A Connectome Computation System for discovery science of brain

Science Bulletin

Volumn 60, Issue 1, 2015, Pages 86-95

  Xu, Ting ^a   Yang, Zhi ^a   Jiang, Lili ^a   Xing, Xiu Xia ^b   Zuo, Xi Nian ^a,c  

^a INSTITUTE OF PSYCHOLOGY   (China)

^b BEIJING UNIVERSITY OF TECHNOLOGY   (China)

^c SOUTHWEST UNIVERSITY   (China)

Author keywords

Big data; Connectome; Discovery science; Life span; Normative charts

Indexed keywords

EID: 84934900656     PISSN: 20959273     EISSN: 20959281     Source Type: Journal    
DOI: 10.1007/s11434-014-0698-3     Document Type: Article

References (95)

1. Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42
2. Bullmore ET, Bassett DS (2011) Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol 7:113–140
3. Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13:336–349
4. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198
5. Breakspear M, Jirsa V, Deco G (2010) Computational models of the brain: from structure to function. Neuroimage 52:727–730
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
7. Deco G, Jirsa VK, McIntosh AR (2013) Resting brains never rest: computational insights into potential cognitive architectures. Trends Neurosci 36:268–274
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
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
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
11. Seung HS (2011) Neuroscience: towards functional connectomics. Nature 471:170–172
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
13. Smith SM, Vidaurre D, Beckmann CF et al (2013) Functional connectomics from resting-state fMRI. Trends Cogn Sci 17:666–682
14. Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921
15. Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351
16. 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing. Nature 467:1061–1073
17. 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491:56–65
18. Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79
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
20. Berger B, Peng J, Singh M (2013) Computational solutions for omics data. Nat Rev Genet 14:333–346
21. Turk-Browne NB (2013) Functional interactions as big data in the human brain. Science 342:580–584
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
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
24. Craddock RC, Jbabdi S, Yan CG et al (2013) Imaging human connectomes at the macroscale. Nat Methods 10:524–539
25. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194
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
27. Segonne F, Dale AM, Busa E et al (2004) A hybrid approach to the skull stripping problem in MRI. Neuroimage 22:1060–1075
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
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
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
31. Eskildsen SF, Coupé P, Fonov V et al (2012) BEaST: brain extraction based on nonlocal segmentation technique. Neuroimage 59:2362–2373
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
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
34. Andersson JL, Skare S (2002) A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI. Neuroimage 16:177–199
35. Chang LC, Jones DK, Pierpaoli C (2005) RESTORE: robust estimation of tensors by outlier rejection. Magn Reson Med 53:1088–1095
36. Beaulieu C, Allen PS (1994) Determinants of anisotropic water diffusion in nerves. Magn Reson Med 31:394–400
37. Mori S, van Zijl PC (2002) Fiber tracking: principles and strategies—a technical review. NMR Biomed 15:468–480
38. Greve DN, Fischl B (2009) Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48:63–72
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
40. Taylor PA, Saad ZS (2013) FATCAT: (an efficient) functional and tractographic connectivity analysis toolbox. Brain Connect 3:523–535
41. Saad ZS, Reynolds RC, Jo HJ et al (2013) Correcting brain-wide correlation differences in resting-state FMRI. Brain Connect 3:339–352
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
43. Carp J (2013) Optimizing the order of operations for movement scrubbing: comment on power. Neuroimage 76:436–438
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
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
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
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
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
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
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
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
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
53. Mechelli A, Friston KJ, Frackowiak RS et al (2005) Structural covariance in the human cortex. J Neurosci 25:8303–8310
54. Evans AC (2013) Networks of anatomical covariance. Neuroimage 80:489–504
55. Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14:322–336
56. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069
57. Hagmann P, Cammoun L, Gigandet X et al (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6:e159
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
59. van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31:15775–15786
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
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
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
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
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
65. Zuo XN, Di Martino A, Kelly C et al (2010) The oscillating brain: complex and reliable. Neuroimage 49:1432–1445
66. Zang Y, Jiang T, Lu Y et al (2004) Regional homogeneity approach to fMRI data analysis. Neuroimage 22:394–400
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
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
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
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
71. Zuo XN, Ehmke R, Mennes M et al (2012) Network centrality in the human functional connectome. Cereb Cortex 22:1862–1875
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
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
74. Loewe K, Grueschow M, Stoppel CM et al (2014) Fast construction of voxel-level functional connectivity graphs. BMC Neurosci 15:78
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
76. Bowman FD (2014) Brain imaging analysis. Annu Rev Stat Appl 1:61–85
77. Xue SW, Weng XC, He S et al (2013) Similarity representation of pattern-information fMRI. Chin Sci Bull 58:1236–1242
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
79. Yang Z, LaConte S, Weng X et al (2008) Ranking and averaging independent component analysis by reproducibility (RAICAR). Neuroimage 63:403–414
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
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
82. Yang Z, Xu Y, Xu T et al (2014) Brain network informed subject community detection in early-onset schizophrenia. Sci Rep 4:5549
83. Castellanos FX, Di Martino A, Craddock RC et al (2013) Clinical applications of the functional connectome. Neuroimage 80:527–540
84. Dosenbach NU, Nardos B, Cohen AL et al (2010) Prediction of individual brain maturity using fMRI. Science 329:1358–1361
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
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
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
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
89. Yeatman JD, Wandell BA, Mezer AA (2014) Lifespan maturation and degeneration of human brain white matter. Nat Commun 5:4932
90. Gutchess A (2014) Plasticity of the aging brain: new directions in cognitive neuroscience. Science 346:579–582
91. Di Martino A, Fair DA, Kelly C et al (2014) Unraveling the miswired connectome: a developmental perspective. Neuron 83:1335–1353
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
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
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
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