Informatics and Applications
2020, Volume 14, Issue 1, pp 40-47
NEUROPHYSIOLOGY AS A SUBJECT DOMAIN FOR DATA INTENSIVE PROBLEM SOLVING
- D. O. Briukhov
- S. A. Stupnikov
- D. Yu. Kovalev
- I. A. Shanin
Abstract
The goal of this survey is to analyze neurophysiology as a data intensive domain. Nowadays, the number of researches on the human brain is increasing. International projects and researches are aimed at improvement of the understanding of the human brain function. The amount of data obtained in typical laboratories in the field of neurophysiology is growing exponentially. The data are represented using a large number of various formats.
This requires creation of infrastructures, databases, and websites that provide unified access to data and support the exchange of data between researchers all over the world. Specific methods and tools forming the field of neuroinformatics (that is, an intersection of neurophysiology and computer science) are used to analyze collected data and to solve neurophysiological problems. These methods include, in particular, statistical analysis, machine learning, and neural networks.
[+] References (31)
- BRAIN Initiative Home Page. Available at: https:// braininitiative.nih.gov/ (accessed November 12, 2019)
- Human Brain Project Home Page. Available at: https:// www.humanbrainproject.eu (accessed November 12, 2019).
- Elam, J. S., and D. Van Essen. 2013. Human Connectome project. Encyclopedia of computational neuroscience. Eds.
D. Jaeger and R. Jung. New York, NY: Springer. 4 p.
- Brunner, C., B. Blankertz, F Cincotti, et al. 2014. BNCI Horizon 2020 - towards a roadmap for brain/neural com-puter interaction. 8th Conference (International) on Uni- versalAccess in Human-ComputerInteraction Proceedings. Lecture notes in computer science ser. Springer. 8513: 475-486.
- Jiang, T., Y. Liu, F Shi, N. Shu, B. Liu, J. Jiang, and Y. Zhou. 2008. Multimodal magnetic resonance imaging for brain disorders: advances and perspectives. Brain Imaging Behav. 2(4):249-257.
- Van Horn, J. D., and A.W Toga. 2009. Multisite neu-roimaging trials. Curr. Opin. Neurol. 22(4):370-378.
- Jack, C. R., M.A. Bernstein, N. C. Fox, et al. 2008. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4):685-691.
- Biswal, B. B., M. Mennes, X. N. Zuo, et al. 2010. Toward discovery science of human brain function. P. Natl. Acad. Sci. USA 107(10):4734-4739.
- Poldrack, R. A., D.M. Barch, J. Mitchell, et al. 2013. Toward open sharing of task-based fMRI data: The OpenfMRI project. Front. Neuroinform. 7:12.
- Hodge, M. R., W. Horton, T. Brown, et al. 2016. ConnectomeDB-sharing human brain connectivity data. NeuroImage 124:1102-1107.
- Marcus, D., T. R. Olsen, M. Ramaratnam, and R. L. Buckner. 2007. The extensible neuroimaging archive toolkit (XNAT): An informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics 5:11-34.
- NITRC Home Page. Available at: https://www.nitrc.org/ (accessed November 12, 2019).
- Neu, S. C., K. L. Crawford, and A.W. Toga. 2012. Practical management of heterogeneous neuroimaging metadata by global neuroimaging data repositories. Front. Neuroinform. 6:8.
- Digital Imaging Communication in Medicine (DICOM).
1999. NEMA Standards Publication PS 3. Washington, DC: National Electrical Manufacturers Association.
- ANALYZE 7.5 file format. Available at: http://eeg. sourceforge.net/ANALYZE75.pdf (accessed November 12, 2019).
- NIFTI home page. Available at: http://nifti.nimh.nih.gov (accessed November 12, 2019).
- The Brain Imaging Data Structure (BIDS) specification. Available at: https://bids.neuroimaging.io/bids_spec.pdf (accessed November 12, 2019).
- Schlogl, A. 2009. An overview on data formats for biomedical signals. World Congress on Medical Physics andBiomedicalEngineering. Berlin-Heidelberg: Springer. 1557-1560.
- Kemp, B., A. Varri, A. C. Rosa, K. D. Nielsen, and J. Gade. 1992. A simple format for exchange of digitized polygraphic recordings. Electroen. Clin. Neuro. 82(5):391- 393.
- Schlogl, A. GDF - a general data format for biomedical signals Version 2.51. Available at: https://arxiv.org/ abs/cs/0608052 (accessed November 12, 2019).
- Smith, J., J. Johnson, J. Schubert, and R. Widell. 2005. A new file format for polysomnography data. Sleep 28(11):1473-1473.
- Fedorov, A., R. Beichel, J. Kalpathy-Cramer, et al. 2012.
3 D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9):1323- 1241.
- Sadigh-Eteghad, S., A. Majdi, M. Farhoudi, M. Talebi, and J. Mahmoudi. 2014. Different patterns of brain activation in normal aging and Alzheimer's disease from cognitional sight: Meta analysis using activation likelihood estimation. J. Neurol. Sci. 343(1-2):159-166.
- Whitfield-Gabrieli, S., and A. Nieto-Castanon. 2012. Conn: A functional connectivity toolbox for correlat
ed and anticorrelated brain networks. Brain Connectivity 2(3):125-141.
- Friston, K. J., J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W D. Penny. 2011. Statistical parametric mapping: The analysis of functional brain images. Academic Press. 688 p.
- Poil, S. 2013. Neurophysiological Biomarkers of cognitive decline: From criticality to toolbox. Amsterdam: VU Uni-versity. 218 p.
- Delorme, A., and S. Makeig. 2004. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics. J. Neurosci. Meth. 134:9-21.
- Oostenveld, R., P. Fries, E. Maris, and J. M. Schoffe- len. 2011. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosc. 2011:156869.
- Vidaurre, C., T. H. Sander, and A. Schlogl. 2011. BioSig: The free and open source software library for biomedical signal processing. Comput. Intell. Neurosc. 2011:935364.
- Brett, M., J. Taylor, C. Burns, et al. 2009. NIPY: An open library and development framework for FMRI data analysis. NeuroImage 47:S196.
- Gramfort, A., M. Luessi, and E. Larson. 2013. EEG data analysis with MNE-Python. Front. Neurosci. 7:267.
[+] About this article
Title
NEUROPHYSIOLOGY AS A SUBJECT DOMAIN FOR DATA INTENSIVE PROBLEM SOLVING
Journal
Informatics and Applications
2020, Volume 14, Issue 1, pp 40-47
Cover Date
2020-03-30
DOI
10.14357/19922264200106
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
neurophysiology; neurophysiological resources; neuroinformatics; data intensive research; analysis of neurophysiological data
Authors
D. O. Briukhov , S. A. Stupnikov , D. Yu. Kovalev , and I. A. Shanin
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
|