Brain Imaging and Behavior

, Volume 8, Issue 2, pp 311–322 | Cite as

The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools

  • Ivo D. Dinov
  • Petros Petrosyan
  • Zhizhong Liu
  • Paul Eggert
  • Alen Zamanyan
  • Federica Torri
  • Fabio Macciardi
  • Sam Hobel
  • Seok Woo Moon
  • Young Hee Sung
  • Zhiguo Jiang
  • Jennifer Labus
  • Florian Kurth
  • Cody Ashe-McNalley
  • Emeran Mayer
  • Paul M. Vespa
  • John D. Van Horn
  • Arthur W. Toga
  • for the Alzheimer’s Disease Neuroimaging Initiative
SI: Genetic Neuroimaging in Aging and Age-Related Diseases
First Online:

Abstract

The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.

Keywords

Aging Pipeline Neuroimaging Genetics Computation solutions Workflows IBS Pain Parkinson’s disease Alzheimer’s disease Shape Volume Analysis Big data Visualization 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This study was partially supported by National Institutes of Health grants NIA P 50 AG16570, NIBIB EB01651, NLM LM05639, NIMH R01 MH071940, NIBIB P41EB015922, U24-RR025736, U24-RR021992, U24 GM104203, as well as National Science Foundation grants 0716055 and 1023115. The authors are also indebted to the faculty, staff and students in the Laboratory of Neuro Imaging (LONI) for their support and dedication.

Some of the data processed in this study was partly funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles.

As of September 2013, the Laboratory of Neuro Imaging (LONI) will be relocated to the University of Southern California (USC). Thus, some of the URL links, web-page references, and internet resources cited throughout this manuscript may be relocated to appropriate subdomains under http://www.loni.usc.edu. If you find broken links or defunct URLs please contact help@loni.usc.edu.

References

  1. Alarifi, S., & Wolthusen S. (2013). Anomaly detection for ephemeral cloud IaaS virtual machines, In Network and system security, Springer. p. 321–335.Google Scholar
  2. Avants, B. B., Tustison, N., & Song, G. (2009). Advanced Normalization Tools (ANTS). Insight J.Google Scholar
  3. Bellec, P., et al. (2012). The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows. Frontiers in Neuroinformatics. 6.Google Scholar
  4. Berger, B., Peng, J., & Singh, M. (2013). Computational solutions for omics data. Nature Reviews Genetics, 14(5), 333–346.PubMedCentralPubMedCrossRefGoogle Scholar
  5. Berthold, M. R., et al. (2008). KNIME: The konstanz information miner, in Data analysis, machine learning and applications. C. Preisach, et al., (Eds.), Springer Berlin Heidelberg. p. 319–326.Google Scholar
  6. Binder, E. B. (2009). The role of FKBP5, a co-chaperone of the glucocorticoid receptor in the pathogenesis and therapy of affective and anxiety disorders. Psychoneuroendocrinology, 34, S186–S195.PubMedCrossRefGoogle Scholar
  7. Breeze, J. L., Poline, J.-B., & Kennedy, D. N. (2012). Data sharing and publishing in the field of neuroimaging. Giga Science, 1(1), 1–3.CrossRefGoogle Scholar
  8. Bremner, J. D., Vermetten, E., & Mazure, C. M. (2000). Development and preliminary psychometric properties of an instrument for the measurement of childhood trauma: the Early Trauma Inventory. Depression and Anxiety, 12(1), 1–12.PubMedCrossRefGoogle Scholar
  9. Buxbaum, J. D., et al. (2012). The autism sequencing consortium: large-scale, high-throughput sequencing in autism spectrum disorders. Neuron, 76(6), 1052–1056.PubMedCrossRefGoogle Scholar
  10. Che, A., Cui, J., & Dinov, I. (2009). SOCR analyses: implementation and demonstration of a New graphical statistics educational toolkit. JSS, 30(3), 1–19.Google Scholar
  11. Chen, R., & Herskovits, E. H. (2005). Graphical-model-based morphometric analysis. Medical Imaging, IEEE Transactions on, 24(10), 1237–1248.CrossRefGoogle Scholar
  12. Chen, Y., Souaiaia, T., & Chen, T. (2009). PerM: efficient mapping of short sequencing reads with periodic full sensitive spaced seeds. Bioinformatics, 25(19), 2514–2521.PubMedCentralPubMedCrossRefGoogle Scholar
  13. Chowdhury, A., et al. (2010). Next-generation E-health communication infrastructure using converged super-broadband optical and wireless access system. In World of Wireless Mobile and Multimedia Networks (WoWMoM), 2010 I.E. International Symposium on a. IEEE.Google Scholar
  14. Cisco Systems Inc. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2012–2017. Cisco 2012; Available from: http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.pdf.
  15. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.PubMedCrossRefGoogle Scholar
  16. Dinov, I. (2006). Statistics online computational resource. Journal of Statistical Software, 16(1), 1–16.Google Scholar
  17. Dinov, I. D., et al. (2002). Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage. IEEE Transactions on Information Technology in Biomedicine, 6(1), 73–85.PubMedCrossRefGoogle Scholar
  18. Dinov, I., et al. (2008). iTools: a framework for classification, categorization and integration of computational biology resources. PLoS One, 3(5), e2265.PubMedCentralPubMedCrossRefGoogle Scholar
  19. Dinov, I., et al. (2010). Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline. PLoS One, 5(9), e13070. doi: 10.1371/journal.pone.0013070.PubMedCentralPubMedCrossRefGoogle Scholar
  20. Dinov, I., et al. (2011). Applications of the pipeline environment for visual informatics and genomics computations. BMC Bioinformatics, 12(1), 304.PubMedCentralPubMedCrossRefGoogle Scholar
  21. Drossman, D., & Dumitrascu, D. (2006). Rome III: new standard for functional gastrointestinal disorders. Journal of Gastrointestinal and Liver Diseases: JGLD, 15(3), 237.PubMedGoogle Scholar
  22. Eliceiri, K. W., et al. (2012). Biological imaging software tools. Nature Methods, 9(7), 697–710.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Evans, A. (2002). Automated 3D analysis of large brain MRI databases. Neuropsychopharmacology: The Fifth Generation of Progress: American College of Neuropsychopharmacology. Nature Publishing, London: p. 301–313.Google Scholar
  24. Fani, N., G.D.T.E.B., et al. (2013). Fkbp5 and attention bias for threat: associations with hippocampal function and shape. JAMA Psychiatry: p. 1–9.Google Scholar
  25. Fennema-Notestine, C., et al. (2006). Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Human Brain Mapping, 27(2), 99–113.PubMedCentralPubMedCrossRefGoogle Scholar
  26. Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97(20), 11050–11055.PubMedCentralPubMedCrossRefGoogle Scholar
  27. Foster, K., Spicer, M., & Nathan, S. (2011). IBM infosphere streams: Assembling continuous insight in the information revolution. San Jose: International Technical Support Organization.Google Scholar
  28. Freire, J., et al. (2006). Managing rapidly-evolving scientific workflows, in IPAW 2006, L.M.a.I.F. (Eds.), Springer-Verlag: Berlin Heidelberg. p. 10–18.Google Scholar
  29. Friston, K. J., et al. (2011). Statistical parametric mapping: The analysis of functional brain images: The analysis of functional brain images: Academic Press.Google Scholar
  30. Fuller, S. H., & Millett, L. I. (2011). Computing performance: game over or next level? Computer, 44(1), 31–38.CrossRefGoogle Scholar
  31. Fuller, S. H., & Millett, L. I. (2011). The future of computing performance: game over or next level?: The National Academies Press.Google Scholar
  32. Geisser, M. E., Robinson, M. E., & Henson, C. D. (1994). The Coping Strategies Questionnaire and chronic pain adjustment: a conceptual and empirical reanalysis. The Clinical Journal of Pain.Google Scholar
  33. German, D. M., Adams, B., & Hassan, A. E. (2013). The Evolution of the R Software Ecosystem. In Software Maintenance and Reengineering (CSMR), 2013 17th European Conference on. IEEE.Google Scholar
  34. Glenn, T. C. (2011). Field guide to next–generation DNA sequencers. Molecular Ecology Resources, 11(5), 759–769.PubMedCrossRefGoogle Scholar
  35. Goecks, J., et al. (2010). Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biology, 11(8), R86.PubMedCentralPubMedCrossRefGoogle Scholar
  36. Gorgolewski, K., et al. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics. 5.Google Scholar
  37. Grabherr, M. G., et al. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29(7), 644–652.PubMedCentralPubMedCrossRefGoogle Scholar
  38. Grossman, R., & White, K. (2012). A vision for a biomedical cloud. Journal of Internal Medicine, 271(2), 122–130.PubMedCrossRefGoogle Scholar
  39. Gudmundsson, J., et al. (2012). A study based on whole-genome sequencing yields a rare variant at 8q24 associated with prostate cancer. Nature Genetics.Google Scholar
  40. Hach, F., et al. (2010). mrsFAST: a cache-oblivious algorithm for short-read mapping. Nature Methods, 7(8), 576–577.PubMedCentralPubMedCrossRefGoogle Scholar
  41. Hanselman, D., & Littlefield, B. C. (1997). Mastering MATLAB 5: A comprehensive tutorial and reference: Prentice Hall PTR.Google Scholar
  42. Hashizume, K., Fernandez, E. B., & Larrondo-Petrie M. M. (2012). A pattern for Software-as-a-Service in Clouds. In BioMedical Computing (BioMedCom), 2012 ASE/IEEE International Conference on. IEEE.Google Scholar
  43. Heinis, T. (2010). Workflow-based services: infrastructure for scientific applications: Suedwestdeutscher Verlag fuer Hochschulschriften.Google Scholar
  44. Hibar, D. P., et al. (2011). Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects. NeuroImage, 56(4), 1875–1891.PubMedCentralPubMedCrossRefGoogle Scholar
  45. Hibar, D. P., et al. (2012). Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N = 1345 young and elderly subjects. Brain Imaging and Behavior p. 1–14.Google Scholar
  46. Howe, D., et al. (2008). Big data: the future of biocuration. Nature, 455(7209), 47–50.PubMedCentralPubMedCrossRefGoogle Scholar
  47. Hu, D., et al. (2005). Unified SPM–ICA for fMRI analysis. NeuroImage, 25(3), 746–755.PubMedCrossRefGoogle Scholar
  48. Iglesias, J. E., et al. (2011). Robust brain extraction across datasets and comparison with publicly available methods. Medical Imaging, IEEE Transactions on, 30(9), 1617–1634.CrossRefGoogle Scholar
  49. Ihaka, R., & Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314.Google Scholar
  50. Jack, C. R., et al. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.PubMedCentralPubMedCrossRefGoogle Scholar
  51. Jiang, Y., & Johnson, G. A. (2010). Microscopic diffusion tensor imaging of the mouse brain. NeuroImage, 50(2), 465–471.PubMedCentralPubMedCrossRefGoogle Scholar
  52. Jiang, Z., et al. (2013). Sex-related differences of cortical thickness in patients with chronic abdominal pain. in press.Google Scholar
  53. Joshi, S. H., et al. (2012). Diffeomorphic sulcal shape analysis on the cortex. Medical Imaging, IEEE Transactions on. PP(99): p. 1–1.Google Scholar
  54. Kennedy, D. N. (2006). The internet analysis tools registry: a public resource for image analysis. Neuroinformatics, 4, 263–270.PubMedCrossRefGoogle Scholar
  55. Kent, W. J. (2002). BLAT—the BLAST-like alignment tool. Genome Research, 12(4), 656–664.PubMedCentralPubMedCrossRefGoogle Scholar
  56. Knobloch, J. (2013). Four decades of computing in subnuclear physics-from bubble chamber to LHC. arXiv preprint arXiv:1302.2974.Google Scholar
  57. Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357–359.PubMedCentralPubMedCrossRefGoogle Scholar
  58. Leung, K. T. K. (2011). Principal ranking meta-algorithms. Los Angeles: University of California.Google Scholar
  59. Leung, K., et al. (2008). IRMA: an image registration meta-algorithm - evaluating alternative algorithms with multiple metrics. SSDBM 2008. Springer-Verlag.Google Scholar
  60. Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25(14), 1754–1760.PubMedCentralPubMedCrossRefGoogle Scholar
  61. Li, H., & Durbin, R. (2010). Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics, 26(5), 589–595.PubMedCentralPubMedCrossRefGoogle Scholar
  62. Li, H., & Homer, N. (2010). A survey of sequence alignment algorithms for next-generation sequencing. Briefings in Bioinformatics, 11(5), 473–483.PubMedCentralPubMedCrossRefGoogle Scholar
  63. Li, H., Ruan, J., & Durbin, R. (2008). Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Research, 18(11), 1851–1858.PubMedCentralPubMedCrossRefGoogle Scholar
  64. Li, R., et al. (2009a). SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 25(15), 1966–1967.PubMedCrossRefGoogle Scholar
  65. Li, H., et al. (2009b). The Sequence alignment/map format and SAMtools. Bioinformatics, 25, 2078–2079.PubMedCentralPubMedCrossRefGoogle Scholar
  66. Li, R., et al. (2010). De novo assembly of human genomes with massively parallel short read sequencing. Genome Research, 20(2), 265.PubMedCentralPubMedCrossRefGoogle Scholar
  67. Lord, H. D. (1995). Improving the application development process with modular visualization environments. SIGGRAPH Computing Graph, 29(2), 10–12.CrossRefGoogle Scholar
  68. Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., Lee, E. A., Tao, J., & Zhao, Y. (2006). Scientific workflow management and the Kepler system. Concurrency and Computation: Practice and Experience, 18(10), 1039–1065.CrossRefGoogle Scholar
  69. Luo, X.-Z. J., Kennedy, D. N., & Cohen, Z. (2009). Neuroimaging informatics tools and resources clearinghouse (NITRC) resource announcement. Neuroinformatics, 7(1), 55–56.PubMedCrossRefGoogle Scholar
  70. Lynch, C. (2008). Big data: how do your data grow? Nature, 455(7209), 28–29.PubMedCrossRefGoogle Scholar
  71. Maraia, V. (2005). The build master: Microsoft’s software configuration management best practices: Addison-Wesley Professional.Google Scholar
  72. Marchini, J., et al. (2007). A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genetics, 39(7), 906–913.PubMedCrossRefGoogle Scholar
  73. Marusina, K. (2012). Big data requires big solutions. Genetic Engineering & Biotechnology News 32(15): p. 1, 34–40.Google Scholar
  74. Matellán Olivera, V. (2012). Studying the evolution of libre software projects using publicly available data; Available from: https://buleria.unileon.es/handle/10612/1796.
  75. McKenna, A., et al. (2010). The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.PubMedCentralPubMedCrossRefGoogle Scholar
  76. Meir, A., & Rubinsky, B. (2009). Distributed network, wireless and cloud computing enabled 3-D ultrasound; a new medical technology paradigm. PLoS One, 4(11), e7974.PubMedCentralPubMedCrossRefGoogle Scholar
  77. Mennes, M., et al. (2013). Making data sharing work: the FCP/INDI experience. Neuroimage, (0).Google Scholar
  78. Minelli, R., & Lanza, M. (2013). Software analytics for mobile applications–insights & lessons learned. In Software maintenance and reengineering (CSMR), 2013 17th European Conference on. IEEE.Google Scholar
  79. Mueller, S. G., et al. (2005). Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s disease neuroimaging initiative (ADNI). Alzheimer’s and Dementia: The Journal of the Alzheimer’s Association, 1(1), 55–66.CrossRefGoogle Scholar
  80. Novak, N. M., et al. (2012). EnigmaVis: online interactive visualization of genome-wide association studies of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Twin Research and Human Genetics: The Official Journal of the International Society for Twin Studies, 15(3), 414.CrossRefGoogle Scholar
  81. Ntziachristos, V. (2010). Going deeper than microscopy: the optical imaging frontier in biology. Nature Methods, 7(8), 603–614.PubMedCrossRefGoogle Scholar
  82. Oinn, T., et al. (2004). Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics, 20(17), 3045–3054.PubMedCrossRefGoogle Scholar
  83. Olabarriaga, S. D., Glatard, T., & de Boer, P. T. (2010). A virtual laboratory for medical image analysis. Information Technology in Biomedicine, IEEE Transactions on, 14(4), 979–985.CrossRefGoogle Scholar
  84. Olson, S. A. (2002). EMBOSS opens up sequence analysis. European molecular biology open software suite. Briefings in Bioinformatics, 3(1), 87.PubMedCrossRefGoogle Scholar
  85. Ostermann, S., et al. (2010). A performance analysis of EC2 cloud computing services for scientific computing. Cloud Computing, p. 115–131.Google Scholar
  86. Patel, V., et al. (2010a). Mesh-based spherical deconvolution: a flexible approach to reconstruction of non-negative fiber orientation distributions. NeuroImage, 51(3), 1071–1081.PubMedCentralPubMedCrossRefGoogle Scholar
  87. Patel, V., et al. (2010b). LONI MiND: metadata in NIfTI for DWI. NeuroImage, 51(2), 665–676.PubMedCentralPubMedCrossRefGoogle Scholar
  88. Pieper, S., Lorensen, B., Schroeder, W., Kikinis, R. (2006). The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. In Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on Google Scholar
  89. Purcell, S., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559–575.CrossRefGoogle Scholar
  90. Raymond, M., & Rousset, F. (1995). GENEPOP (Version 1.2): population genetics software for exact tests and ecumenicism. Journal of Heredity, 86(3), 248–249.Google Scholar
  91. Rimol, L. M., et al. (2010). Sex-dependent association of common variants of microcephaly genes with brain structure. Proceedings of the National Academy of Sciences, 107(1), 384–388.CrossRefGoogle Scholar
  92. Roy, D., et al. (2009). 3D cryo–imaging: a very high–resolution view of the whole mouse. The Anatomical Record, 292(3), 342–351.PubMedCentralPubMedCrossRefGoogle Scholar
  93. Rupp, K., & Selberherr, S. (2011). The economic limit to Moore’s Law. Semiconductor Manufacturing, IEEE Transactions on, 24(1), 1–4.CrossRefGoogle Scholar
  94. Rutherford, K., et al. (2000). Artemis: sequence visualization and annotation. Bioinformatics, 16(10), 944–945.PubMedCrossRefGoogle Scholar
  95. Scholl, I., et al. (2011). Challenges of medical image processing. Computer Science–Research and Development, 26(1–2), 5–13.CrossRefGoogle Scholar
  96. Shattuck, D., & Leahy R. (2000). BrainSuite: An automated cortical surface identification tool, in Medical image computing and computer-assisted intervention–MICCAI 2000, Lecture Notes in Computer Science. p. 50–61.Google Scholar
  97. Shen, L., et al. (2010). Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: a study of the ADNI cohort. NeuroImage, 53(3), 1051.PubMedCentralPubMedCrossRefGoogle Scholar
  98. Shi, Y., Thompson, P. M., Dinov, I. D., Osher, S., & Toga, A. W. (2007). Direct cortical mapping via solving partial differential equations on implicit surfaces. Medical Image Analysis, 11(3), 207–223.PubMedCentralPubMedCrossRefGoogle Scholar
  99. Smith, S. M., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Supplement 1), S208–S219.PubMedCrossRefGoogle Scholar
  100. Smith, S. M., et al. (2005). Variability in fMRI: a re–examination of inter–session differences. Human Brain Mapping, 24(3), 248–257.PubMedCrossRefGoogle Scholar
  101. Smith, D. R., et al. (2008). Rapid whole-genome mutational profiling using next-generation sequencing technologies. Genome Research, 18(10), 1638–1642.PubMedCentralPubMedCrossRefGoogle Scholar
  102. Sood, A., et al. (2012). Predicting the path of technological innovation: SAW vs. Moore, bass, gompertz, and kryder. Marketing Science, 31(6), 964–979.CrossRefGoogle Scholar
  103. Sperber, A. D., et al. (2007). A comparative reappraisal of the Rome II and Rome III diagnostic criteria: are we getting closer to the‘true’prevalence of irritable bowel syndrome? European Journal of Gastroenterology & Hepatology, 19(6), 441.CrossRefGoogle Scholar
  104. Spielberger, C. D. (2005). State-trait anxiety inventory: Wiley Online Library.Google Scholar
  105. Spjuth, O., et al. (2007). Bioclipse: an open source workbench for chemo- and bioinformatics. BMC Bioinformatics, 8(1), 59.PubMedCentralPubMedCrossRefGoogle Scholar
  106. Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848–853.PubMedCentralPubMedCrossRefGoogle Scholar
  107. Sultan, F., & Braitenberg, V. (1993). Shapes and sizes of different mammalian cerebella. A study in quantitative comparative neuroanatomy. Journal für Hirnforschung, 34(1), 79.PubMedGoogle Scholar
  108. Talley, N., et al. (1995). Initial validation of a bowel symptom questionnaire* and measurement of chronic gastrointestinal symptoms in Australians. Internal Medicine Journal, 25(4), 302–308.Google Scholar
  109. Tang, Y., et al. (2010). The construction of a Chinese MRI brain atlas: a morphometric comparison study between Chinese and caucasian cohorts. NeuroImage, 51(1), 33–41.PubMedCentralPubMedCrossRefGoogle Scholar
  110. Taylor, I., Shields, M., Wang, I., & Harrison, A. (2006). Visual grid workflow in triana. Journal of Grid Computing, 3, 153–169.CrossRefGoogle Scholar
  111. Tenenbaum, J. D., et al. (2011). The biomedical resource ontology (BRO) to enable resource discovery in clinical and translational research. Journal of Biomedical Informatics, 44(1), 137–145.PubMedCentralPubMedCrossRefGoogle Scholar
  112. Thompson, P. M., et al. (2013). Genetics of the connectome. Neuroimage.Google Scholar
  113. Toga, A. W., et al. (2012). The center for computational biology: resources, achievements, and challenges. Journal of the American Medical Informatics Association, 19(2), 202–206.PubMedCentralPubMedCrossRefGoogle Scholar
  114. Tohka, J., et al. (2007). Genetic algorithms for finite mixture model based voxel classification in neuroimaging. Medical Imaging, IEEE Transactions on, 26(5), 696–711.CrossRefGoogle Scholar
  115. Tohka, J., et al. (2010). Brain MRI tissue classification based on local Markov random fields. Magnetic Resonance Imaging, 28(4), 557–573.PubMedCentralPubMedCrossRefGoogle Scholar
  116. Torri, F., et al. (2012). Next generation sequence analysis and computational genomics using graphical pipeline workflows. Genes, 3(3), 545–575.PubMedCentralPubMedCrossRefGoogle Scholar
  117. Truong, H.-L., & Dustdar, S. (2012). A survey on cloud-based sustainability governance systems. International Journal of Web Information Systems, 8(3), 278–295.Google Scholar
  118. Tu, Z., et al. (2008). Brain anatomical structure segmentation by hybrid discriminative/generative models. IEEE Transactions on Medical Imaging, 27(4), 495–508.PubMedCentralPubMedCrossRefGoogle Scholar
  119. Van Essen, D. C., et al. (2012). The human connectome project: a data acquisition perspective. NeuroImage, 62(4), 2222–2231.PubMedCentralPubMedCrossRefGoogle Scholar
  120. Walter, C. (2005). Kryder’s law. Scientific American, 293(2), 32–33.PubMedCrossRefGoogle Scholar
  121. Wang, W., & Guo, L. (2012). The Development and Applications of Wireless Streaming Media Technology. In Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on. IEEE.Google Scholar
  122. Wang, Q., et al. (2005). Construction and validation of mean shape atlas templates for atlas-based brain image segmentation. In Information Processing in Medical Imaging. Springer.Google Scholar
  123. Wang, R., et al. (2007). Diffusion toolkit: a software package for diffusion imaging data processing and tractography. In Proc Intl Soc Mag Reson Med.Google Scholar
  124. Wang, K., et al. (2007b). PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Research, 17(11), 1665–1674.PubMedCentralPubMedCrossRefGoogle Scholar
  125. Ware, J. E., Jr., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220.PubMedCrossRefGoogle Scholar
  126. Wen, X., et al. (2012). Comparison of open-source cloud management platforms: OpenStack and OpenNebula. In Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on. IEEE.Google Scholar
  127. White, T. (2012). Hadoop: The definitive guide: O’Reilly Media.Google Scholar
  128. Woods, R. P., Dapretto, M., Sicotte, N. L., Toga, A. W., & Mazziotta, J. C. (1999). Creation and use of a Talairach-compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data. Human Brain Mapping, 8(2–3), 73–79.PubMedCrossRefGoogle Scholar
  129. Xing, W., et al. (2013). Probabilistic MRI brain anatomical atlases based on 1,000 Chinese subjects. PLoS One, 8(1), e50939.PubMedCentralCrossRefGoogle Scholar
  130. Zerbino, D. R., & Birney, E. (2008). Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Research, 18(5), 821–829.PubMedCentralPubMedCrossRefGoogle Scholar
  131. Zhang, W., et al. (2011). A practical comparison of de novo genome assembly software tools for next-generation sequencing technologies. PLoS One, 6(3), e17915.PubMedCentralPubMedCrossRefGoogle Scholar
  132. Zigmond, A. S., & Snaith, R. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361–370.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Ivo D. Dinov
    • 1
    • 2
  • Petros Petrosyan
    • 1
  • Zhizhong Liu
    • 1
  • Paul Eggert
    • 1
    • 4
  • Alen Zamanyan
    • 1
  • Federica Torri
    • 2
    • 3
  • Fabio Macciardi
    • 2
    • 3
  • Sam Hobel
    • 1
  • Seok Woo Moon
    • 5
  • Young Hee Sung
    • 6
  • Zhiguo Jiang
    • 9
    • 10
  • Jennifer Labus
    • 7
  • Florian Kurth
    • 7
  • Cody Ashe-McNalley
    • 7
  • Emeran Mayer
    • 7
  • Paul M. Vespa
    • 8
  • John D. Van Horn
    • 1
  • Arthur W. Toga
    • 1
    • 2
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Laboratory of Neuro Imaging (LONI), David Geffen School of Medicine at UCLAUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Biomedical Informatics Research Network (BIRN), Information Sciences InstituteUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineUSA
  4. 4.Department of Computer ScienceUniversity of California, Los AngelesLos AngelesUSA
  5. 5.Department of PsychiatryKonkuk University Chungju HospitalSeoulSouth Korea
  6. 6.Department of NeurologyGachon University, Gil HospitalIncheonSouth Korea
  7. 7.Center for Neurobiology of StressUniversity of California, Los AngelesLos AngelesUSA
  8. 8.Brain Injury Research CenterRonald Reagan UCLA Medical CenterLos AngelesUSA
  9. 9.Human Performance and Engineering LaboratoryKesser Foundation Research CenterWest OrangeUSA
  10. 10.Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkUSA

Personalised recommendations