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postgraduate thesis: A study of dynamic functional brain connectivity using functional magnetic resonance imaging (fMRI) : method and applications

TitleA study of dynamic functional brain connectivity using functional magnetic resonance imaging (fMRI) : method and applications
Authors
Issue Date2016
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Fu, Z. [傅泽宁]. (2016). A study of dynamic functional brain connectivity using functional magnetic resonance imaging (fMRI) : method and applications. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
AbstractIdentifying the statistical interdependence (functional connectivity, FC) between brain regions using functional magnetic resonance imaging (fMRI)is an important approach towards understanding how brain system is organized. Most fMRI studies assumed temporal stationarity of FC, so that the dynamic fluctuations of FC were overlooked. Emerging evidence has shown that FC fluctuates significantly across time and such fluctuations are physiologically relevant. The objectives of this work were (1) to develop novel methods for estimating dynamic FC from non-stationary fMRI signals, and (2) to apply new methods on real-life fMRI datasets for exploring dynamic patterns of FC in tasks and at rest. In particular, new methods were introduced to tackle two key issues in dynamic FC estimation: how to adaptively select window size to estimate dynamic FC and how to estimate dynamic FC networks with sparse architecture and sparse evolution. Firstly, a local polynomial regression (LPR) method was introduced to estimate time-varying covariance (TVCOV) for the inference of dynamic FC. The asymptotic analysis of this covariance estimator was performed and then a data-driven method, intersection of confidence intervals (ICI), was adopted to adaptively determine the window size. Simulation results showed that the LPR-ICI method could achieve robust and reliable performance in estimating TVCOV, making it a powerful tool for studying the dynamic FC from fMRI signals. Secondly, the LPR-ICI method was applied to a visual task fMRI dataset for studying the changes of FC in a block-designed visual checkerboard experiment. Reliable task-related FC changes were identified among activated visual regions during the task block. The results suggested that characterizing the task-related FC dynamics might provide greater insight into condition shifts and coordination between brain regions. Thirdly, the LPR-ICI method was applied to a resting-state fMRI dataset for exploring FC dynamics across the whole brain and investigating their relationships with dynamics of local brain activities. Converging results demonstrated that resting-state FC exhibited remarkable different dynamic patterns across the brain and these dynamic patterns were significantly correlated with the dynamic patterns of brain activities. These findings suggested that the brain might bean adaptive network, in which brain activities and their FC coevolve across time. Lastly, a novel dual l0-penalized (DLP) time-varying in verse covariance estimation method was introduced for estimating sparse dynamic FC networks. This DLP method was able to estimate dynamic networks with sparse architecture and sparse evolution by minimizing a log-likelihood function regularized by two l0-penalties (to enforce sparse architecture and sparse evolution, respectively).A coordinate descent algorithm was developed for searching the local minimizers of the objective function. Extensive simulation results showed that the DLP method could achieve better performance than conventionall1-penalized methods. In summary, two newly-developed methods (LPR-ICI and DLP) could be effective tools for studying dynamic brain FC and our results have advanced the knowledge of how brain regions dynamically coordinate. This study was also clinically relevant, as the quantification of altered FC dynamics in clinical populations of neuropsychiatric diseases might lead to a better understanding of the neuropathology and better diagnostic indicators.
DegreeDoctor of Philosophy
SubjectBrain - Magnetic resonance imaging
Dept/ProgramElectrical and Electronic Engineering
Persistent Identifierhttp://hdl.handle.net/10722/233921

 

DC FieldValueLanguage
dc.contributor.authorFu, Zening-
dc.contributor.author傅泽宁-
dc.date.accessioned2016-10-07T01:44:32Z-
dc.date.available2016-10-07T01:44:32Z-
dc.date.issued2016-
dc.identifier.citationFu, Z. [傅泽宁]. (2016). A study of dynamic functional brain connectivity using functional magnetic resonance imaging (fMRI) : method and applications. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/233921-
dc.description.abstractIdentifying the statistical interdependence (functional connectivity, FC) between brain regions using functional magnetic resonance imaging (fMRI)is an important approach towards understanding how brain system is organized. Most fMRI studies assumed temporal stationarity of FC, so that the dynamic fluctuations of FC were overlooked. Emerging evidence has shown that FC fluctuates significantly across time and such fluctuations are physiologically relevant. The objectives of this work were (1) to develop novel methods for estimating dynamic FC from non-stationary fMRI signals, and (2) to apply new methods on real-life fMRI datasets for exploring dynamic patterns of FC in tasks and at rest. In particular, new methods were introduced to tackle two key issues in dynamic FC estimation: how to adaptively select window size to estimate dynamic FC and how to estimate dynamic FC networks with sparse architecture and sparse evolution. Firstly, a local polynomial regression (LPR) method was introduced to estimate time-varying covariance (TVCOV) for the inference of dynamic FC. The asymptotic analysis of this covariance estimator was performed and then a data-driven method, intersection of confidence intervals (ICI), was adopted to adaptively determine the window size. Simulation results showed that the LPR-ICI method could achieve robust and reliable performance in estimating TVCOV, making it a powerful tool for studying the dynamic FC from fMRI signals. Secondly, the LPR-ICI method was applied to a visual task fMRI dataset for studying the changes of FC in a block-designed visual checkerboard experiment. Reliable task-related FC changes were identified among activated visual regions during the task block. The results suggested that characterizing the task-related FC dynamics might provide greater insight into condition shifts and coordination between brain regions. Thirdly, the LPR-ICI method was applied to a resting-state fMRI dataset for exploring FC dynamics across the whole brain and investigating their relationships with dynamics of local brain activities. Converging results demonstrated that resting-state FC exhibited remarkable different dynamic patterns across the brain and these dynamic patterns were significantly correlated with the dynamic patterns of brain activities. These findings suggested that the brain might bean adaptive network, in which brain activities and their FC coevolve across time. Lastly, a novel dual l0-penalized (DLP) time-varying in verse covariance estimation method was introduced for estimating sparse dynamic FC networks. This DLP method was able to estimate dynamic networks with sparse architecture and sparse evolution by minimizing a log-likelihood function regularized by two l0-penalties (to enforce sparse architecture and sparse evolution, respectively).A coordinate descent algorithm was developed for searching the local minimizers of the objective function. Extensive simulation results showed that the DLP method could achieve better performance than conventionall1-penalized methods. In summary, two newly-developed methods (LPR-ICI and DLP) could be effective tools for studying dynamic brain FC and our results have advanced the knowledge of how brain regions dynamically coordinate. This study was also clinically relevant, as the quantification of altered FC dynamics in clinical populations of neuropsychiatric diseases might lead to a better understanding of the neuropathology and better diagnostic indicators.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.subject.lcshBrain - Magnetic resonance imaging-
dc.titleA study of dynamic functional brain connectivity using functional magnetic resonance imaging (fMRI) : method and applications-
dc.typePG_Thesis-
dc.identifier.hkulb5793629-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineElectrical and Electronic Engineering-
dc.description.naturepublished_or_final_version-

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