Bayesian model reduction and empirical Bayes for group (DCM) studies

NeuroImage

Volumn 128, Issue , 2016, Pages 413-431

  Friston, Karl J ^a   Litvak, Vladimir ^a   Oswal, Ashwini ^a   Razi, Adeel ^a,b   Stephan, Klaas E ^a,c   Van Wijk, Bernadette C M ^a   Ziegler, Gabriel ^a   Zeidman, Peter ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b NED UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Pakistan)

^c UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

Bayesian model reduction; Classification; Dynamic causal modelling; Empirical Bayes; Fixed effects; Hierarchical modelling; Random effects

Indexed keywords

ACCURACY; ARTICLE; BAYES THEOREM; BAYESIAN MODEL REDUCTION; CLASSIFICATION; ELECTROENCEPHALOGRAM; HUMAN; MISMATCH NEGATIVITY; NONLINEAR SYSTEM; PARAMETRIC TEST; PREDICTION; PRIORITY JOURNAL; SCHIZOPHRENIA; SIMULATION; BIOLOGICAL MODEL;

BAYES THEOREM; HUMANS; MODELS, NEUROLOGICAL; SCHIZOPHRENIA;

EID: 84960801280     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.11.015     Document Type: Article

References (28)

1. Bernal-Casas D., Balaguer-Ballester E., Gerchen M.F., Iglesias S., Walter H., Heinz A., Meyer-Lindenberg A., Stephan K.E., Kirsch P. Dynamical causal modeling reveals modulation of prefrontal-hippocampal connectivity by a genomewide significant schizophrenia variant. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012 2012.
2. Brodersen K.H., Schofield T.M., Leff A.P., Ong C.S., Lomakina E.I., Buhmann M., Stephan K.E. Generative embedding for model-based classification of fMRI data". PLoS Comput. Biol. 2011, 7.
3. Efron B., Morris C. Stein's estimation rule and its competitors-an empirical Bayes approach. J. Am. Stat. Assoc. 1973, 68:117-130.
4. Fogelson N., Litvak V., Peled A., Fernandez-del-Olmo M., Friston K. The functional anatomy of schizophrenia: a DCM study of predictive coding. Schizophr. Res. 2014, 158(1-3):204-212.
5. Friston K. Hierarchical models in the brain. PLoS Comput. Biol. 2008, 4(11).
6. Friston K., Penny W. Post hoc Bayesian model selection. NeuroImage 2011, 56(4):2089-2099.
7. Friston K., Mattout J., Trujillo-Barreto N., Ashburner J., Penny W. Variational free energy and the Laplace approximation. NeuroImage 2007, 34(1):220-234.
8. Friston K.J., Li B., Daunizeau J., Stephan K. Network discovery with DCM. NeuroImage 2011, 56(3):1202-1221.
9. Garrido M.I., Kilner J.M., Kiebel S.J., Friston K.J. Evoked brain responses are generated by feedback loops. Proc. Natl. Acad. Sci. U. S. A. 2007, 104(52):20961-20966.
10. Garrido M.I., Kilner J.M., Kiebel S.J., Friston K.J. Dynamic causal modeling of the response to frequency deviants. J. Neurophysiol. 2009, 101(5):2620-2631.
11. Garrido M.I., Kilner J.M., Kiebel S.J., Stephan K.E., Baldeweg T., Friston K.J. Repetition suppression and plasticity in the human brain. NeuroImage 2009, 48(1):269-279.
12. Kass R.E., Steffey D. Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). J. Am. Stat. Assoc. 1989, 407:717-726.
13. Kiebel S.J., Garrido M.I., Friston K.J. Dynamic causal modelling of evoked responses: the role of intrinsic connections. NeuroImage 2007, 36(2):332-345.
14. Marreiros A.C., Kiebel S.J., Friston K.J. A dynamic causal model study of neuronal population dynamics. NeuroImage 2010, 51:91-101.
15. Ozaki T. A bridge between nonlinear time-series models and nonlinear stochastic dynamical systems: a local linearization approach. Stat. Sin. 1992, 2:113-135.
16. Penny W., Mattout J., Trujillo-Barreto N. Bayesian model selection and averaging. Statistical Parametric Mapping: The Analysis of Functional Brain Images 2006, Elsevier, London.
17. Penny W.D., Stephan K.E., Daunizeau J., Joao M., Friston K., Schofield T., Leff A.P. Comparing families of dynamic causal models. PLoS Comput. Biol. 2010, 6.
18. Raman S.S., Deserno L., Schlagenhauf F., Stephan K.E. A Hierarchical Model for Unifying Unsupervised Generative Embedding and Empirical Bayes 2015, (under review).
19. Rigoux L., Stephan K.E., Friston K.J., Daunizeau J. Bayesian model selection for group studies-revisited. NeuroImage 2014, 84:971-985.
20. Rosa M.J., Friston K., Penny W. Post-hoc selection of dynamic causal models. J. Neurosci. Methods 2012, 208(1):66-78.
21. Roweis S., Ghahramani Z. A unifying review of linear gaussian models. Neural Comput. 1999, 11(2):305-345.
22. Savage L.J. The Foundations of Statistics 1954, Wiley, New York.
23. Sengupta B., Friston K.J., Penny W.D. Gradient-based MCMC samplers for dynamic causal modelling. NeuroImage 2016, 125:1107-1118.
24. 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, 42:649-662.
25. Stephan K.E., Penny W.D., Daunizeau J., Moran R.J., Friston K.J. Bayesian model selection for group studies. NeuroImage 2009, 46(4):1004-1017.
26. Tipping M. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 2001, 1:211-244.
27. Trujillo-Barreto N.J., Aubert-Vázquez E., Valdés-Sosa P.A. Bayesian model averaging in EEG/MEG imaging. NeuroImage 2004, 21(4):1300-1319.
28. Woolrich M.W., Jbabdi S., Patenaude B., Chappell M., Makni S., Behrens T., Beckmann C., Jenkinson M., Smith S.M. Bayesian analysis of neuroimaging data in FSL. NeuroImage 2009, 45(1):173-186.