Modelling Effective Connectivity in Functional Magnetic Resonance Imaging Data by State Space Models
Ho, Moon-Ho Ringo
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/82046
Description
Title
Modelling Effective Connectivity in Functional Magnetic Resonance Imaging Data by State Space Models
Author(s)
Ho, Moon-Ho Ringo
Issue Date
2003
Doctoral Committee Chair(s)
Hubert, Lawrence J.
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Biology, Neuroscience
Language
eng
Abstract
This dissertation develops a new approach using state space models for effective connectivity analysis. The proposed approach integrates both activation and connectivity analysis into one single model. It takes the temporal information in the fMRI data into consideration explicitly and can be used to model the relationship among multiple region-of-interests in the brain. Dynamic characteristics of connectivity can also be investigated. The proposed approach is very flexible and can be used to test for commonalities and differences in the connectivity patterns across subjects. An empirical application for testing an attentional control network from a fMRI experiment is presented. The results provide partial support that higher order control (middle frontal gyrus, MFG) influences the two sites of control (lingual gyrus, LG, and middle occipital gyrus, MOG, in this experiment). There is also strong evidence indicating heterogeneity in the subjects' connectivity between LG and MFG. Moreover, the strength of the connectivity between LG and MFG, and between MOG and MFG show decreasing trend during the experiment.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
