Nonparametric Bayesian Inference of Dynamic Functional Connectivity

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Neuroimaging techniques reveal the human brain exhibiting a great repertoire of activity patterns—not only while perceiving external stimuli or acting in an experimental paradigm, but also at times of rest. In connectomic research, the time-average functional interactions of large-scale brain network components is commonly summarized in the measure of functional connectivity. Recently, there has been a surge of interest in considering functional connectivity at rest no longer as time-invariant, but as a characterization of brain networks displaying qualitatively differentiable dynamics over time. Previous research on dynamical functional connectivity provides a glimpse into the rich world of macroscopic brain dynamics, yet most of this work relies on heuristics or ad-hoc assumptions, which are not a priori given and often part of ongoing research themselves. To circumvent such issues, this thesis investigates functional connectivity in functional magnetic resonance imaging recordings with the aid of a hierarchical model in a Bayesian framework, leveraging recent developments in nonparametric modeling. The parameters of interest are inferred via an efficient Gibbs sampling scheme. The nonparametric model is evaluated and benchmarked against a sliding-window based K-means clustering on synthetic resting state data as well as on empirical motor-task and resting state recordings. The outcome of the analysis is that the proposed model generally infers more states of dynamic functional connectivity than do actually exist, deteriorating its otherwise superior predictive validity. Moreover, it is demonstrated that blind deconvolution of the functional magnetic resonance imaging data may be beneficial for the inference of dynamic functional connectivity. The findings reported in this thesis highlight the potential and challenges of nonparametric models in exploratory research of the human connectome.
Faculteit der Sociale Wetenschappen