Uncovering sparse brain effective connectivity: A voxel-based approach using penalized regression

Statistica Sinica

Volumn 18, Issue 4, 2008, Pages 1501-1518

  Sánchez Bornot, José M ^a   Martínez Montes, Eduardo ^a   Lage Castellanos, Agustín ^a   Vega Hernández, Mayrim ^a   Valdés Sosa, Pedro A ^a  

^a CUBAN NEUROSCIENCE CENTER   (Cuba)

Author keywords

Brain connectivity; FDR; Feature selection; fMRI

Indexed keywords

EID: 60149112407     PISSN: 10170405     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate - a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B 57, 289-300.
2. Buchel, C. and Friston, K. J. (1997). Modulation of connectivity in visual pathways by attention: Cortical interactions evaluated with structural equation modelling and fMRI. Cerebral Cortex 7, 768-778.
3. Bullmore, E., Harrison, L., Lee, L., Mechelli, A., and Friston, K. (2004). Brain Connectivity Workshop, Cambridge UK, May 2003. Neuroinformatics 2, 123-125.
4. Eichler, M. (2005). A graphical approach for evaluating effective connectivity in neural systems. Phil. Trans. Roy. Soc. B 360, 953-967.
5. Fan, J. Q. and Li, R. Z. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statis. Assoc. 96, 1348-1360.
6. Fan, J. Q. and Peng, H. (2004). Nonconcave penalized likelihood with a diverging number of parameters. Ann. Statist. 32, 928-961.
7. Friston, K. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping 2, 56-78.
8. Friston, K., Ashburner, J., Stefan, K., Thomas, E. and Penny, W. (2006). Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier, London.
9. Friston, K. J., Harrison, L. and Penny, W. (2003). Dynamic causal modelling. Neuroimage 19, 1273-1302.
10. Goldman, R. I., Stern, M., Engel, J., and Cohen, M. S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport 13, 2487-2492.
11. Harrison, L., Penny, W. D., and Friston, K. (2003). Multivariate autoregressive modeling of fmri time series. Neuroimage 19, 1477-1491.
12. Heeger, D. J. and Ress, D. (2002). What does fMRI tell us about neuronal activity? Nature Reviews Neuroscience 3, 142-151.
13. Hoerl, A. E. and Kennard, R. W. (2000). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 42, 80-86.
14. Hunter, D. R. and Li, R. (2005). Variable selection using MM algorithms. Ann. Statist. 33, 1617-1642.
15. Iturria-Medina, Y., Canales-Rodriguez, E. J., Melie-Garcia, L., Valdes-Hernandez, P. A., Martinez-Montes, E., Aleman-Gomez, Y., and Sanchez-Bornot, J. M. (2007). Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. Neuroimage 36, 645-660.
16. Jirsa, V. K. and McIntosh, A. R. (2007). Handbook of Brain Connectivity. Springer, Berlin.
17. Kotter, R. (2001). Neuroscience databases: tools for exploring brain structure-function relationships. Phil. Trans. Roy. Soc. B 356, 1111-1120.
18. Le Bihan, D. (2003). Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews Neuroscience 4, 469-480.
19. Lee, L., Harrison, L. M. and Mechelli, A. (2003). A report of the functional connectivity workshop, Dusseldorf 2002. Neuroimage 19, 457-465.
20. Li, R. Z., Dziak, J., Ma, H. Y. (2006). Nonconvex penalized least squares: characterizations, algorithm and application. Manuscript.
21. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., and Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150-157.
22. Martínez-Montes, E., Valdés-Sosa, P. A., Miwakeichi, F., Goldman, R. I., and Cohen, M. S. (2004). Concurrent EEG/fMRI Analysis by Multi-way Partial Least Squares. Neuroimage 22, 1023-1034.
23. Ogawa, S. and Sung, Y. W. (2007). Functional Magnetic Resonance Imaging. Scholarpedia.
24. Penny, W. and Friston, K. (2007). Functional Imaging. Scholarpedia.
25. Perkins, S., Lacker, K., and Theiler, J. (2003). Grafting: Fast, incremental feature selection by gradient descent in function space. J. Mach. Learn Res. 3, 1333-1356.
26. Ramsay, J. O. and Silverman, B. W. (1997). Functional Data Analysis. Springer, New York.
27. Salvador, R., Suckling, J., Schwarzbauer, C, and Bullmore, E. (2005). Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Phil. Trans. Roy. Soc. B 360, 937-946.
28. Sporns, O., Tononi, G. and Kotter, R. (2005). The human connectome: A structural description of the human brain. Plos Computational Biology 1, 245-251.
29. Stephan, K. E., Kamper, L., Bozkurt, A., Burns, G., Young, M. P., and Kotter, R. (2001). Advanced database methodology for the collation of connectivity data on the Macaque brain (CoCoMac). Phil. Trans. Roy. Soc. B 356, 1159-1186.
30. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. J. Roy. Statist. Soc. Ser. B 58, 267-288.
31. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005). Sparsity and smoothness via the fused lasso. J. Roy. Statist. Soc. B 67, 91-108.
32. Valdés-Sosa, P. A. (2004). Spatio-temporal autoregressive models defined over brain manifolds. Neuroinformatics 2, 239-250.
33. Valdés-Sosa, P. A., Sánchez-Bornot, J. M., Lage-Castellanos, A., Vega-Hernández, M., Bosch- Bayard, J., Melie-García, L. and Canales-Rodríguez, E. (2005). Estimating brain functional connectivity with sparse multivariate autoregression. Phil. Trans. Roy. Soc. B 360, 969-981.
34. Valdés-Sosa, P. A., Sánchez-Bornot, J. M., Vega-Hernández, M., Melie-García, L., Lage- Castellanos, A., and Canales-Rodríguez, E. (2006). Granger Causality on Spatial Manifolds: Applications to Neuroimaging. Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, Chapter 18. ISBN: 3-527-40623-9.
35. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. J. Roy. Statist. Soc. Ser. B 67, 301-320.