Informatics and Applications
2021, Volume 15, Issue 1, pp 78-85
AN ARCHITECTURE FOR DISTRIBUTED DATA ANALYSIS PROBLEM SOLVING IN NEUROPHYSIOLOGY
- D. O. Briukhov
- S. A. Stupnikov
- D. Yu. Kovalev
- I. A. Shanin
Abstract
The growth of volume and variety of data in the field of neurophysiology increases the need of the application of computer science methods such as statistical analysis, machine learning, and neural networks for the data analysis. Infrastructures providing storage of a large volume of data in neurophysiology as well as data distributed processing and analysis are required. This article proposes a software architecture for the problem solving based on the Hadoop distributed storage and analysis framework and GPU-assisted high-performance computing technologies.
[+] References (10)
- 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. Available at: https://www.nitrc.org/ (accessed January 14, 2021)
- Musatian, S., A. Lomakin, and A. Chizhova. 2019. Medical images research framework. CEUR Workshop Procee. 2372:60-66.
- Human brain project. Available at: https://www. humanbrainproject.eu (accessed January 14, 2021).
- Amunts, K., C. Ebell, J. Muller, M. Telefont, A. Knoll, and L. Lippert. 2016. The human brain project: Creating a European research infrastructure to decode the human brain. Neuron 92:574-581.
- Bryukhov, D. O., S. A. Stupnikov, D. Yu. Kovalev, and I. A. Shanin. 2020. Neyrofiziologiya kak predmetnaya oblast' dlya resheniya zadach s intensivnym is- pol'zovaniem dannykh [Neurophysiology as a subject do-main for data intensive problem solving]. Informatika i ee Primeneniya - Inform. Appl. 14(1):40-47.
- Kovalev, D., D. Sergeev, E. Tirikov, and N. Ponomareva. 2020. Methods and tools for analyzing human brain signals based on functional magnetic resonance imaging data. CEUR Workshop Procee. 2790:214-229.
- Desikan, R. S., F. Segonne, B. Fischl, etal. 2006. Anautomated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968-980.
- Allgaier, N., T. Banaschewski, G. Barker, et al. 2015. Nonlinear functional mapping of the human brain. 21 p. Available at: https://arxiv.org/abs/1510.03765 (accessed January 14, 2021).
- 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. doi: 10.1007/978-l-4614-7320-6_592-l.
[+] About this article
Title
AN ARCHITECTURE FOR DISTRIBUTED DATA ANALYSIS PROBLEM SOLVING IN NEUROPHYSIOLOGY
Journal
Informatics and Applications
2021, Volume 15, Issue 1, pp 78-85
Cover Date
2021-03-30
DOI
10.14357/19922264210111
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
neurophysiology; neurophysiological resources; neuroinformatics; data intensive research; problem solving infrastructure; 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
|