Grid infrastructures for computational neuroscience: The neuGRID example

Redolfi, A., McClatchey, R., Anjum, A., Zijdenbos, A., Manset, D., Legré, Y., Wahlund, L.-O., Barattieri di San Pietro, C. and Frisoni, G. B. (2009) Grid infrastructures for computational neuroscience: The neuGRID example. Future Neurology, 4 (6). pp. 703-722. ISSN 1479-6708

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Publisher's URL: http://dx.doi.org/10.2217/fnl.09.53

Abstract

Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid- and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer’s and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer’s disease imaging community, which will allow the collection and archiving of large amounts of imaging data coupled with Grid-based algorithms and sufficiently powered computational resources. The major benefit will be the faster discovery of new disease markers that will be valuable for earlier diagnosis and development of innovative drugs. The initial setup of neuGRID will feature three nodes equipped with supercomputer capabilities and resources of more than 300 processor cores, 300 GB of RAM memory and approximately 20 TB of physical space. The scope of this article highlights the new perspectives and potential for the study of the neurodegenerative disorders using the emerging Grid technology.

Item Type:Article
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:11612
Deposited By: Professor R. Mcclatchey
Deposited On:21 Sep 2010 10:40
Last Modified:07 Sep 2013 07:19

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