Abstract
Complex human research, in particular, research in the field of brain pathologies requires strong informational support for consolidation of clinical and biological data from various sources to enable data processing and analysis. In this paper we present design and implementation of an information system for patient data collection, consolidation and analysis. We show and discuss results of applying cluster analysis methods for the automated processing of magnetic resonance voxel-based morphometry data to facilitate the early diagnosis of Alzheimer’s disease. Our results indicate that detailed investigation of the properties of cluster analysis data can significantly help neurophysiologists in the study of Alzheimer’s disease especially with the means of automated data handling provided by the developed information system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bogdanov, A., Degtyarev, A., Guschanskiy, D., Lysov, K., Ananieva, N., Zalutskaya, N., Neznanov, N.: Analog-digital approach in human brain modeling. In: Proceedings - 201717th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2017, pp. 807–812 (2017)
Bogdanov, A., Degtyarev, A., Guschanskiy, D., Lysov, K., Ananieva, N., Zalutskaya, N., Neznanov, N.: Hybrid approaches and human brain activity modelling. V.M. Bekhterev Rev. Psychiatry Med. Psychol. 1, 19–25 (2017)
Ananyeva, N.I., Bogdanov, A.V., Gushchanskiy, D.E., Degtyarev, A.B., Zalutskaya, N.M., Lysov, K.A., Neznanov, N.G., Iakushkin, O.O.: Analog and digital systems and high-performance solutions in problems of brain research and modeling. V.M. Bekhterev Rev. Psychiatry Med. Psychol. 3, 16–21 (2016)
Watson, P., Lord, P., Gibson, F., Periorellis, P., Pitsilis, G.: Cloud computing for e-Science with CARMEN. In: 2nd Iberian Grid Infrastructure Conference Proceedings, pp. 3–14, May 2008
D’Haese, P.F., Konrad, P.E., Pallavaram, S., Li, R., Prassad, P., Rodriguez, W., Dawant, B.M.: CranialCloud: a cloud-based architecture to support trans-institutional collaborative efforts in neurodegenerative disorders. Int. J. Comput. Assist. Radiol. Surg. 10(6), 815–823 (2015)
Wang, Y., Anderson, M.J., Cohen, J.D., Heinecke, A., Li, K., Satish, N., Sundaram, N., Turk-Browne, N.B., Willke, T.L.: Full correlation matrix analysis of fMRI data on Intel®Xeon™Phi coprocessors. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, p. 23. ACM (2015)
Bogdanov, A., Degtyarev, A., Korkhov, V.: Desktop supercomputer: what can it do? Phys. Part. Nucl. Lett. 14(7), 985–992 (2017)
Bogdanov, A., Degtyarev, A., Korkhov, V.: New approach to the simulation of complex systems. In: EPJ Web of Conferences, vol. 108, p. 01002. EDP Sciences (2016)
Jinzhou, Y., Jin, H., Kai, Z., Zhijun, W.: Discussion on private cloud PaaS construction of large scale enterprise. In: 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 273–278. IEEE (2016)
Korkhov, V., Gankevich, I., Degtyarev, A., Bogdanov, A., Gaiduchok, V., Ahmed, N., Cubahiro, A.: Experience in building virtual private supercomputer. In: Proceedings of International Conference on Computer Science and Information Technologies (CSIT), pp. 220–223 (2015). ISBN 978-5-8080-0797-0
Swanson, L.W., Lichtman, J.W.: From Cajal to Connectome and beyond. Annu. Rev. Neurosci. 39(1), 197–216 (2016)
Tomassy, G.S., Berger, D.R., Chen, H.H., Kasthuri, N., Hayworth, K.J., Vercelli, A., Seung, H.S., Lichtman, J.W., Arlotta, P.: Distinct profiles of Myelin distribution along single axons of pyramidal neurons in the neocortex. Science 344(6181), 319–324 (2014)
Lichtman, J.W., Denk, W.: The big and the small: challenges of imaging the brain’s circuits. Science 334(6056), 618–623 (2011)
Lichtman, J.W., Pfister, H., Shavit, N.: The big data challenges of connectomics. Nat. Neurosci. 17(11), 1448–1454 (2014)
Han, Y.: Cloud storage for digital preservation: optimal uses of Amazon S3 and Glacier. Library Hi Tech 33(2), 261–271 (2015)
Miller, J.A., Ding, S.L., Sunkin, S.M., Smith, K.A., Ng, L., Szafer, A., Ebbert, A., Riley, Z.L., Royall, J.J., Aiona, K., Arnold, J.M.: Transcriptional landscape of the prenatal human brain. Nature 508(7495), 199–206 (2014)
Mohlberg, H., Eickhoff, S.B., Schleicher, A., Zilles, K., Amunts, K.: A new processing pipeline and release of cytoarchitectonic probabilistic maps-JuBrain (2012)
Antoniu, G., Costan, A., Mota, B.D., Thirion, B., Tudoran, R.: A-brain: using the cloud to understand the impact of genetic variability on the brain. ERCIM News 89, 21–22 (2012)
Prieto, A., Prieto, B., Ortigosa, E.M., Ros, E., Pelayo, F., Ortega, J., Rojas, I.: Neural networks: an overview of early research, current frameworks and new challenges. Neurocomputing 214, 242–268 (2016)
Neven, H., Denchev, V.S., Rose, G., Macready, W.G.: QBoost: large scale classifier training with adiabatic quantum optimization. In: ACML, pp. 333–348 (2012)
Singh, H., Sachdev, A.: The quantum way of cloud computing. In: 2014 International Conference on Optimization, Reliabilty, and Information Technology (ICROIT), pp. 397–400. IEEE, February 2014
Iakushkin, O.O., Sedova, O.S.: Creating CAD designs and performing their subsequent analysis using opensource solutions in Python. In: AIP Conference Proceedings 1922, no. 140011 (2018). https://doi.org/10.1063/1.5019153
Iakushkin, O., Kondratiuk, A., Sedova, O., Grishkin, V.: Jupyter extension for creating CAD designs and their subsequent analysis by the finite element method. CEUR Workshop Proc. 1787, 530–534 (2016)
Cunningham, J.P.: Analyzing neural data at huge scale. Nat. Methods 11(9), 911–912 (2014)
Leon, P.S., Knock, S.A., Woodman, M.M., Domide, L., Mersmann, J., McIntosh, A.R., Jirsa, V.: The virtual brain: a simulator of primate brain network dynamics. In: Information-based methods for neuroimaging: analyzing structure, function and dynamics, p. 10 (2015)
Freesurfer. http://surfer.nmr.mgh.harvard.edu/. Accessed 15 Apr 2018
Angular. https://angular.io/
Express+Node.js. https://expressjs.com/
MongoDB. https://www.mongodb.com/
Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD International Conference on Management of Data, pp. 49–60. ACM Press (1999)
Petrov, D.A., Stankova, E.N.: Use of consolidation technology for meteorological data processing. In: Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2014. LNCS, vol. 8579, pp. 440–451. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09144-0_30
Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Shorov, A.V., Korkhov, V.V.: Using technologies of OLAP and machine learning for validation of the numerical models of convective clouds. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O., Stankova, E., Wang, S. (eds.) ICCSA 2016. LNCS, vol. 9788, pp. 463–472. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42111-7_36
Acknowledgments
Research has been partially supported by the RFBR grant 16-07-00886.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Korkhov, V. et al. (2018). Data Storage, Processing and Analysis System to Support Brain Research. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10963. Springer, Cham. https://doi.org/10.1007/978-3-319-95171-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-95171-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95170-6
Online ISBN: 978-3-319-95171-3
eBook Packages: Computer ScienceComputer Science (R0)