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A Connectome Computation System for discovery science of brain

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  • ReceivedNov 24, 2014
  • AcceptedDec 1, 2014
  • PublishedJan 5, 2015

Abstract

Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce a computational pipeline-namely the Connectome Computation System (CCS)-for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging technologies. The CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping and connectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. We demonstrate the utility of the CCS based upon a publicly available dataset, the NKI-Rockland Sample, to delineate the normative trajectories of well-known large-scale neural networks across the natural life span (6-85 years of age). The CCS has been made freely available to the public via GitHub (https://github.com/zuoxinian/CCS) and our laboratory's Web site (http://lfcd.psych.ac.cn/ccs.html) to facilitate progress in discovery science in the field of human brain connectomics.


References

[1] Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42. CrossRef Google Scholar

[2] Bullmore ET, Bassett DS (2011) Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol 7:113-140. CrossRef Google Scholar

[3] Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13:336-349. Google Scholar

[4] Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186-198. CrossRef Google Scholar

[5] Breakspear M, Jirsa V, Deco G (2010) Computational models of the brain: from structure to function. Neuroimage 52:727-730. CrossRef Google Scholar

[6] Deco G, Jirsa VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12:43-56. CrossRef Google Scholar

[7] Deco G, Jirsa VK, McIntosh AR (2013) Resting brains never rest: computational insights into potential cognitive architectures. Trends Neurosci 36:268-274. CrossRef Google Scholar

[8] Song HF, Kennedy H, Wang XJ (2014) Spatial embedding of structural similarity in the cerebral cortex. Proc Natl Acad Sci USA 111:16580-16585. CrossRef Google Scholar

[9] Chen Y, Wang S, Hilgetag CC et al (2013) Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems. PLoS Comput Biol 9:e1002937. CrossRef Google Scholar

[10] Biswal BB, Mennes M, Zuo XN et al (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107:4734-4739. CrossRef Google Scholar

[11] Seung HS (2011) Neuroscience: towards functional connectomics. Nature 471:170-172. CrossRef Google Scholar

[12] Alivisatos AP, Chun M, Church GM et al (2012) The brain activity map project and the challenge of functional connectomics. Neuron 74:970-974. CrossRef Google Scholar

[13] Smith SM, Vidaurre D, Beckmann CF et al (2013) Functional connectomics from resting-state fMRI. Trends Cogn Sci 17:666-682. CrossRef Google Scholar

[14] Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860-921. CrossRef Google Scholar

[15] Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304-1351. CrossRef Google Scholar

[16] 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing. Nature 467:1061-1073. CrossRef Google Scholar

[17] 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491:56-65. CrossRef Google Scholar

[18] Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62-79. CrossRef Google Scholar

[19] Schadt EE, Linderman MD, Sorenson J et al (2010) Computational solutions to large-scale data management and analysis. Nat Rev Genet 11:647-657. CrossRef Google Scholar

[20] Berger B, Peng J, Singh M (2013) Computational solutions for omics data. Nat Rev Genet 14:333-346. CrossRef Google Scholar

[21] Turk-Browne NB (2013) Functional interactions as big data in the human brain. Science 342:580-584. CrossRef Google Scholar

[22] Marcus DS, Olsen TR, Ramaratnam M et al (2007) The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics 5:11-34. Google Scholar

[23] Scott A, Courtney W, Wood D et al (2011) COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front Neuroinform 5:33. CrossRef Google Scholar

[24] Craddock RC, Jbabdi S, Yan CG et al (2013) Imaging human connectomes at the macroscale. Nat Methods 10:524-539. CrossRef Google Scholar

[25] Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179-194. CrossRef Google Scholar

[26] Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II. Inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195-207. CrossRef Google Scholar

[27] Segonne F, Dale AM, Busa E et al (2004) A hybrid approach to the skull stripping problem in MRI. Neuroimage 22:1060-1075. CrossRef Google Scholar

[28] Segonne F, Pacheco J, Fischl B (2007) Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans Med Imaging 26:518-529. CrossRef Google Scholar

[29] Xing XX, Zhou YL, Adelstein JS et al (2011) PDE-based spatial smoothing: a practical demonstration of impacts on MRI brain extraction, tissue segmentation and registration. Magn Reson Imaging 29:731-738. CrossRef Google Scholar

[30] Zuo XN, Xing XX (2011) Effects of non-local diffusion on structural MRI preprocessing and default network mapping: statistical comparisons with isotropic/anisotropic diffusion. PLoS One 6:e26703. CrossRef Google Scholar

[31] Eskildsen SF, Coupé P, Fonov V et al (2012) BEaST: brain extraction based on nonlocal segmentation technique. Neuroimage 59:2362-2373. CrossRef Google Scholar

[32] Klein A, Andersson J, Ardekani BA et al (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46:786-802. CrossRef Google Scholar

[33] Behrens TE, Woolrich MW, Jenkinson M et al (2003) Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 50:1077-1088. CrossRef Google Scholar

[34] Andersson JL, Skare S (2002) A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI. Neuroimage 16:177-199. CrossRef Google Scholar

[35] Chang LC, Jones DK, Pierpaoli C (2005) RESTORE: robust estimation of tensors by outlier rejection. Magn Reson Med 53:1088-1095. CrossRef Google Scholar

[36] Beaulieu C, Allen PS (1994) Determinants of anisotropic water diffusion in nerves. Magn Reson Med 31:394-400. CrossRef Google Scholar

[37] Mori S, van Zijl PC (2002) Fiber tracking: principles and strategies-a technical review. NMR Biomed 15:468-480. CrossRef Google Scholar

[38] Greve DN, Fischl B (2009) Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48:63-72. CrossRef Google Scholar

[39] Behrens TE, Berg HJ, Jbabdi S et al (2007) Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34:144-155. CrossRef Google Scholar

[40] Taylor PA, Saad ZS (2013) FATCAT: (an efficient) functional and tractographic connectivity analysis toolbox. Brain Connect 3:523-535. CrossRef Google Scholar

[41] Saad ZS, Reynolds RC, Jo HJ et al (2013) Correcting brain-wide correlation differences in resting-state FMRI. Brain Connect 3:339-352. CrossRef Google Scholar

[42] Power JD, Mitra A, Laumann TO et al (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84:320-341. CrossRef Google Scholar

[43] Carp J (2013) Optimizing the order of operations for movement scrubbing: comment on power. Neuroimage 76:436-438. CrossRef Google Scholar

[44] Yan CG, Cheung B, Kelly C et al (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76:183-201. CrossRef Google Scholar

[45] Satterthwaite TD, Elliott MA, Gerraty RT et al (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64:240-256. CrossRef Google Scholar

[46] Jo HJ, Saad ZS, Simmons WK et al (2010) Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage 52:571-582. CrossRef Google Scholar

[47] Yeo BT, Krienen FM, Sepulcre J et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125-1165. CrossRef Google Scholar

[48] Fox MD, Zhang D, Snyder AZ et al (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101:3270-3283. CrossRef Google Scholar

[49] Murphy K, Birn RM, Handwerker DA et al (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44:893-905. CrossRef Google Scholar

[50] Yan CG, Craddock RC, Zuo XN et al (2013) Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80:246-262. CrossRef Google Scholar

[51] Zuo XN, Anderson JS, Bellec P et al (2014) An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data 1:140049. Google Scholar

[52] 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. CrossRef Google Scholar

[53] Mechelli A, Friston KJ, Frackowiak RS et al (2005) Structural covariance in the human cortex. J Neurosci 25:8303-8310. CrossRef Google Scholar

[54] Evans AC (2013) Networks of anatomical covariance. Neuroimage 80:489-504. CrossRef Google Scholar

[55] Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14:322-336. CrossRef Google Scholar

[56] Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059-1069. CrossRef Google Scholar

[57] Hagmann P, Cammoun L, Gigandet X et al (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6:e159. CrossRef Google Scholar

[58] Gong G, Rosa-Neto P, Carbonell F et al (2009) Age- and gender-related differences in the cortical anatomical network. J Neurosci 29:15684-15693. CrossRef Google Scholar

[59] van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31:15775-15786. CrossRef Google Scholar

[60] Zuo XN, Xing XX (2014) Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neurosci Biobehav Rev 45:100-118. CrossRef Google Scholar

[61] Zuo XN, Xu T, Jiang L et al (2013) Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage 65:374-386. CrossRef Google Scholar

[62] Jiang L, Xu T, He Y et al (2014) Toward neurobiological characterization of functional homogeneity in the human cortex: regional variation, morphological association and functional covariance network organization. Brain Struct Funct. doi:10.1007/s00429-014-0795-8. Google Scholar

[63] Zang YF, He Y, Zhu CZ et al (2007) Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 29:83-91. CrossRef Google Scholar

[64] Zou QH, Zhu CZ, Yang Y et al (2008) An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172:137-141. CrossRef Google Scholar

[65] Zuo XN, Di Martino A, Kelly C et al (2010) The oscillating brain: complex and reliable. Neuroimage 49:1432-1445. CrossRef Google Scholar

[66] Zang Y, Jiang T, Lu Y et al (2004) Regional homogeneity approach to fMRI data analysis. Neuroimage 22:394-400. CrossRef Google Scholar

[67] Zuo XN, Kelly C, Di Martino A et al (2010) Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J Neurosci 30:15034-15043. CrossRef Google Scholar

[68] Biswal B, Yetkin FZ, Haughton VM et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537-541. CrossRef Google Scholar

[69] Greicius MD, Krasnow B, Reiss AL et al (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 100:253-258. CrossRef Google Scholar

[70] Zuo XN, Kelly C, Adelstein JS et al (2010) Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach. Neuroimage 49:2163-2177. CrossRef Google Scholar

[71] Zuo XN, Ehmke R, Mennes M et al (2012) Network centrality in the human functional connectome. Cereb Cortex 22:1862-1875. CrossRef Google Scholar

[72] Destrieux C, Fischl B, Dale A et al (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53:1-15. CrossRef Google Scholar

[73] Wu X, Xu L, Yao L (2014) Big data analysis of the human's functional interactions based on fMRI. Chin Sci Bull 59:5059-5065. CrossRef Google Scholar

[74] Loewe K, Grueschow M, Stoppel CM et al (2014) Fast construction of voxel-level functional connectivity graphs. BMC Neurosci 15:78. CrossRef Google Scholar

[75] Liao W, Wu GR, Xu Q et al (2014) DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis. Brain Connect. doi:10.1089/brain.2014.0253. Google Scholar

[76] Bowman FD (2014) Brain imaging analysis. Annu Rev Stat Appl 1:61-85. Google Scholar

[77] Xue SW, Weng XC, He S et al (2013) Similarity representation of pattern-information fMRI. Chin Sci Bull 58:1236-1242. CrossRef Google Scholar

[78] Yang Z, Zuo XN, Wang P et al (2012) Generalized RAICAR: discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks. Neuroimage 63:403-414. CrossRef Google Scholar

[79] Yang Z, LaConte S, Weng X et al (2008) Ranking and averaging independent component analysis by reproducibility (RAICAR). Neuroimage 63:403-414. CrossRef Google Scholar

[80] Kapur S, Phillips AG, Insel TR (2013) Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol Psychiatry 17:1174-1179. CrossRef Google Scholar

[81] Yang Z, Chang C, Xu T et al (2012) Connectivity trajectory across lifespan differentiates the precuneus from the default network. Neuroimage 89:45-56. CrossRef Google Scholar

[82] Yang Z, Xu Y, Xu T et al (2014) Brain network informed subject community detection in early-onset schizophrenia. Sci Rep 4:5549. Google Scholar

[83] Castellanos FX, Di Martino A, Craddock RC et al (2013) Clinical applications of the functional connectome. Neuroimage 80:527-540. CrossRef Google Scholar

[84] Dosenbach NU, Nardos B, Cohen AL et al (2010) Prediction of individual brain maturity using fMRI. Science 329:1358-1361. CrossRef Google Scholar

[85] Collin G, van den Heuvel MP (2013) The ontogeny of the human connectome: development and dynamic changes of brain connectivity across the life span. Neuroscientist 19:616-628. CrossRef Google Scholar

[86] Cao M, Wang JH, Dai ZJ et al (2014) Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci 7:76-93. CrossRef Google Scholar

[87] Betzel RF, Byrge L, He Y et al (2014) Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 102:345-357. CrossRef Google Scholar

[88] Chan MY, Park DC, Savalia NK et al (2014) Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci USA 111:E4997-E5006. CrossRef Google Scholar

[89] Yeatman JD, Wandell BA, Mezer AA (2014) Lifespan maturation and degeneration of human brain white matter. Nat Commun 5:4932. CrossRef Google Scholar

[90] Gutchess A (2014) Plasticity of the aging brain: new directions in cognitive neuroscience. Science 346:579-582. CrossRef Google Scholar

[91] Di Martino A, Fair DA, Kelly C et al (2014) Unraveling the miswired connectome: a developmental perspective. Neuron 83:1335-1353. CrossRef Google Scholar

[92] Nooner KB, Colcombe SJ, Tobe RH et al (2012) The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front Neurosci 6:152. CrossRef Google Scholar

[93] Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape. J R Stat Soc Ser C Appl Stat 54:507-554. CrossRef Google Scholar

[94] Multicentre Growth Reference Study Group WHO (2009) WHO Child Growth Standards: growth velocity based on weight, length and head circumference: methods and development. World Health Organization, Geneva. Google Scholar

[95] Rigby RA, Stasinopoulos DM (2013) Automatic smoothing parameter selection in GAMLSS with an application to centile estimation. Stat Methods Med Res 23:318-332. CrossRef Google Scholar

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