The Experience and Practice of Developing a Brain Functional Analysis System Using ICA on a Grid Environment

Authors:
Takeshi Kaishima;Yuko Mizuno-Matsumoto;Susumu Date;Shinji Shimojo
Affiliations:
-;-;-;-
Venue:
ASIAN '02 Proceedings of the7th Asian Computing Science Conference on Advances in Computing Science: Internet Computing and Modeling, Grid Computing, Peer-to-Peer Computing, and Cluster
Year:
2002

Citing 7
Cited 1

The grid: blueprint for a new computing infrastructure

The grid: blueprint for a new computing infrastructure
Independent component analysis for identification of artifacts in magnetoencephalographic recordings

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Independent component analysis: algorithms and applications

Neural Networks
A grid-enabled MPI: message passing in heterogeneous distributed computing systems

SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
The Globus Project: A Status Report

HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
A Grid Application for an Evaluation of Brain Function using Independent Component Analysis (ICA)

CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Toward Scalable Performance Visualization with Jumpshot

International Journal of High Performance Computing Applications

An adaptive grid implementation of DNA sequence alignment

Future Generation Computer Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

For the effective and early diagnosis of brain diseases, we have been developing a brain functional analysis system on a Grid environment. Until recently, neuroscientists have proposed and exploited numerous methods for the analysis of brain function. Although recently proposed analysis methods tend to be computationally-intensive, they are performed, in most cases, on a single-processor basis such as a commodity personal computer. The system developed on the Grid environment allows researchers and medical doctors to integrate a variety of geographically distributed resources and to analyze functional brain data in a realistic time period without detailed knowledge of the Grid. Through this paper, we show that the system built on the Grid has the capability to deliver enough computational power to perform promising brain functional analysis such as Independent Component Analysis (ICA). In addition, we present the experience and practice related to developing a Grid-enabled system for brain functional analysis mainly from the viewpoint of application building.