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http://dx.doi.org/10.4051/ibc.2012.4.3.0007

Combining Neuroinformatics Databases for Multi-Level Analysis of Brain Disorders  

Yu, Ha Sun (Department of Bio and Brain Engineering, KAIST)
Bang, Joon (Winchester College)
Jo, Yousang (Department of Bio and Brain Engineering, KAIST)
Lee, Doheon (Department of Bio and Brain Engineering, KAIST)
Publication Information
Interdisciplinary Bio Central / v.4, no.3, 2012 , pp. 7.1-7.8 More about this Journal
Abstract
With the development of many methods of studying the brain, the field of neuroscience has generated large amounts of information obtained from various techniques: imaging techniques, electrophysiological techniques, techniques for analyzing brain connectivity, techniques for getting molecular information of the brain, etc. A plenty of neuroinformatics databases have been made for storing and sharing this useful information and those databases can be publicly accessed by researchers as needed. However, since there are too many neuroinformatics databases, it is difficult to find the appropriate database depending on the needs of researcher. Moreover, many researchers in neuroscience fields are unfamiliar with using neuroinformatics databases for their studies because data is too diverse for neuroscientists to handle this and there is little precedent for using neuroinformatics databases for their research. Therefore, in this article, we review databases in the field of neuroscience according to both their methods for obtaining data and their objectives to help researchers to use databases properly. We also introduce major neuroinformatics databases for each type of information. In addition, to show examples of novel uses of neuroinformatics databases, we represent several studies that combine neuroinformatics databases of different information types and discover new findings. Finally, we conclude our paper with the discussion of potential applications of neuroinformatics databases.
Keywords
neuroscience; database; neuroinformatics databases; usages of neuroinformatics databases; application of neuroinformatics databases;
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1 Kempton, M.J., Salvador, Z., Munafo, M.R., Geddes, J.R., Simmons, A., Frangou, S., and Williams, S.C. (2011). Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder. Arch Gen Psychiatry 68, 675-690.   DOI
2 Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., and Buckner, R.L. (2007). Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19, 1498-1507.   DOI
3 Torrey, E.F., Webster, M., Knable, M., Johnston, N., and Yolken, R.H. (2000). The stanley foundation brain collection and neuropathology consortium. Schizophr Res 44, 151-155.   DOI
4 Taccioli, C., Tegner, J., Maselli, V., Gomez-Cabrero, D., Altobelli, G., Emmett, W., Lescai, F., Gustincich, S., and Stupka, E. (2011). ParkDB: a Parkinson's disease gene expression database. Database (Oxford) 2011, bar007.
5 Lill, C.M., Roehr, J.T., McQueen, M.B., Kavvoura, F.K., Bagade, S., Schjeide, B.M., Schjeide, L.M., Meissner, E., Zauft, U., Allen, N.C., et al. (2012). Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics: The PDGene database. PLoS Genet 8, e1002548.   DOI
6 Allen, N.C., Bagade, S., McQueen, M.B., Ioannidis, J.P., Kavvoura, F.K., Khoury, M.J., Tanzi, R.E., and Bertram, L. (2008). Systematic meta-analyses and field synopsis of genetic association studies in schizophrenia: the SzGene database. Nat Genet 40, 827-834.   DOI   ScienceOn
7 Lill, C.M., Abel, O., Bertram, L., and Al-Chalabi, A. (2011). Keeping up with genetic discoveries in amyotrophic lateral sclerosis: the ALSoD and ALSGene databases. Amyotroph Lateral Scler 12, 238-249.   DOI
8 Lill, C.M., R.J., McQueen, M.B., Bagade, S., Schjeide, B.M., Zipp, F., and Bertram, L. The MSGene Database. Alzheimer Research Forum.
9 Kotter, R. (2001). Neuroscience databases: tools for exploring brain structure-function relationships. Philos Trans R Soc Lond B Biol Sci 356, 1111-1120.   DOI
10 Yarkoni, T., Poldrack, R.A., Van Essen, D.C., and Wager, T.D. (2010). Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends Cogn Sci 14, 489-496.   DOI
11 French, L., and Pavlidis, P. (2007). Informatics in neuroscience. Brief Bioinform 8, 446-456.   DOI
12 http://www.med.harvard.edu/AANLIB/home.html.
13 Akil, H., Martone, M.E., and Van Essen, D.C. (2011). Challenges and opportunities in mining neuroscience data. Science 331, 708-712.   DOI
14 http://www.brain-development.org/.
15 http://www.brainmuseum.org/index.html.
16 Laird, A.R., Lancaster, J.L., and Fox, P.T. (2005). BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3, 65-78.   DOI
17 Mikula, S., Trotts, I., Stone, J.M., and Jones, E.G. (2007). Internet-enabled high-resolution brain mapping and virtual microscopy. Neuroimage 35, 9-15.   DOI
18 http://connectomes.org/.
19 Ascoli, G.A., Donohue, D.E., and Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. J Neurosci 27, 9247-9251.   DOI
20 http://www.carmen.org.uk/.
21 http://crcns.org/.
22 http://openconnectomeproject.org/.
23 Stephan, K.E., Kamper, L., Bozkurt, A., Burns, G.A., Young, M.P., and Kotter, R. (2001). Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). Philos Trans R Soc Lond B Biol Sci 356, 1159-1186.   DOI
24 Bota, M., Dong, H.W., and Swanson, L.W. (2005). Brain architecture management system. Neuroinformatics 3, 15-48.   DOI
25 Lein, E.S., Hawrylycz, M.J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A.F., Boguski, M.S., Brockway, K.S., Byrnes, E.J., et al. (2007). Genome- wide atlas of gene expression in the adult mouse brain. Nature 445, 168-176.   DOI
26 Jesse Brown, J.R., and Susan Bookheimer. (2011). Networks in the Cloud: Web-based Neuroimaging Brain Network Analysis and Data Sharing. Organization for Human Brain Mapping, 2011, Quebec City, Quebec.
27 Edgar, R., Domrachev, M., and Lash, A.E. (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30, 207-210.   DOI
28 Rocca-Serra, P., Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., Contrino, S., Vilo, J., Abeygunawardena, N., Mukherjee, G., Holloway, E., et al. (2003). ArrayExpress: a public database of gene expression data at EBI. C R Biol 326, 1075-1078.   DOI
29 Bohland, J.W., Bokil, H., Pathak, S.D., Lee, C.K., Ng, L., Lau, C., Kuan, C., Hawrylycz, M., and Mitra, P.P. (2010). Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy. Methods 50, 105-112.   DOI
30 Kang, H.J., Kawasawa, Y.I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A.M., Pletikos, M., Meyer, K.A., Sedmak, G., et al. (2011). Spatio-temporal transcriptome of the human brain. Nature 478, 483-489.   DOI
31 Cahoy, J.D., Emery, B., Kaushal, A., Foo, L.C., Zamanian, J.L., Christopherson, K.S., Xing, Y., Lubischer, J.L., Krieg, P.A., Krupenko, S.A., et al. (2008). A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci 28, 264-278.   DOI
32 Jack, C.R. Jr., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., J, L.W., Ward, C., et al. (2008). The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27, 685-691.   DOI
33 http://www.cdc.gov/nchs/NHANES.htm.
34 Bertram, L., McQueen, M.B., Mullin, K., Blacker, D., and Tanzi, R.E. (2007). Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet 39, 17-23.   DOI
35 Tang, S., Zhang, Z., Kavitha, G., Tan, E.K., and Ng, S.K. (2009). MDPD: an integrated genetic information resource for Parkinson's disease. Nucleic Acids Res 37, D858-862.   DOI
36 Patel, C.J., Bhattacharya, J., and Butte, A.J. (2010). An Environment- Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS One 5, e10746.   DOI   ScienceOn
37 Gardner, D., Akil, H., Ascoli, G.A., Bowden, D.M., Bug, W., Donohue, D.E., Goldberg, D.H., Grafstein, B., Grethe, J.S., Gupta, A., et al. (2008). The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics 6, 149-160.   DOI
38 http://neurolex.org/wiki/Main_Page.
39 Bowden, D.M., Song, E., Kosheleva, J., and Dubach, M.F. (2012). NeuroNames: an ontology for the BrainInfo portal to neuroscience on the web. Neuroinformatics 10, 97-114.   DOI
40 French, L., and Pavlidis, P. (2011). Relationships between gene expression and brain wiring in the adult rodent brain. PLoS Comput Biol 7, e1001049.   DOI
41 Wolf, L., Goldberg, C., Manor, N., Sharan, R., and Ruppin, E. (2011). Gene expression in the rodent brain is associated with its regional connectivity. PLoS Comput Biol 7, e1002040.   DOI
42 Park, B., Lee, W., and Han, K. (2012). Modeling the interactions of Alzheimer- related genes from the whole brain microarray data and diffusion tensor images of human brain. BMC Bioinformatics 13 Suppl 7, S10.