2026-09-18T22:51:23Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/344722026-06-10T16:42:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
William M. Wells, III and W. Eric L. Grimson.
Cosman, Eric Richard, 1977-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-03-26T20:36:35Z
2008-03-26T20:36:35Z
2005
2005
http://hdl.handle.net/1721.1/34472
70720509
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (p. 157-169).
A hierarchical model based on the Multivariate Autoregessive (MAR) process is proposed to jointly model functional neuroimaging time series collected from multiple subjects, and to characterize the distribution of MAR coefficients across the population from which those subjects were drawn. Thus, model-based inference about the interaction between brain regions, termed effective connectivity, may be generalized beyond those subjects studied. The posterior density of population- and subject-level connectivity parameters is estimated in a Variational Bayesian (VB) framework, and structural model parameters are chosen by the corresponding evidence criterion. The significance of resulting connectivity statistics are evaluated by permutation-based approximations to the null distribution. The method is demonstrated on simulated data and on actual multi-subject functional time series from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI).
by Eric Richard Cosman, Jr.
Ph.D.
169 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Bayesian population inference for effective connectivity
Thesis
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