Brain connectivity analysis by reduction to pair classification

2010 2nd International Workshop on Cognitive Information Processing, CIP2010

Volumn , Issue , 2010, Pages 275-280

  Olivetti, Emanuele ^a,b   Veeramachaneni, Sriharsha ^c   Greiner, Susanne ^a,b   Avesani, Paolo ^a,b  

^a FONDAZIONE BRUNO KESSLER   (Italy)

^b UNIVERSITY OF TRENTO   (Italy)

^c THOMSON REUTERS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN AREAS; BRAIN CONNECTIVITY; FIBER TRACT SEGMENTATION; FUNCTIONAL MRI;

DATA PROCESSING;

EID: 78349285599     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIP.2010.5604101     Document Type: Conference Paper

References (22)

1. E. Rykhlevskaia, G. Gratton, and M. Fabiani, "Combining structural and functional neuroimaging data for studying brain connectivity: A review," Psychophysiology, vol. 45, no. 2, pp. 173-187, March 2008. [Online]. Available: http://dx.doi.org/10.1111/j.1469-8986.2007.00621.x
2. P. J. Basser, J. Mattiello, and D. Lebihan, "Mr diffusion tensor spectroscopy and imaging." Biophysical journal, vol. 66, no. 1, pp. 259-267, January 1994. [Online]. Available: http://dx.doi.org/10.1016/S0006- 3495(94)80775-1
3. D. S. Tuch, T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau, and V. J. Wedeen, "High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity," Magnetic Resonance in Medicine, vol. 48, no. 4, pp. 577-582, October 2002. [Online]. Available: http://dx.doi.org/10. 1002/mrm.10268
4. S. Mori and C. M. van Zijl, "Fiber tracking: principles and strategies - a technical review," NMR in Biomedicine, vol. 15, no. 7-8, pp. 468-480, 2002.
5. S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, "Brain magnetic resonance imaging with contrast dependent on blood oxygenation," Proceedings of the National Academy of Sciences of the United States of America, vol. 87, no. 24, pp. 9868-9872, December 1990. [Online]. Available: http://dx.doi.org/10.1073/pnas.87.24.9868
6. S. Zhang, S. Correia, and D. H. Laidlaw, "Identifying white-matter fiber bundles in dti data using an automated proximity-based fiber-clustering method," Visualization and Computer Graphics, IEEE Transactions on, vol. 14, no. 5, pp. 1044-1053, 2008. [Online]. Available: http://dx.doi.org/10.1109/ TVCG.2008.52
7. M. Maddah, W. E. Grimson, S. K. Warfield, and W. M. Wells, "A unified framework for clustering and quantitative analysis of white matter fiber tracts." Medical image analysis, vol. 12, no. 2, pp. 191-202, April 2008. [Online]. Available: http://dx.doi.org/10.1016/j.media.2007.10.003
8. M. Wiegell, "Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging," NeuroImage, vol. 19, no. 2, pp. 391-401, June 2003. [Online]. Available: http://dx.doi.org/10.1016/S1053- 8119(03)00044-2
9. E. Davoodi-Bojd and H. Soltanian-Zadeh, "Atlas based segmentation of white matter fiber bundles in dtmri using fractional anisotropy and principal eigen vectors," in Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, 2008, pp. 879-882. [Online]. Available: http://dx.doi.org/10.1109/ISBI.2008.4541137
10. H. Daume and D. Marcu, "A bayesian model for supervised clustering with the dirichlet process prior," J. Mach. Learn. Res., vol. 6, pp. 1551-1577, 2005. [Online]. Available: http://portal.acm.org/citation.cfm?id= 1194897
11. W. W. Cohen and J. Richman, "Learning to match and cluster large high-dimensional data sets for data integration," in KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM, 2002, pp. 475-480. [Online]. Available: http://dx.doi.org/10.1145/775047.775116
12. A. Monge and C. Elkan, "An efficient domain-independent algorithm for detecting approximately duplicate database records," in SIGMOD workshop on data mining and knowledge discovery, May 1997. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.8405
13. A. Mccallum, K. Nigam, and L. H. Ungar, "Efficient clustering of high-dimensional data sets with application to reference matching," in Knowledge Discovery and Data Mining, 2000, pp. 169-178. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.6569
14. A. Mccallum and B. Wellner, "Conditional models of identity uncertainty with application to noun coreference," in NIPS 2004, 2004. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13. 8466
15. A. Bar-hillel and D. Weinshall, "Learning with equivalence constraints, and the relation to multiclass learning," in In Proceedings of the Conference on Computational Learning Theory (COLT, 2003. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.7574
16. T. Kamishima and F. Motoyoshi, "Learning from cluster examples," Machine Learning, vol. 53, no. 3, pp. 199-233, December 2003. [Online]. Available: http://dx.doi.org/10.1023/A:1026351106797
17. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, illustrated edition ed. Cambridge University Press, June 2004. [Online]. Available: http://www.worldcat.org/isbn/0521813972
18. E. Pekalska and R. P. W. Duin, The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence). World Scientific Publishing Company, December 2005. [Online]. Available: http://www.worldcat.org/isbn/9812565302
19. B. E. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal margin classifiers," in Computational Learing Theory, 1992, pp. 144-152. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi= 10.1.1.21.3818
20. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, pp. 273-297, September 1995. [Online]. Available: http://www. ingentaconnect.com/content/klu/mach/1995/00000020/00000003/0042240
21. S. Varma and R. Simon, "Bias in error estimation when using cross-validation for model selection," BMC Bioinformatics, vol. 7, no. 1, pp. 91+, February 2006. [Online]. Available: http://dx.doi.org/10.1186/1471- 2105-7-91
22. M. Hanke, Y. O. Halchenko, P. B. Sederberg, E. Olivetti, I. Fründ, J. Rieger, C. S. Herrmann, J. V. Haxby, S. J. Hanson, and S. Pollmann, "Pymvpa: a unifying approach to the analysis of neuroscientific data," Fontiers in Neuroinformatics, vol. 3, no. 3, 2009. [Online]. Available: http://dx.doi.org/10.3389/neuro.11.003.2009