Bayesian population inference for effective connectivity

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).

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Cosman, Eric Richard, 1977-

Advisor dc:contributor.advisor

• William M. Wells, III and W. Eric L. Grimson.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/34472
• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Identifier URI

http://dspace.mit.edu/handle/1721.1/34472

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34472