Recent research has suggested disrupted interactions between brain regions may contribute
to some of the symptoms of motor disorders such as Parkinson’s Disease (PD). It is therefore
important to develop models for inferring brain functional connectivity from data obtained
through non-invasive imaging technologies, such as functional magnetic resonance imaging
(fMRI). The complexity of brain activities as well as the dynamic nature of motor disorders
require such models to be able to perform complex, large-scale, and dynamic system computation. Traditional models proposed in the literature such as structural equation modeling
(SEM), multivariate autoregressive models (MAR), dynamic causal modeling (DCM), and
dynamic Bayesian networks (DBNs) have all been suggested as suitable for fMRI data analysis. However, they suffer from their own disadvantages such as high computational cost (e.g.
DBNs), inability to deal with non-linear case (e.g. MAR), large sample size requirement
(e.g. SEM), et., al. In this research, we propose applying Probabilistic Boolean Network
(PBN) for modeling brain connectivity due to its solid stochastic properties, computational
simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for
fIVIRI data sets. Applying the proposed PBN framework to real fMRI data recorded from
PD subjects enables us to identify statistically significant abnormality in PD connectivity by
comparing it with normal subjects. The PBN results also suggest a mechanism of evaluating
the effectiveness of L-dopa, the principal treatment for PD. In addition to PBNs’ promising application in inferring brain connectivity, PBN modeling for brain ROTs also enables
researchers to study dynamic activities of the system under stochastic conditions, gaining
essential information regarding asymptotic behaviors of ROTs for potential therapeutic intervention in PD. The results indicate significant difference in feature states between PD
patients and normal subjects. Hypothesizing the observed feature states for normal subject
as the desired functional states, we further explore possible methods to manipulate the dynamic network behavior of PD patients in the favor of the desired states from the view of
random perturbation as well as intervention. Results identified a target ROT with the best
intervention performance, and that ROl is a potential candidate for therapeutic exercise. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/4172 |
| Date | 11 1900 |
| Creators | Ma, Zheng |
| Publisher | University of British Columbia |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Format | 1677549 bytes, application/pdf |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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