Cortico-cortical and cortico-muscular connectivity analysis : methods and application to Parkinson's disease
| Autor: | Chiang, Joyce Hsien-yin |
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| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Druh dokumentu: | Text |
| Popis: | The concept of brain connectivity provides a new perspective to the understanding of the mechanism underlying brain functions, complementing the traditional approach of analyzing neural activity of isolated regions. Among the existing connectivity analysis techniques, multivariate autoregressive (mAR)-based measures are of great interest for their ability to characterize both directionality and spectral property of cortical interactions. Yet, the direct estimation of mAR-based connectivity from scalp electroencephalogram (EEG) is confounded by volume conduction, statistical instability and inter-subject variability. In this thesis, we propose novel signal processing methods to enhance the existing mAR-based connectivity methods. First, we explore incorporating sparsity constraints into the mAR formulation at both subject level and group level using LASSO-based regression. We show by simulation that sparse mAR yields more stable and accurate connectivity estimates compared to the traditional, non-sparse approach. Furthermore, the group-wise sparsity simplifies the inference of group-level connectivity patterns from multi-subject data. To mitigate the effect of volume conduction, we investigate source-level connectivity and propose a state-space generalized mAR framework to jointly model the mixing effect of volume conduction and causal relationships between underlying neural sources. By jointly estimating the mixing process and mAR model parameters, the proposed technique demonstrates improved connectivity estimation performance. Finally, we expanded our connectivity analysis to cortico-muscular level by modeling the relationships between EEG and simultaneously recorded electromyography (EMG) data using a multiblock partial least square (mbPLS) framework. The hierarchical construction of the mbPLS framework provides a natural way to model multi-subject, multi-modal data, enabling the identification of maximally covarying common patterns from EEG and EMG across subjects. Applications of the proposed techniques to EEG and EMG data of healthy and Parkinson's disease (PD) subjects demonstrate that directional connectivity analysis is a more sensitive technique than traditional univariate spectral analysis in revealing complex effects of motor tasks and disease. Moreover, alternations in connectivity accurately predict disease severity in PD. These new analysis tools allow a better understanding of brain function and provide a basis for developing objective measures to assess progression of neurological diseases. Applied Science, Faculty of Electrical and Computer Engineering, Department of Graduate |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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