From online resources to collaborative global neuroscience research: where are we heading?

Authors

  • Pike-See Cheah (1) Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. (2) NeuroBiology & Genetics Group, Genetics and Regenerative Medicine Research Centre, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. https://orcid.org/0000-0003-2181-2502
  • King-Hwa Ling (1) NeuroBiology & Genetics Group, Genetics and Regenerative Medicine Research Centre, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. (2) Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. https://orcid.org/0000-0002-3968-7263
  • Eric Tatt Wei Ho Center for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Perak, Malaysia. https://orcid.org/0000-0002-7590-1028

DOI:

https://doi.org/10.31117/neuroscirn.v3i3.51

Keywords:

neuroinformatics, machine learning, bioinformatics, brain project, online resources, databases

Abstract

Neuroscience has emerged as a richly transdisciplinary field, poised to leverage potential synergies with information technology. To investigate the complex nervous system in its normal function and the disease state, researchers in the field are increasingly reliant on generating, sharing and analyzing diverse data from multiple experimental paradigms at multiple spatial and temporal scales. There is growing recognition that brain function must be investigated from a systems perspective. This requires an integrated analysis of genomic, proteomic, anatomical, functional, topological and behavioural information to arrive at accurate scientific conclusions. The integrative neuroinformatics approaches for exploring complex structure-function relationships in the nervous system have been extensively reviewed. To support neuroscience research, the neuroscientific community also generates and maintains web-accessible databases of experimental and computational data and innovative software tools. Neuroinformatics is an emerging sub-field of neuroscience which focuses on addressing the unique technological and computational challenges to integrate and analyze the increasingly high-volume, multi-dimensional, and fine-grain data generated from neuroscience experiments. The most visible contributions from neuroinformatics include the myriad reference atlases of brain anatomy (human and other mammals such as rodents, primates and pig), gene and protein sequences and the bioinformatics software tools for alignment, matching and identification. Other neuroinformatics initiatives include the various open-source preprocessing and processing software and workflows for data analysis as well as the specifications for data format and software interoperability that allow seamless exchange of data between labs, software tools and modalities.

References

Abrams, M., Bjaalie, J., Das, S., Egan, G., Ghosh, S., Goscinski, W., Grethe, J., Kotaleski, J., Ho, E., Kennedy, D., Lanyon, L., Leergaard, T., Mayberg, H., Milanesi, L., Mouček, R., Poline, J.-B., Roy, P., Tang, T., Tiesinga, P., … Martone, M. (2019). A standards organization for Open and FAIR neuroscience: the International Neuroinformatics Coordinating Facility. OSF Preprints. https://doi.org/10.31219/OSF.IO/3RT9B

Campbell, P. (2009). Data’s shameful neglect. Nature, 461(7261), 7261. https://doi.org/10.1038/461145a

Frackowiak, R., & Markram, H. (2015). The future of human cerebral cartography: A novel approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1668). https://doi.org/10.1098/rstb.2014.0171

Huerta, M. F., Koslow, S. H., & Leshner, A. I. (1993). The Human Brain Project : an international resource. Trends in Neurosciences, 16(11), 436–438. https://doi.org/10.1016/0166-2236(93)90069-X

Kötter, R. (2001). Neuroscience databases: Tools for exploring brain structure-function relationships. Philosophical Transactions of the Royal Society B: Biological Sciences, 356(1412), 1111–1120. https://doi.org/10.1098/rstb.2001.0902

Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Ferrante, T. C., Terry, R., Turczyk, B. M., Yang, J. L., Lee, H. S., Aach, J., Zhang, K., & Church, G. M. (2015). Fluorescent in situ sequencing ( FISSEQ ) of RNA for gene expression profiling in intact cells and tissues. Nature Protocols, 10(3), 442–458. https://doi.org/10.1038/nprot.2014.191

Markram, H. (2006). The Blue Brain Project. Nature Reviews Neuroscience, 7, 153–160. https://doi.org/1038/nrn1848

Polavaram, S., & Ascoli, G. (2015). Neuroinformatics. Scholarpedia, 10(11), 1312. https://doi.org/10.4249/scholarpedia.1312

Rao, R. A., Cecchi, G. A., & Kaplan, E. (2015). Editorial: Towards an integrated approach to measurement, analysis and modeling of cortical networks. Frontiers in Neural Circuits, 9(OCTOBER), 1–4. https://doi.org/10.3389/fncir.2015.00061

Saunders, A., Macosko, E. Z., Wysoker, A., Brumbaugh, S., Kulp, D., Mccarroll, S. A., Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., & Krienen, F. M. (2018). Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain Resource Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain. Cell, 174(4), 1015–1030. https://doi.org/10.1016/j.cell.2018.07.028

Shepherd, G. M., Mirsky, J. S., Healy, M. D., Singer, M. S., Skoufos, E., Hines, M. S., Nadkarni, P. M., & Miller, P. L. (1998). The Human Brain Project : neuroinformatics tools for integrating , searching and modeling multidisciplinary neuroscience data. Trends in Neurosciences, 21(11), 460–468. https://doi.org/10.1016/s0166-2236(98)01300-9

The Tabula Muris Consortium, Pisco, A. O., & Schaum, N. (2019). A Single Cell Transcriptomic Atlas Characterizes Aging Tissues in the Mouse. BioRxiv. https://doi.org/https://doi.org/10.1101/661728

Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., Nolan, G. P., Bava, F., & Deisseroth, K. (2018). Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science, 361(6400), eaat5691. https://doi.org/10.1126/science.aat5691

Wertheim, S. L., & Sidman, R. L. (1991). Databases for neuroscience. Nature, 354(6348), 88–89. https://doi.org/10.1038/354088a0

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). Comment: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 1–9. https://doi.org/10.1038/sdata.2016.18

Ximerakis, M., Lipnick, S. L., Innes, B. T., Simmons, S. K., Adiconis, X., Dionne, D., Mayweather, B. A., Nguyen, L., Niziolek, Z., Ozek, C., Butty, V. L., Isserlin, R., Buchanan, S. M., Levine, S. S., Regev, A., Bader, G. D., Levin, J. Z., & Rubin, L. L. (2019). Single-cell transcriptomic profiling of the aging mouse brain. Nature Neuroscience, 22(10), 1696–1708. https://doi.org/10.1038/s41593-019-0491-3

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Published

2020-07-21

How to Cite

Cheah, P.-S., Ling, K.-H., & Ho, E. T. W. (2020). From online resources to collaborative global neuroscience research: where are we heading?. Neuroscience Research Notes, 3(3), 1–8. https://doi.org/10.31117/neuroscirn.v3i3.51