Monte Carlo expectation maximization with hidden Markov models to detect functional networks in resting-state fMRI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7009 LNCS, Issue , 2011, Pages 59-66

  Liu, Wei ^a   Awate, Suyash P ^a   Anderson, Jeffrey S ^b   Yurgelun Todd, Deborah ^b   Fletcher, P Thomas ^a  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FRAMEWORKS; EXPECTATION MAXIMIZATION; FMRI DATA; FUNCTIONAL NETWORK; HIDDEN MARKOV RANDOM FIELDS; HIGH-DIMENSIONAL; LABELINGS; MODE APPROXIMATIONS; MONTE CARLO; MONTE CARLO INTEGRATION; NETWORK CLUSTER; SPATIAL COHERENCE; SYNTHETIC DATA; TIME-SERIES DATA; VON MISES-FISHER DISTRIBUTION;

ALGORITHMS; BAYESIAN NETWORKS; COMPUTER AIDED INSTRUCTION; IMAGE SEGMENTATION; LEARNING SYSTEMS; MAXIMUM PRINCIPLE; MEDICAL IMAGING; MONTE CARLO METHODS; SPHERES;

HIDDEN MARKOV MODELS;

EID: 80053979348     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24319-6_8     Document Type: Conference Paper

References (11)

1. Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.: Clustering on the unit hypersphere using von Mises-Fisher distributions. J. Machine Learning Res. 6(2), 1345 (2006)
2. Beckmann, C., Smith, S.: Tensorial extensions of independent component analysis for multisubject fMRI analysis. Neuroimage 25(1), 294-311 (2005)
3. Descombes, X., Kruggel, F., Cramon, D.V.: Spatio-temporal fMRI analysis using Markov random fields. IEEE Trans. on Medical Imaging 17(6), 1028-1039 (1998)
4. Fox, M., Greicius, M.: Clinical Applications of Resting State Functional Connectivity. Frontiers in Systems Neuroscience 4 (2010)
5. Golland, P., Lashkari, D., Venkataraman, A.: Spatial patterns and functional profiles for discovering structure in fMRI data. In: 42nd Asilomar Conference on Signals, Systems and Computers, pp. 1402-1409 (2008)
6. Li, S.Z.: Markov random field modeling in computer vision. Springer, Heidelberg (1995)
7. Liu, W., Zhu, P., Anderson, J., Yurgelun-Todd, D., Fletcher, P.: Spatial regularization of functional connectivity using high-dimensional markov random fields. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 363-370. Springer, Heidelberg (2010)
8. Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L.: A default mode of brain function. PNAS 98(2), 676-682 (2001)
9. Thirion, B., Dodel, S., Poline, J.: Detection of signal synchronizations in restingstate fMRI datasets. Neuroimage 29(1), 321-327 (2006)
10. Wei, G., Tanner, M.: A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association 85(411), 699-704 (1990)
11. Whitfield-Gabrieli, S.: Conn Matlab toolbox (March 2011), http://web.mit.edu/swg/software.htm