Effective connectivity of fMRI data using ancestral graph theory: Dealing with missing regions

NeuroImage

Volumn 54, Issue 4, 2011, Pages 2695-2705

  Waldorp, Lourens ^a   Christoffels, Ingrid ^b   van de Ven, Vincent ^c  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b LEIDEN UNIVERSITY   (Netherlands)

^c MAASTRICHT UNIVERSITY   (Netherlands)

Author keywords

Graphical model; Model selection; Network analysis; Random effects; Sandwich

Indexed keywords

ACCURACY; ARTICLE; BRAIN REGION; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MONTE CARLO METHOD; PRIORITY JOURNAL; SCORING SYSTEM;

BRAIN; BRAIN MAPPING; HUMANS; MAGNETIC RESONANCE IMAGING; MONTE CARLO METHOD; NEURAL PATHWAYS;

EID: 78650928732     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2010.10.054     Document Type: Article

References (51)

1. Akaike H. Information and an extension of the maximum likelihood principle. Proceedings of the second international symposium on information theory. Supplement to problems of control and information theory 1973, 267-281. Akademiai Kiado, Budapest. B. Petrov, F. Csáki (Eds.).
2. Amemiya T. The estimation of the variances in a variance components model. Int. Econ. Rev. 1971, 12(1):1-13.
3. Amemiya T. Advanced econometrics 1985, Basil Blackwell, Oxford.
4. Andersson S., Madigan D., Perlman M. A characterization of markov equivalence classes for acyclic digraphs. Ann. Stat. 1997, 25:505-541.
5. Beckmann C., Jenkinson M., Smith S. General multilevel linear modeling for group analysis in FMRI. Neuroimage 2003, 20:1052-1063.
6. Bilodeau M., Brenner D. Theory of multivariate statistics 1999, Springer-Verlag, New York.
7. Bozdogan H. Akaike's information criterion and recent developments in information complexity. J. Math. Psychol. 2000, 44:62-91.
8. Browne M. Generalized least squares estimators in the analysis of covariance structures. S. Afr. Stat. J. 1974, 8:1-24.
9. Buchel C., Friston K. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fmri. Cereb. Cortex 1997, 7:768-778.
10. Büchel C., Friston K. Assessing interactions among neuronal systems using functional neuroimaging. Neural Netw. 2000, 13:871-882.
11. Burnham K.P., Anderson D.R. Multimodel inference: understanding aic and bic in model selection. Sociol. Meth. Res. 2004, 33:261-304.
12. Casteren P., Gooijer J. Model selection by maximum entropy. Advances in econometrics: applying maximum entropy to econometric problems 1997, Vol.12:135-161. JAI Press, Greenwich, Connecticut. T. Fomby, R. Hill (Eds.).
13. Christoffels I., Formisano E., Schiller N. Neural correlates of verbal feedback processing: an fmri study employing overt speech. Hum. Brain Mapp. 2007, 28:868-879.
14. Claeskens G., Hjort N. Model selection and model averaging 2008, Cambridge University Press, Cambridge.
15. Cox D., Wermuth N. Multivariate dependencies: models, analysis and interpretation. Monographs on statistics and applied probability 1996, Chapman & Hall.
16. Demidenko E. Mixed models: theory and applications 2004, John Wiley and Sons.
17. Drton M., Perlman M. A sinful approach to gaussian graphical model selection. Tech. Rep. 2004, 457. August, University of Washington, Department of Statistics.
18. Drton M., Richardson T. Iterative conditional fitting for gaussian ancestral graph models. Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence 2004, 130-137.
19. Edwards Introduction to graphical modelling 2003, Springer-Verlag, New York.
20. Eichler M. A graphical approach for evaluating effective connectivity in neural systems. Philos. Trans. R. Soc. Lond. B 2005, 360:953-967.
21. Friston K. Functional integration. Statistical parametric mapping 2007, Ch.36:471-491. Academic Press, London. K. Friston, J. Ashburner, S. Kiebel, T. Nichols, W. Penny (Eds.).
22. Friston K., Harrison L., Penny W. Dynamic causal modelling. Neuroimage 2003, 19:1273-1302.
23. Goebel R., Esposito F., Formisano E. Analysis of fiac data with brainvoyager qx: from single-subject to cortically aligned group glm analysis and self-organizing group ica. Hum. Brain Mapp. 2006, 27(5):392-401.
24. Grunwald P. Model selection based on minimum description length. J. Math. Psychol. 2000, 44:133-152.
25. Lauritzen S. Graphical Models 1996, Oxford University Press.
26. Marrelec G., Krainik A., Duffau H., Pélégrini-Issac M., Lehéricy S., Doyon J., Benali H. Partial correlation for functional brain interactivity investigation in functional mri. Neuroimage 2006, 32(1):228-237. Aug.
27. Mclntosh A., Gonzalez-Lima F. Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 1997, 2:2-22.
28. Pearl J. Causality: models and prediction 2000, Cambridge University Press.
29. Penny W., Holmes A. Random effects analysis. Human Brain Function 2004, Academic Press. R.S. Frackowiak, J.T. Ashburner, W.D. Penny, S. Zeki, K.J. Friston, C.D. Frith, R.J. Dolan, C.J. Price (Eds.).
30. Rajapakse J., Zhou J. Learning effective brain connectivity with dynamic bayesian networks. Neuroimage 2007, 37(3):749-760.
31. Richardson T., Spirtes P. Ancestral graph markov models. Ann. Stat. 2002, 30(4):962-1030.
32. Robinson G.K. That blup is a good thing: the estimation of random effects. Stat. Sci. 1991, 6(1):15-32.
33. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using granger causality and fmri. Neuroimage 2005, 25:230-242.
34. Roebroeck A., Formisano E., Goebel R., 2009. The identification of interacting networks in the brain using fmri: model selection, causality and deconvolution. NeuroImage. doi:10.1016/j.neuroimage.2009.09.036.
35. Serfling R. Approximation theorems for mathematical statistics 1980, John Wiley and Sons.
36. Spirtes P., Glymour C., Sceines R. Causation, prediction, and search 1993, Springer-Verlag.
37. Stephan K.E., Kasper L., Harrison L.M., Daunizeau J., den Ouden H.E.M., Breakspear M., Friston K.J. Nonlinear dynamic causal models for fmri. Neuroimage 2008, 42(2):649-662.
38. Valdés-Sosa P.A., Sánchez-Bornot J.M., Lage-Castellanos A., Vega-Hernández M., Bosch-Bayard J., Melie-García L., Canales-Rodríguez E. Estimating brain functional connectivity with sparse multivariate autoregression. Philos. Trans. R. Soc. Lond. B 2005, 360:969-981.
39. Van der Vaart A. Asymptotic statistics 1998, Cambridge University Press, New York.
40. Verbeke G., Molenberghs G. Linear mixed models 2000, Springer.
41. Verma T., Pearl J. Equivalence and synthesis of causal models. Proceedings of the 6th Annual Conference on Uncertainty in Artificial Intelligence (UAI-91) 1991, Elsevier Science, New York, NY.
42. Waldorp L. Robust and unbiased variance of glm coefficients for misspecified autocorrelation and hemodynamic response models in fmri. Int. J. Biomed. Imaging 2009, 2009:723912.
43. Waldorp L., Huizenga H., Grasman R. The wald test and Cramer-Rao bound for misspecified models in electromagnetic source analysis. IEEE Trans. Signal Process. 2005, 53(9):3427-3435.
44. Waldorp L.J., Grasman R.P.P.P., Huizenga H.M. Goodness-of-fit and confidence intervals of approximate models. J. Math. Psychol. 2005, 50(2):203-213.
45. Weeda W., Waldorp L., Christoffels I., Huizenga H. Activated region fitting: a robust high power method for fmri analysis using parameterized regions of activation. Hum. Brain Mapp. 2009, 30:2595-2605.
46. White H. Maximum likelihood estimation of misspecified models. Econometrica 1982, 50(1):1-25.
47. Whittaker J. Grapphical models in applied multivariate statistics 1990, John Wiley and Sons.
48. Wink A., Roerdink J. BOLD noise assumptions in fMRI. Int. J. Biomed. Imaging 2006, 1-11.
49. Young G., Smith R. Essentials of statistical inference 2005, Cambridge University Press.
50. Yuan K.-H., Bentler P. Mean and covariance structure analysis: theoretical and practical improvements. J. Am. Stat. Assoc. 1997, 92:767-774.
51. Zhang J. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artif. Intell. 2008, 172:1873-1896.