Dynamic Bayesian network modeling of fMRI: A comparison of group-analysis methods

NeuroImage

Volumn 41, Issue 2, 2008, Pages 398-407

  Li, Junning ^a   Wang, Z Jane ^a   Palmer, Samantha J ^b   McKeown, Martin J ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Dynamic Bayesian network; Functional magnetic resonance imaging (fMRI); Group analysis; Parkinson's disease

Indexed keywords

LEVODOPA;

ADULT; AGED; ARTICLE; BAYES THEOREM; BRAIN REGION; CLINICAL ARTICLE; CONTROLLED STUDY; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; INTERMETHOD COMPARISON; MALE; PARKINSON DISEASE; PRIORITY JOURNAL; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; TASK PERFORMANCE;

AGED; ANTIPARKINSON AGENTS; BAYES THEOREM; BRAIN; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LEVODOPA; MAGNETIC RESONANCE IMAGING; MALE; MODELS, NEUROLOGICAL; PARKINSON DISEASE;

EID: 44249123488     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2008.01.068     Document Type: Article

References (37)

1. Brainard D.H. The psychophysics toolbox. Spat. Vis. 10 4 (1997) 433-436
2. Corbeil R.R., and Searle S.R. A comparison of variance component estimators. Biometrics 32 4 (1976) 779-791 (Dec.)
3. Edelman G.M., and Gally J.A. Degeneracy and complexity in biological systems. PNAS 98 24 (2001) 13763-13768
4. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2 1-2 (1994) 56-78
5. (Aug.). Friston K.J., Harrison L., and Penny W. Dynamic causal modelling. NeuroImage 19 4 (2003) 1273-1302 Aug.
6. (Dec.). Goncalves M.S., Hall D.A., Johnsrude I.S., and Haggard M.P. Can meaningful effective connectivities be obtained between auditory cortical regions?. NeuroImage 14 6 (2001) 1353-1360 Dec.
7. Harrison L., and Friston K. Human Brain Function. 2nd Edition (2004), Academic Press Ch. 48 and 50
8. Harrison L., Penny W.D., and Friston K. Multivariate autoregressive modeling of fMRI time series. NeuroImage 19 4 (2003) 1477-1491 (Aug.)
9. Hoehn M.M., and Yahr M.D. Parkinsonism: onset, progression and mortality. Neurology 17 5 (1967) 427-442 (May)
10. In: Jankovic J., and Tolosa E. (Eds). Parkinson's Disease and Movement Disorders. 4th Edition (2002), Williams & Wilkins
11. Kherif F., Poline J.-B., Meriaux S., Benali H., Flandin G., and Brett M. Group analysis in functional neuroimaging: selecting subjects using similarity measures. NeuroImage 20 4 (2003) 2197-2208 (Dec.)
12. Kim J., Zhu W., Chang L., Bentler P.M., and Ernst T. Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Hum. Brain Mapp. 28 2 (2007) 85-93
13. Lauritzen, S. L., 1996a. Graphical Models. Clarendon Press, Oxford University Press, Oxford, New York.
14. Lauritzen, S.L., 1996b. Graphical Models. Clarendon Press, Oxford University Press, Ch. 3.2.2, pp. 46-52.
15. Li J., Wang Z., and McKeown M. A multi-subject dynamic Bayesian network (DBN) framework for brain effective connectivity. Acoustics, Speech and Signal Processing, 2007. IEEE International Conference on. Vol. 1 (2007) I-429-I-432
16. Liao R., Krolik J.L., and McKeown M.J. An information-theoretic criterion for intrasubject alignment of fMRI time series: motion corrected independent component analysis. IEEE Trans. Med. Imag. 24 1 (2005) 29-44 (Jan)
17. Liao R., McKeown M.J., and Krolik J.L. Isolation and minimization of head motion-induced signal variations in fMRI data using independent component analysis. Magn. Reson. Med. 55 6 (2006) 1396-1413 (Jun.)
18. McIntosh A.R., and Gonzalez-Lima F. Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 2 (1994) 2-22
19. Mechelli A., Penny W.D., Price C.J., Gitelman D.R., and Friston K.J. Effective connectivity and intersubject variability: using a multisubject network to test differences and commonalities. NeuroImage 17 3 (2002) 1459-1469 (11)
20. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., and Teller E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21 6 (1953) 1087-1092
21. Murphy, K. P., 2002. Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley.
22. Niculescu-Mizil A., and Caruana R. Inductive transfer for Bayesian network structure learning. Proceedings of the 11th International Conference on AI and Statistics (AISTATS '07) (2007)
23. Pelli D.G. The videotoolbox software for visual psychophysics: transforming numbers into movies. Spat. Vis. 10 4 (1997) 437-442
24. Penny W., Ghahramani Z., and Friston K. Bilinear dynamical systems. Phil. Trans. R. Soc. B 360 1457 (2005) 983-993 (May)
25. Pope P.A., Praamstra P., and Wing A.M. Force and time control in the production of rhythmic movement sequences in Parkinson's disease. Eur. J. Neurosci. 23 6 (2006) 1643-1650 (Mar)
26. Price C.J., and Friston K.J. Degeneracy and cognitive anatomy. Trends Cogn. Sci. 6 10 (2002) 416-421 (Oct.)
27. Rajapakse J.C., and Zhou J. Learning effective brain connectivity with dynamic Bayesian networks. NeuroImage 37 (2007) 749-760
28. Rorden C., and Brett M. Stereotaxic display of brain lesions. Behav. Neurol. 12 4 (2000) 191-200
29. Schwarz G. Estimating the dimension of a model. Ann. Stat. 6 2 (1978) 461-464 Short Communications (Mar.)
30. Stelmach G.E., Teasdale N., Phillips J., and Worringham C.J. Force production characteristics in Parkinson's disease. Exp. Brain Res. 76 1 (1989) 165-172
31. Storey J.D. A direct approach to false discovery rates. J. R. Stat. Soc., Ser. B Stat. Methodol. 64 3 (2002) 479-498
32. Sugihara G., Kaminaga T., and Sugishita M. Interindividual uniformity and variety of the "writing center": a functional MRI study. NeuroImage 32 4 (2006) 1837-1849 (Oct.)
33. Talairach J., and Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain (1988), Thieme Medical Publishers
34. Turner R.S., Grafton S.T., McIntosh A.R., DeLong M.R., and Hoffman J.M. The functional anatomy of Parkinsonian bradykinesia. Neuroimage 19 1 (2003) 163-179 (May)
35. Vandenbroucke M.W.G., Goekoop R., Duschek E.J.J., Netelenbos J.C., Kuijer J.P.A., Barkhof F., Scheltens P., and Rombouts S.A.R.B. Interindividual differences of medial temporal lobe activation during encoding in an elderly population studied by fMRI. NeuroImage 21 1 (2004) 173-180 (Jan.)
36. Yang S., and Chang K.-C. Comparison of score metrics for Bayesian network learning. Systems, Man and Cybernetics: Part A. IEEE Transactions on 32 3 (2002) 419-428
37. Zheng X., and Rajapakse J.C. Learning functional structure from fMR images. NeuroImage 31 4 (2006) 1601-1613 (Jul.)