A multivariate distance-based analytic framework for connectome-wide association studies

NeuroImage

Volumn 93, Issue P1, 2014, Pages 74-94

  Shehzad, Zarrar ^a,b,c   Kelly, Clare ^d   Reiss, Philip T ^b,e   Cameron Craddock, R ^b,c   Emerson, John W ^a   McMahon, Katie ^f   Copland, David A ^f   Xavier Castellanos, F ^b,d   Milham, Michael P ^b,c  

^a YALE UNIVERSITY   (United States)

^b NATHAN S KLINE INSTITUTE FOR PSYCHIATRIC RESEARCH   (United States)

^c CHILD MIND INSTITUTE   (United States)

^d NEW YORK UNIVERSITY CHILD STUDY CENTER   (United States)

^e NEW YORK UNIVERSITY   (United States)

^f UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Brain behavior relationships; Connectome; Discovery; Functional connectivity; Phenotype; Resting state

Indexed keywords

LEVODOPA;

ADOLESCENT; ADULT; AGED; ANALYTICAL PARAMETERS; ARTICLE; ARTIFICIAL NEURAL NETWORK; ATTENTION DEFICIT DISORDER; BRAIN BEHAVIOR RELATIONSHIP; CHILD; CONCEPTUAL FRAMEWORK; CONNECTOME; CONTROLLED STUDY; DATA PROCESSING; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; INTELLIGENCE QUOTIENT; MAJOR CLINICAL STUDY; MALE; MANIPULATIVE MEDICINE; MULTIVARIATE DISTANCE MATRIX REGRESSION; NERVOUS SYSTEM PARAMETERS; PHENOTYPE; PRIORITY JOURNAL; RESTING STATE NETWORK; VOXEL WISE ANALYSIS; BRAIN; INTELLIGENCE; MIDDLE AGED; MULTIVARIATE ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; BRAIN; CONNECTOME; FEMALE; HUMANS; INTELLIGENCE; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MULTIVARIATE ANALYSIS; YOUNG ADULT;

EID: 84896979657     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.02.024     Document Type: Article

References (100)

1. Akil H., Martone M.E., Essen D.C.V. Challenges and opportunities in mining neuroscience data. Science 2011, 331:708-712.
2. Anderson M.J. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 2001, 26:32-46.
3. Andersson J.L.R., Jenkinson M., Smith S. Non-linear registration, aka spatial normalisation (No. TR07JA2). FMRIB Technical Report 2007, FMRIB Centre, Oxford, UK.
4. Andrews-Hanna J.R., Reidler J.S., Sepulcre J., Poulin R., Buckner R.L. Functional-anatomic fractionation of the brain's default network. Neuron 2010, 65:550-562.
5. Beckmann C.F. Modelling with independent components. NeuroImage 2012, 62:891-901.
6. Behzadi Y., Restom K., Liau J., Liu T.T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 2007, 37:90-101.
7. Bellec P., Rosa-Neto P., Lyttelton O.C., Benali H., Evans A.C. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 2010, 51:1126-1139.
8. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 1995, 57:289-300.
9. Biswal B.B., Mennes M., Zuo X.-N., Gohel S., Kelly C., Smith S.M., Beckmann C.F., Adelstein J.S., Buckner R.L., Colcombe S., Dogonowski A.-M., Ernst M., Fair D., Hampson M., Hoptman M.J., Hyde J.S., Kiviniemi V.J., Kötter R., Li S.-J., Lin C.-P., Lowe M.J., Mackay C., Madden D.J., Madsen K.H., Margulies D.S., Mayberg H.S., McMahon K., Monk C.S., Mostofsky S.H., Nagel B.J., Pekar J.J., Peltier S.J., Petersen S.E., Riedl V., Rombouts S.A.R.B., Rypma B., Schlaggar B.L., Schmidt S., Seidler R.D., Siegle G.J., Sorg C., Teng G.-J., Veijola J., Villringer A., Walter M., Wang L., Weng X.-C., Whitfield-Gabrieli S., Williamson P., Windischberger C., Zang Y.-F., Zhang H.-Y., Castellanos F.X., Milham M.P. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. 2010, 107:4734-4739.
10. Buckner R.L., Sepulcre J., Talukdar T., Krienen F.M., Liu H., Hedden T., Andrews-Hanna J.R., Sperling R.A., Johnson K.A. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 2009, 29:1860-1873.
11. Buckner R.L., Krienen F.M., Castellanos A., Diaz J.C., Yeo B.T.T. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 2011, 106:2322-2345.
12. Bullmore E.T., Bassett D.S. Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 2011, 7:113-140.
13. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009, 10:186-198.
14. Burton, P.R., Clayton, D.G., Cardon, L.R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D.P., McCarthy, M.I., Ouwehand, W.H., Samani, N.J., Todd, J.A., Donnelly, P., Barrett, J.C., Davison, D., Easton, D., Evans, D., Leung, H.-T., Marchini, J.L., Morris, A.P., Spencer, C.C.A., Tobin, M.D., Attwood, A.P., Boorman, J.P., Cant, B., Everson, U., Hussey, J.M., Jolley, J.D., Knight, A.S., Koch, K., Meech, E., Nutland, S., Prowse, C.V., Stevens, H.E., Taylor, N.C., Walters, G.R., Walker, N.M., Watkins, N.A., Winzer, T., Jones, R.W., McArdle, W.L., Ring, S.M., Strachan, D.P., Pembrey, M., Breen, G., Clair, D.S., Caesar, S., Gordon-Smith, K., Jones, L., Fraser, C., Green, E.K., Grozeva, D., Hamshere, M.L., Holmans, P.A., Jones, I.R., Kirov, G., Moskvina, V., Nikolov, I., O'Donovan, M.C., Owen, M.J., Collier, D.A., Elkin, A., Farmer, A., Williamson, R., McGuffin, P., Young, A.H., Ferrier, I.N., Ball, S.G., Balmforth, A.J., Barrett, J.H., Bishop, D.T., Iles, M.M., Maqbool, A., Yuldasheva, N., Hall, A.S., Braund, P.S., Dixon, R.J., Mangino, M., Stevens, S., Thompson, J.R., Bredin, F., Tremelling, M., Parkes, M., Drummond, H., Lees, C.W., Nimmo, E.R., Satsangi, J., Fisher, S.A., Forbes, A., Lewis, C.M., Onnie, C.M., Prescott, N.J., Sanderson, J., Mathew, C.G., Barbour, J., Mohiuddin, M.K., Todhunter, C.E., Mansfield, J.C., Ahmad, T., Cummings, F.R., Jewell, D.P., Webster, J., Brown, M.J., Lathrop, G.M., Connell, J., Dominiczak, A., Marcano, C.A.B., Burke, B., Dobson, R., Gungadoo, J., Lee, K.L., Munroe, P.B., Newhouse, S.J., Onipinla, A., Wallace, C., Xue, M., Caulfield, M., Farrall, M., Barton, A., (braggs), T.B. in R.G. and G., Bruce, I.N., Donovan, H., Eyre, S., Gilbert, P.D., Hider, S.L., Hinks, A.M., John, S.L., Potter, C., Silman, A.J., Symmons, D.P.M., Thomson, W., Worthington, J., Dunger, D.B., Widmer, B., Frayling, T.M., Freathy, R.M., Lango, H., Perry, J.R.B., Shields, B.M., Weedon, M.N., Hattersley, A.T., Hitman, G.A., Walker, M., Elliott, K.S., Groves, C.J., Lindgren, C.M., Rayner, N.W., Timpson, N.J., Zeggini, E., Newport, M., Sirugo, G., Lyons, E., Vannberg, F., Hill, A.V.S., Bradbury, L.A., Farrar, C., Pointon, J.J., Wordsworth, P., Brown, M.A., Franklyn, J.A., Heward, J.M., Simmonds, M.J., Gough, S.C.L., Seal, S., (uk), B.C.S.C., Stratton, M.R., Rahman, N., Ban, M., Goris, A., Sawcer, S.J., Compston, Alastair, Conway, D., Jallow, M., Rockett, K.A., Bumpstead, S.J., Chaney, A., Downes, K., Ghori, M.J.R., Gwilliam, R., Hunt, S.E., Inouye, M., Keniry, A., King, E., McGinnis, R., Potter, S., Ravindrarajah, R., Whittaker, P., Widden, C., Withers, D., Cardin, N.J., Ferreira, T., Pereira-Gale, J., Hallgrimsdóttir, I.B., Howie, B.N., Su, Z., Teo, Y.Y., Vukcevic, D., Bentley, D., Compston, Alistair, 2007. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661-678.
15. Chabernaud C., Mennes M., Kelly C., Nooner K., Di Martino A., Castellanos F.X., Milham M.P. Dimensional brain-behavior relationships in children with attention-deficit/hyperactivity disorder. Biol. Psychiatry 2012, 71:434-442.
16. Chai X.J., Castañón A.N., Öngür D., Whitfield-Gabrieli S. Anticorrelations in resting state networks without global signal regression. NeuroImage 2012, 59:1420-1428.
17. Choi E.Y., Yeo B.T.T., Buckner R.L. The organization of the human striatum estimated by intrinsic functional connectivity. J. Neurophysiol. 2012, 108:2242-2263.
18. Chumbley J.R., Friston K.J. False discovery rate revisited: FDR and topological inference using Gaussian random fields. NeuroImage 2009, 44:62-70.
19. Cole D.M., Smith S.M., Beckmann C.F. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front. Syst. Neurosci. 2010, 4.
20. Cole M.W., Pathak S., Schneider W. Identifying the brain's most globally connected regions. NeuroImage 2010, 49:3132-3148.
21. Cole M.W., Yarkoni T., Repovš G., Anticevic A., Braver T.S. Global connectivity of prefrontal cortex predicts cognitive control and intelligence. J. Neurosci. 2012, 32:8988-8999.
22. Cole M.W., Reynolds J.R., Power J.D., Repovs G., Anticevic A., Braver T.S. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 2013, 16:1348-1355.
23. Craddock R.C., James G.A., Holtzheimer P.E., Hu X.P., Mayberg H.S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 2012, 33:1914-1928.
24. Craddock R.C., Jbabdi S., Yan C.-G., Vogelstein J.T., Castellanos F.X., Di Martino A., Kelly C., Heberlein K., Colcombe S., Milham M.P. Imaging human connectomes at the macroscale. Nat. Methods 2013, 10:524-539.
25. Daducci A., Gerhard S., Griffa A., Lemkaddem A., Cammoun L., Gigandet X., Meuli R., Hagmann P., Thiran J.-P. The connectome mapper: an open-source processing pipeline to map connectomes with MRI. PLoS ONE 2012, 7:e48121.
26. Damoiseaux J.S., Rombouts S.a.R.B., Barkhof F., Scheltens P., Stam C.J., Smith S.M., Beckmann C.F. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. 2006, 103:13848-13853.
27. Deary I.J., Penke L., Johnson W. The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 2010, 11:201-211.
28. Di Martino A., Kelly C., Grzadzinski R., Zuo X.-N., Mennes M., Mairena M.A., Lord C., Castellanos F.X., Milham M.P. Aberrant striatal functional connectivity in children with autism. Biol. Psychiatry 2011, 69:847-856.
29. Dosenbach N.U.F., Fair D.A., Cohen A.L., Schlaggar B.L., Petersen S.E. A dual-networks architecture of top-down control. Trends Cogn. Sci. 2008, 12:99-105.
30. Dosenbach N.U.F., Nardos B., Cohen A.L., Fair D.A., Power J.D., Church J.A., Nelson S.M., et al. Prediction of Individual Brain Maturity Using fMRI. Science 2010, 329(5997):1358-1361. 10.1126/science.1194144.
31. Duncan J., Burgess P., Emslie H. Fluid intelligence after frontal lobe lesions. Neuropsychologia 1995, 33:261-268.
32. Duncan J., Seitz R.J., Kolodny J., Bor D., Herzog H., Ahmed A., Newell F.N., Emslie H. A neural basis for general intelligence. Science 2000, 289:457-460.
33. Fair D.A., Nigg J.T., Iyer S., Bathula D., Mills K.L., Dosenbach N.U.F., Schlaggar B.L., Mennes M., Gutman D., Bangaru S., Buitelaar J.K., Dickstein D.P., Di Martino A., Kennedy D.N., Kelly C., Luna B., Schweitzer J.B., Velanova K., Wang Y.-F., Mostofsky S., Castellanos F.X., Milham M.P. Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Front. Syst. Neurosci. 2013, 6.
34. Fornito A., Bullmore E.T. What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders?. Curr. Opin. Psychiatry 2010, 23:239-249.
35. Genovese C.R., Lazar N.A., Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage 2002, 15:870-878.
36. Gorgolewski K., Madison C., Clark D., Halchenko Y.O., Waskom M.L., Ghosh S.S. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinformatics 2011, 5:13.
37. Gotts S.J., Saad Z.S., Jo H.J., Wallace G.L., Cox R.W., Martin A. The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders. Front. Hum. Neurosci. 2013, 7.
38. Gower J.C., Krzanowski W.J. Analysis of distance for structured multivariate data and extensions to multivariate analysis of variance. J. R. Stat. Soc.: Ser. C: Appl. Stat. 1999, 48:505-519.
39. Greicius M. Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 2008, 24:424-430.
40. Gretton A., Borgwardt K., Rasch M.J., Scholkopf B., Smola A.J. A kernel method for the two-sample problem. ArXiv08052368 Cs 2008.
41. Harchaoui Z., Bach F., Cappe O., Moulines E. Kernel-based methods for hypothesis testing: a unified view. IEEE Signal Process. Mag. 2013, 30:87-97.
42. Hartigan J.A. Clustering Algorithms 1975, John Wiley & Sons, Inc., New York, NY, USA. 99th ed.
43. Haynes J.-D., Rees G. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 2006, 7:523-534.
44. Hu J.X., Zhao H., Zhou H.H. False discovery rate control with groups. J. Am. Stat. Assoc. 2010, 105:1215-1227.
45. Hyvarinen A., Smith S.M. Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. J. Mach. Learn. Res. 2013, 14:111-152.
46. Jung R.E., Haier R.J. The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav. Brain Sci. 2007, 30:135-154.
47. Kane M.J., Engle R.W. The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon. Bull. Rev. 2002, 9:637-671.
48. Kane M., Emerson J.W., Weston S. Scalable strategies for computing with massive data. J. Stat. Softw. 2013, 55.
49. Kelly A.M.C., Uddin L.Q., Biswal B.B., Castellanos F.X., Milham M.P. Competition between functional brain networks mediates behavioral variability. NeuroImage 2008, 39:527-537.
50. Kelly A.M.C., Martino A.D., Uddin L.Q., Shehzad Z., Gee D.G., Reiss P.T., Margulies D.S., Castellanos F.X., Milham M.P. Development of anterior cingulate functional connectivity from late childhood to early adulthood. Cereb. Cortex 2009, 19:640-657.
51. Kelly C., de Zubicaray G., Martino A.D., Copland D.A., Reiss P.T., Klein D.F., Castellanos F.X., Milham M.P., McMahon K. L-DOPA modulates functional connectivity in striatal cognitive and motor networks: a double-blind placebo-controlled study. J. Neurosci. 2009, 29:7364-7378.
52. Kelly C., Biswal B.B., Craddock R.C., Castellanos F.X., Milham M.P. Characterizing variation in the functional connectome: promise and pitfalls. Trends Cogn. Sci. 2012, 16:181-188.
53. Koyama M.S., Martino A.D., Zuo X.-N., Kelly C., Mennes M., Jutagir D.R., Castellanos F.X., Milham M.P. Resting-state functional connectivity indexes reading competence in children and adults. J. Neurosci. 2011, 31:8617-8624.
54. Kriegeskorte N., Simmons W.K., Bellgowan P.S.F., Baker C.I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 2009, 12:535-540.
55. Lin W.Y., Schaid D.J. Power Comparisons between Similarity-Based Multilocus Association Methods, Logistic Regression, and Score Tests for Haplotypes. Genetic Epidemiology 2009, 33(3):183-197. 10.1002/gepi.20364.
56. Mardia K.V., Kent J.T., Bibby J.M. Multivariate Analysis 1979, Academic Press, London; New York.
57. Margulies D.S., Böttger J., Long X., Lv Y., Kelly C., Schäfer A., Goldhahn D., Abbushi A., Milham M.P., Lohmann G., Villringer A. Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. MAGMA 2010, 23:289-307.
58. Margulies D.S., Böttger J., Watanabe A., Gorgolewski K.J. Visualizing the human connectome. NeuroImage 2013, 80:445-461.
59. McArdle B.H., Anderson M.J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 2001, 82:290-297.
60. McCarthy M.I., Abecasis G.R., Cardon L.R., Goldstein D.B., Little J., Ioannidis J.P.A., Hirschhorn J.N. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 2008, 9:356-369.
61. Mckeown M.J., Makeig S., Brown G.G., Jung T.-P., Kindermann S.S., Bell A.J., Sejnowski T.J. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 1998, 6:160-188.
62. Mennes M., Vega Potler N., Kelly C., Di Martino A., Castellanos F.X., Milham M.P. Resting state functional connectivity correlates of inhibitory control in children with attention-deficit/hyperactivity disorder. Front. Psychiatry 2012, 2.
63. Milham M.P. Open neuroscience solutions for the connectome-wide association era. Neuron 2012, 73:214-218.
64. Minas C., Montana G. Distance-based analysis of variance: approximate inference and an application to genome-wide association studies (arXiv e-print No. 1205.2417) 2012.
65. Moeller S., Yacoub E., Olman C.A., Auerbach E., Strupp J., Harel N., Ugurbil K. Multiband multislice GE-EPI at 7 Tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain FMRI. Magn. Reson. Med. 2010, 63:1144-1153.
66. Murphy K., Birn R.M., Handwerker D.A., Jones T.B., Bandettini P.A. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?. NeuroImage 2009, 44:893-905.
67. Murphy K., Birn R.M., Bandettini P.A. Resting-state fMRI confounds and cleanup. NeuroImage 2013, 80:349-359.
68. Nee D.E., Wager T.D., Jonides J. Interference resolution: Insights from a meta-analysis of neuroimaging tasks. Cogn. Affect. Behav. Neurosci. 2007, 7:1-17.
69. Nooner K.B., Colcombe S.J., Tobe R.H., Mennes M., Benedict M.M., Moreno A.L., Panek L.J., Brown S., Zavitz S.T., Li Q., Sikka S., Gutman D., Bangaru S., Schlachter R.T., Kamiel S.M., Anwar A.R., Hinz C.M., Kaplan M.S., Rachlin A.B., Adelsberg S., Cheung B., Khanuja R., Yan C., Craddock C.C., Calhoun V., Courtney W., King M., Wood D., Cox C.L., Kelly A.M.C., Di Martino A., Petkova E., Reiss P.T., Duan N., Thomsen D., Biswal B., Coffey B., Hoptman M.J., Javitt D.C., Pomara N., Sidtis J.J., Koplewicz H.S., Castellanos F.X., Leventhal B.L., Milham M.P. The NKI-rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front. Neurosci. 2012, 6.
70. Norman K.A., Polyn S.M., Detre G.J., Haxby J.V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 2006, 10:424-430.
71. Owen A.M., McMillan K.M., Laird A.R., Bullmore E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp. 2005, 25:46-59.
72. Patel R.S., Bowman F.D., Rilling J.K. A Bayesian approach to determining connectivity of the human brain. Hum. Brain Mapp. 2006, 27:267-276.
73. Potter D.M. A permutation test for inference in logistic regression with small- and moderate-sized data sets. Stat. Med. 2005, 24:693-708.
74. Power J.D., Barnes K.A., Snyder A.Z., Schlaggar B.L., Petersen S.E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 2012, 59:2142-2154.
75. R Core Team R: A Language and Environment for Statistical Computing 2013, R Foundation for Statistical Computing, Vienna, Austria.
76. Reiss P.T., Stevens M.H.H., Shehzad Z., Petkova E., Milham M.P. On distance-based permutation tests for between-group comparisons. Biometrics 2010, 66:636-643.
77. Rizzo M.L., Székely G.J. DISCO analysis: a nonparametric extension of analysis of variance. Ann. Appl. Stat. 2010, 4:1034-1055.
78. Saad Z.S., Reynolds R.C., Jo H.J., Gotts S.J., Chen G., Martin A., Cox R.W. Correcting brain-wide correlation differences in resting-state FMRI. Brain Connect. 2013, 3:339-352.
79. Schork N.J., Wessel J., Malo N. DNA sequence-based phenotypic association analysis. Advances in Genetics, Genetic Dissection of Complex Traits 2008, 195-217. Academic Press. D.C. Rao, C. Charles Gu (Eds.).
80. Simmonds D.J., Pekar J.J., Mostofsky S.H. Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia 2008, 46:224-232.
81. Sporns O. The human connectome: a complex network. Ann. N. Y. Acad. Sci. 2011, 1224:109-125.
82. Sporns O., Tononi G., Kötter R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 2005, 1:e42.
83. Trosset M.W., Priebe C.E., Park Y., Miller M.I. Semisupervised learning from dissimilarity data. Comput. Stat. Data Anal. 2008, 52:4643-4657.
84. Valera E.M., Faraone S.V., Murray K.E., Imperati D., Seidman L.J. Meta-Analysis of Structural Imaging Findings in Attention-Deficit Hyperactivity Disorder. Biological Psychiatry 2006, 61(12):1361-1369. 10.1016/j.biopsych.2006.06.011.
85. Van Horn J.D., Gazzaniga M.S. Databasing fMRI studies - towards a "discovery science" of brain function. Nat. Rev. Neurosci. 2002, 3:314-318.
86. Varoquaux G., Craddock R.C. Learning and comparing functional connectomes across subjects. NeuroImage 2013, 80:405-415.
87. Worsley K.J., Chen J.-I., Lerch J., Evans A.C. Comparing functional connectivity via thresholding correlations and singular value decomposition. Philos. Trans. R. Soc. B Biol. Sci. 2005, 360:913-920.
88. Xu J., Moeller S., Auerbach E.J., Strupp J., Smith S.M., Feinberg D.A., Yacoub E., Uǧurbil K. Evaluation of slice accelerations using multiband echo planar imaging at 3T. NeuroImage 2013, 83:991-1001.
89. Yan C.-G., Cheung B., Kelly C., Colcombe S., Craddock R.C., Di Martino A., Li Q., Zuo X.-N., Castellanos F.X., Milham M.P. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 2013, 76:183-201.
90. Yan C.-G., Craddock R.C., Zuo X.-N., Zang Y.-F., Milham M.P. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage 2013, 80:246-262.
91. Yarkoni T., Poldrack R.A., Nichols T.E., Van Essen D.C., Wager T.D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 2011, 8:665-670.
92. Yeo B.T.T., Krienen F.M., Sepulcre J., Sabuncu M.R., Lashkari D., Hollinshead M., Roffman J.L., Smoller J.W., Zöllei L., Polimeni J.R., Fischl B., Liu H., Buckner R.L. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 2011, 106:1125-1165.
93. Zakharov S., Salim A., Thalamuthu A. Comparison of similarity-based tests and pooling strategies for rare variants. BMC Genomics 2013, 14:50.
94. Zalesky A., Fornito A., Bullmore E.T. Network-based statistic: identifying differences in brain networks. NeuroImage 2010, 53:1197-1207.
95. Zalesky A., Fornito A., Harding I.H., Cocchi L., Yücel M., Pantelis C., Bullmore E.T. Whole-brain anatomical networks: does the choice of nodes matter?. NeuroImage 2010, 50:970-983.
96. Zapala M.A., Schork N.J. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl. Acad. Sci. 2006, 103:19430-19435.
97. Zapala M.A., Schork N.J. Statistical properties of multivariate distance matrix regression for high-dimensional data analysis. Front. Genet. 2012, 3.
98. Zhu C.-Z., Zang Y.-F., Cao Q.-J., Yan C.-G., He Y., Jiang T.-Z., Sui M.-Q., Wang Y.-F. Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. NeuroImage 2008, 40:110-120.
99. Zuo X.-N., Kelly C., Martino A.D., Mennes M., Margulies D.S., Bangaru S., Grzadzinski R., Evans A.C., Zang Y.-F., Castellanos F.X., Milham M.P. Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J. Neurosci. 2010, 30:15034-15043.
100. Zuo X.-N., Ehmke R., Mennes M., Imperati D., Castellanos F.X., Sporns O., Milham M.P. Network centrality in the human functional connectome. Cereb. Cortex 2012, 22:1862-1875.