The week's pick
Reseach Article
Pattern Classification and Analysis of Brain Maps through fMRI data with Multiple Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 27 |
| Year of Publication: 2010 |
| Authors: H N Suma, Murali S |
10.5120/490-801
|
H N Suma, Murali S . Pattern Classification and Analysis of Brain Maps through fMRI data with Multiple Methods. International Journal of Computer Applications. 1, 27 ( February 2010), 103-111. DOI=10.5120/490-801
Abstract
The activity patterns in functional Magnetic Resonance Imaging (fMRI) data are unique and located in specific location in the brain. The main aim of analyzing these datasets is to localize the areas of the brain that have been activated by a predefined stimulus [1]. The basic analysis involves carrying out a statistical test for activation at thousands of locations in the brain. The analysis is based on fMRI brain activation maps generated using the Statistical Parametric Mapping (SPM) approach. The use of individually generated activation maps with SPM allows for better scalability to very large subject pools and it has the potential to integrate data at the activation map level that would be technically difficult to combine at the raw data level.
References
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- 4. H N Suma, S Murali “Principal component analysis for Analysis and Classification of fMRI Activation Maps”, International journal of computer science and network security, VOL 7, No 11, 235-242, Nov 2007.
- 5. Insung Jung, et, al, "Pattern Classification of Back-Propagation Algorithm Using Exclusive Connecting Network", International Journal of Computer Science and Engineering, Vol 2; pp 76-80,2008.
- 6. H N Suma, Murali S, “Neural network approach towards pattern classification using fMRI activity maps”, proceedings of IEEE indexed International Conference on computer communication Engineering-ICCCE08, pp 288-292, Kuala lampur, Malaysia, 2008.
- 7. Francisco Azuaje, "Genomic data sampling and its effect on classification performance assessment", BMC Bioinformatics, Vol 4:5, 2003.
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- 9. Saulius J. Garalevicius, "Memory–Prediction Framework for Pattern Recognition: Performance and Suitability of the Bayesian Model of Visual Cortex", American Association for Artificial Intelligence, 2007.
- 10. H N Suma, Murali S, “Naïve Bayesian classification approach towards classification of fMRI activity maps”, proceedings of International Conference on cognition and Recognition ICCR-08, pp 580-586, Mysore 2008.
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