Bayesian population inference for effective connectivity
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://dspace.mit.edu/handle/1721.1/34472 http://hdl.handle.net/1721.1/34472 |
| _version_ | 1811091405237387264 |
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| author | Cosman, Eric Richard, 1977- |
| author2 | William M. Wells, III and W. Eric L. Grimson. |
| author_facet | William M. Wells, III and W. Eric L. Grimson. Cosman, Eric Richard, 1977- |
| author_sort | Cosman, Eric Richard, 1977- |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. |
| first_indexed | 2024-09-23T15:01:53Z |
| format | Thesis |
| id | mit-1721.1/34472 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:01:53Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/344722019-04-11T07:27:51Z Bayesian population inference for effective connectivity Cosman, Eric Richard, 1977- William M. Wells, III and W. Eric L. Grimson. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2008-03-26T20:36:35Z 2008-03-26T20:36:35Z 2005 2005 Thesis http://dspace.mit.edu/handle/1721.1/34472 http://hdl.handle.net/1721.1/34472 70720509 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/34472 http://dspace.mit.edu/handle/1721.1/7582 169 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Cosman, Eric Richard, 1977- Bayesian population inference for effective connectivity |
| title | Bayesian population inference for effective connectivity |
| title_full | Bayesian population inference for effective connectivity |
| title_fullStr | Bayesian population inference for effective connectivity |
| title_full_unstemmed | Bayesian population inference for effective connectivity |
| title_short | Bayesian population inference for effective connectivity |
| title_sort | bayesian population inference for effective connectivity |
| topic | Electrical Engineering and Computer Science. |
| url | http://dspace.mit.edu/handle/1721.1/34472 http://hdl.handle.net/1721.1/34472 |
| work_keys_str_mv | AT cosmanericrichard1977 bayesianpopulationinferenceforeffectiveconnectivity |