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Dynamic Bayesian networks : modeling and analysis of neural signals

University of British Columbia

Studying interactions between different brain regions or neural components is crucial in understanding neurological disorders. Dynamic Bayesian networks, a type of statistical graphical model, have been suggested as a promising tool to model neural communication systems. This thesis investigates the employment of dynamic Bayesian networks for analyzing neural connectivity, especially with focus on three topics: structural feature extraction, group analysis, and error control in learning network structures. Extracting interpretable features from experimental data is important for clinical diagnosis and improving experiment design. A framework is designed for discovering structural differences, such as the pattern of sub-networks, between two groups of Bayesian networks. The framework consists of three components: Bayesian network modeling, statistical structure-comparison, and structure-based classification. In a study on stroke using surface electromyography, this method detected several coordination patterns among muscles that could effectively differentiate patients from healthy people. Group analyses are widely conducted in neurological research. However for dynamic Bayesian networks, the performances of different group-analysis methods had not been systematically investigated. To provide guidance on selecting group-analysis methods, three popular methods, i.e. the virtual-typical-subject, the common-structure and the individual-structure methods, were compared in a study on Parkinson's disease, from the aspects of their statistical goodness-of-fit to the data, and more importantly, their sensitivity in detecting the effect of medication. The three methods led to considerably different group-level results, and the individual-structure approach was more sensitive to the normalizing effect of medication. Controlling errors is a fundamental problem in applying dynamic Bayesian networks to discovering neural connectivity. An algorithm is developed for this purpose, particularly for controlling the false discovery rate (FDR). It is proved that the algorithm is able to curb the FDR under user-specified levels (for example, conventionally 5%) at the limit of large sample size, and meanwhile recover all the true connections with probability one. Several extensions are also developed, including a heuristic modification for moderate sample sizes, an adaption to prior knowledge, and a combination with Bayesian inference.

Thesis/Dissertation

application/pdf

2009-08-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/12618

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/