chen H, Mantini D, Ding JR, et al. (2011) Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain 134: 2912–2928.
[59]	Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52: 1059–1069.
[60]	He B, Dai Y, Astolfi L, Babiloni F, Yuan H, et al. (2011) eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Methods 195: 261–269.
[61]	Hosseini SM, Hoeft F, Kesler SR (2012) GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS One 7: e40709.
[62]	Cui Z, Zhong S, Xu P, He Y, Gong G (2013) PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 7: 42.
[63]	Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9: 179–194.
[64]	Mangin JF, Riviere D, Cachia A, Duchesnay E, Cointepas Y, et al. (2004) A framework to study the cortical folding patterns. Neuroimage 23 Suppl 1S129–138.
[65]	Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15: 273–289.
[66]	Brodmann K (1909) Vergleichende lokalisationslehre der grobhirnrinde. Barth: Leipzig.
[67]	Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, et al. (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 Suppl 1S208–219.
[68]	Dosenbach NU, Nardos B, Cohen AL, Fair DA, Power JD, et al. (2010) Prediction of individual brain maturity using fMRI. Science 329: 1358–1361.
[69]	Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, et al. (2008) Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39: 1064–1080.
[70]	Yan C, Zang Y (2010) DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI. Front Syst Neurosci 4: 13.
[71]	Chang C, Glover GH (2010) Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50: 81–98.
[72]	Kang J, Wang L, Yan C, Wang J, Liang X, et al. (2011) Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches. Neuroimage 56: 1222–1234.
[73]	Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, et al.. (2012) Tracking Whole-Brain Connectivity Dynamics in the Resting State. Cereb Cortex.
[74]	Jones DT, Vemuri P, Murphy MC, Gunter JL, Senjem ML, et al. (2012) Non-Stationarity in the “Resting Brain’s” Modular Architecture. PLoS One 7: e39731.
[75]	Margulies DS, B？ttger J, Watanabe A, Gorgolewski KJ (2013) Visualizing the Human Connectome. NeuroImage.
[76]	Gerhard S, Daducci A, Lemkaddem A, Meuli R, Thiran JP, et al. (2011) The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes. Front Neuroinform 5: 3.
[77]	Gleeson P, Steuber V, Silver RA (2007) neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54: 219–235.
[78]	Davison AP, Bruderle D, Eppler J, Kremkow J, Muller E, et al. (2008) PyNN: A Common Interface for Neuronal Network Simulators. Front Neuroinform 2: 11.
[79]	Nordlie E, Plesser HE (2010) Visualizing neuronal network connectivity with connectivity pattern tables. Front Neuroinform 3: 39.
[80]	Liang X, Wang J, Yan C, Shu N, Xu K, et al. (2012) Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study. PLoS One 7: e32766.
[81]	Liang X, Zou Q, He Y, Yang Y (2013) Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc Natl Acad Sci U S A 110: 1929–1934.
[82]	Liu M, Zhang D, Shen D, the Alzheimer’s Disease Neuroimaging I (2013) Hierarchical fusion of features and classifier decisions for Alzheimer’s disease diagnosis. Hum Brain Mapp.
[83]	Dai Z, Yan C, Wang Z, Wang J, Xia M, et al. (2012) Discriminative analysis of early Alzheimer’s disease using multi-modal imaging and multi-level characterization with multi-classifier (M3). Neuroimage 59: 2187–2195.
[84]	Bai F, Shu N, Yuan Y, Shi Y, Yu H, et al. (2012) Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci 32: 4307–4318.
[85]	Yi L, Wang J, Jia L, Zhao Z, Lu J, et al. (2012) Structural and functional changes in subcortical vascular mild cognitive impairment: a combined voxel-based morphometry and resting-state fMRI study. PLoS One 7: e44758.
[86]	Fang P, Zeng LL, Shen H, Wang L, Li B, et al. (2012) Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging. PLoS One 7: e45972.
[87]	Zhang J, Wang J, Wu Q, Kuang W, Huang X, et al. (2011) Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry 70: 334–342.
[88]	Wang L, Dai Z, Peng H, Tan L, Ding Y, et al.. (2013) Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect. Hum Brain Mapp.
[89]	Taylor PN, Goodfellow M, Wang Y, Baier G (2013) Towards a large-scale model of patient-specific epileptic spike-wave discharges. Biol Cybern 107: 83–94.
[90]	Sequeira KM, Tabesh A, Sainju RK, DeSantis SM, Naselaris T, et al. (2013) Perfusion network shift during seizures in medial temporal lobe epilepsy. PLoS One 8: e53204.
[91]	Hong SB, Zalesky A, Cocchi L, Fornito A, Choi EJ, et al. (2013) Decreased functional brain connectivity in adolescents with internet addiction. PLoS One 8: e57831.
[92]	Shu N, Liu Y, Li K, Duan Y, Wang J, et al. (2011) Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cereb Cortex 21: 2565–2577.
[93]	Wang Z, Yan C, Zhao C, Qi Z, Zhou W, et al. (2011) Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer’s disease: a resting-state functional MRI study. Hum Brain Mapp 32: 1720–1740.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal