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

Brain Imaging and Behavior

Volumn 8, Issue 2, 2014, Pages 311-322

  Dinov, Ivo D ^a,b   Petrosyan, Petros ^a   Liu, Zhizhong ^a   Eggert, Paul ^a,d   Zamanyan, Alen ^a   Torri, Federica ^b,c   Macciardi, Fabio ^b,c   Hobel, Sam ^a   Moon, Seok Woo ^e   Sung, Young Hee ^f   Jiang, Zhiguo ^i,j   Labus, Jennifer ^g   Kurth, Florian ^g   Ashe McNalley, Cody ^g   Mayer, Emeran ^g   Vespa, Paul M ^h   Van Horn, John D ^a   Toga, Arthur W ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d Department of Civil and Environmental Engineering   (United States)

^e Konkuk University Hospital   (South Korea)

^f Gachon University   (South Korea)

^g UNIVERSITY OF CALIFORNIA   (United States)

^h UCLA MEDICAL CENTER   (United States)

ⁱ Human Performance and Engineering Laboratory Kesser Foundation Research Center   (United States)

^j NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

more..

Author keywords

Aging; Alzheimer's disease; Analysis; Big data; Computation solutions; Genetics; IBS; Neuroimaging; Pain; Parkinson's disease; Pipeline; Shape; Visualization; Volume; Workflows

Indexed keywords

GLUCOCORTICOID RECEPTOR;

ALZHEIMER DISEASE; ARTICLE; COMPUTER; COMPUTER PROGRAM; DIFFUSION TENSOR IMAGING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENETIC ASSOCIATION; GENETICS; GENOMICS; GENOTYPE; HUMAN; IMAGE PROCESSING; INFORMATION PROCESSING; IRRITABLE COLON; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PAIN; POSTTRAUMATIC STRESS DISORDER; PRIORITY JOURNAL; SEQUENCE ALIGNMENT; SINGLE NUCLEOTIDE POLYMORPHISM; WORKFLOW; ALGORITHM; BIOLOGY; BRAIN; BRAIN INJURY; CHRONIC PAIN; COMPLICATION; FEMALE; INFORMATION DISSEMINATION; INTERNET; MALE; MIDDLE AGED; PATHOLOGY; PATHOPHYSIOLOGY; PROCEDURES;

ALGORITHMS; ALZHEIMER DISEASE; BRAIN; BRAIN INJURIES; CHRONIC PAIN; COMPUTATIONAL BIOLOGY; FEMALE; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; INFORMATION DISSEMINATION; INTERNET; IRRITABLE BOWEL SYNDROME; MALE; MIDDLE AGED; NEUROIMAGING; SOFTWARE; WORKFLOW;

EID: 84899952512     PISSN: 19317557     EISSN: 19317565     Source Type: Journal    
DOI: 10.1007/s11682-013-9248-x     Document Type: Article

References (132)

1. Alarifi, S., & Wolthusen S. (2013). Anomaly detection for ephemeral cloud IaaS virtual machines, In Network and system security, Springer. p. 321-335.
2. Avants, B. B., Tustison, N., & Song, G. (2009). Advanced Normalization Tools (ANTS). Insight J.
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.
4. Berger, B., Peng, J., & Singh, M. (2013). Computational solutions for omics data. Nature Reviews Genetics, 14(5), 333-346.
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.
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.
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.
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.
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.
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.
11. Chen, R., & Herskovits, E. H. (2005). Graphical-model-based morphometric analysis. Medical Imaging, IEEE Transactions on, 24(10), 1237-1248.
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.
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.
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.
16. Dinov, I. (2006). Statistics online computational resource. Journal of Statistical Software, 16(1), 1-16.
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.
18. Dinov, I., et al. (2008). iTools: a framework for classification, categorization and integration of computational biology resources. PLoS One, 3(5), e2265.
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.
20. Dinov, I., et al. (2011). Applications of the pipeline environment for visual informatics and genomics computations. BMC Bioinformatics, 12(1), 304.
21. Drossman, D., & Dumitrascu, D. (2006). Rome III: new standard for functional gastrointestinal disorders. Journal of Gastrointestinal and Liver Diseases: JGLD, 15(3), 237.
22. Eliceiri, K. W., et al. (2012). Biological imaging software tools. Nature Methods, 9(7), 697-710.
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.
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.
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.
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.
27. Foster, K., Spicer, M., & Nathan, S. (2011). IBM infosphere streams: Assembling continuous insight in the information revolution. San Jose: International Technical Support Organization.
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.
29. Friston, K. J., et al. (2011). Statistical parametric mapping: The analysis of functional brain images: The analysis of functional brain images: Academic Press.
30. Fuller, S. H., & Millett, L. I. (2011). Computing performance: game over or next level? Computer, 44(1), 31-38.
31. Fuller, S. H., & Millett, L. I. (2011). The future of computing performance: game over or next level?: The National Academies Press.
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.
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.
34. Glenn, T. C. (2011). Field guide to next-generation DNA sequencers. Molecular Ecology Resources, 11(5), 759-769.
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.
36. Gorgolewski, K., et al. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics. 5.
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.
38. Grossman, R., & White, K. (2012). A vision for a biomedical cloud. Journal of Internal Medicine, 271(2), 122-130.
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.
40. Hach, F., et al. (2010). mrsFAST: a cache-oblivious algorithm for short-read mapping. Nature Methods, 7(8), 576-577.
41. Hanselman, D., & Littlefield, B. C. (1997). Mastering MATLAB 5: A comprehensive tutorial and reference: Prentice Hall PTR.
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.
43. Heinis, T. (2010). Workflow-based services: infrastructure for scientific applications: Suedwestdeutscher Verlag fuer Hochschulschriften.
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.
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.
46. Howe, D., et al. (2008). Big data: the future of biocuration. Nature, 455(7209), 47-50.
47. Hu, D., et al. (2005). Unified SPM-ICA for fMRI analysis. NeuroImage, 25(3), 746-755.
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.
49. Ihaka, R., & Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299-314.
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.
51. Jiang, Y., & Johnson, G. A. (2010). Microscopic diffusion tensor imaging of the mouse brain. NeuroImage, 50(2), 465-471.
52. Jiang, Z., et al. (2013). Sex-related differences of cortical thickness in patients with chronic abdominal pain. in press.
53. Joshi, S. H., et al. (2012). Diffeomorphic sulcal shape analysis on the cortex. Medical Imaging, IEEE Transactions on. PP(99): p. 1-1.
54. Kennedy, D. N. (2006). The internet analysis tools registry: a public resource for image analysis. Neuroinformatics, 4, 263-270.
55. Kent, W. J. (2002). BLAT-the BLAST-like alignment tool. Genome Research, 12(4), 656-664.
56. Knobloch, J. (2013). Four decades of computing in subnuclear physics-from bubble chamber to LHC. arXiv preprint arXiv: 1302. 2974.
57. Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357-359.
58. Leung, K. T. K. (2011). Principal ranking meta-algorithms. Los Angeles: University of California.
59. Leung, K., et al. (2008). IRMA: an image registration meta-algorithm - evaluating alternative algorithms with multiple metrics. SSDBM 2008. Springer-Verlag.
60. Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760.
61. Li, H., & Durbin, R. (2010). Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics, 26(5), 589-595.
62. Li, H., & Homer, N. (2010). A survey of sequence alignment algorithms for next-generation sequencing. Briefings in Bioinformatics, 11(5), 473-483.
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.
64. Li, R., et al. (2009a). SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 25(15), 1966-1967.
65. Li, H., et al. (2009b). The Sequence alignment/map format and SAMtools. Bioinformatics, 25, 2078-2079.
66. Li, R., et al. (2010). De novo assembly of human genomes with massively parallel short read sequencing. Genome Research, 20(2), 265.
67. Lord, H. D. (1995). Improving the application development process with modular visualization environments. SIGGRAPH Computing Graph, 29(2), 10-12.
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.
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.
70. Lynch, C. (2008). Big data: how do your data grow? Nature, 455(7209), 28-29.
71. Maraia, V. (2005). The build master: Microsoft's software configuration management best practices: Addison-Wesley Professional.
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.
73. Marusina, K. (2012). Big data requires big solutions. Genetic Engineering & Biotechnology News 32(15): p. 1, 34-40.
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.
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.
77. Mennes, M., et al. (2013). Making data sharing work: the FCP/INDI experience. Neuroimage, (0).
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.
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.
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.
81. Ntziachristos, V. (2010). Going deeper than microscopy: the optical imaging frontier in biology. Nature Methods, 7(8), 603-614.
82. Oinn, T., et al. (2004). Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics, 20(17), 3045-3054.
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.
84. Olson, S. A. (2002). EMBOSS opens up sequence analysis. European molecular biology open software suite. Briefings in Bioinformatics, 3(1), 87.
85. Ostermann, S., et al. (2010). A performance analysis of EC2 cloud computing services for scientific computing. Cloud Computing, p. 115-131.
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.
87. Patel, V., et al. (2010b). LONI MiND: metadata in NIfTI for DWI. NeuroImage, 51(2), 665-676.
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.
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.
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.
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.
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.
93. Rupp, K., & Selberherr, S. (2011). The economic limit to Moore's Law. Semiconductor Manufacturing, IEEE Transactions on, 24(1), 1-4.
94. Rutherford, K., et al. (2000). Artemis: sequence visualization and annotation. Bioinformatics, 16(10), 944-945.
95. Scholl, I., et al. (2011). Challenges of medical image processing. Computer Science-Research and Development, 26(1-2), 5-13.
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.
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.
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.
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.
100. Smith, S. M., et al. (2005). Variability in fMRI: a re-examination of inter-session differences. Human Brain Mapping, 24(3), 248-257.
101. Smith, D. R., et al. (2008). Rapid whole-genome mutational profiling using next-generation sequencing technologies. Genome Research, 18(10), 1638-1642.
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.
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.
104. Spielberger, C. D. (2005). State-trait anxiety inventory: Wiley Online Library.
105. Spjuth, O., et al. (2007). Bioclipse: an open source workbench for chemo- and bioinformatics. BMC Bioinformatics, 8(1), 59.
106. Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848-853.
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.
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.
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.
110. Taylor, I., Shields, M., Wang, I., & Harrison, A. (2006). Visual grid workflow in triana. Journal of Grid Computing, 3, 153-169.
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.
112. Thompson, P. M., et al. (2013). Genetics of the connectome. Neuroimage.
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.
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.
115. Tohka, J., et al. (2010). Brain MRI tissue classification based on local Markov random fields. Magnetic Resonance Imaging, 28(4), 557-573.
116. Torri, F., et al. (2012). Next generation sequence analysis and computational genomics using graphical pipeline workflows. Genes, 3(3), 545-575.
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.
118. Tu, Z., et al. (2008). Brain anatomical structure segmentation by hybrid discriminative/generative models. IEEE Transactions on Medical Imaging, 27(4), 495-508.
119. van Essen, D. C., et al. (2012). The human connectome project: a data acquisition perspective. NeuroImage, 62(4), 2222-2231.
120. Walter, C. (2005). Kryder's law. Scientific American, 293(2), 32-33.
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.
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.
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.
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.
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.
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.
127. White, T. (2012). Hadoop: The definitive guide: O'Reilly Media.
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.
129. Xing, W., et al. (2013). Probabilistic MRI brain anatomical atlases based on 1, 000 Chinese subjects. PLoS One, 8(1), e50939.
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.
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.
132. Zigmond, A. S., & Snaith, R. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361-370.