Brain networks in time : deriving and quantifying dynamic functional connectivity

Abstract: Studying the brain’s structure and function as a network has provided insight about the brain’s activity in health and disease. Networks in the brain are often averaged over trials, frequency and time and this is called functional connectivity. This thesis aims to extend the analyses beyond these assumptions and simplifiations. Connectivity that varies over time has been called dynamic functional connectivity. This thesis considers diffrent ways to derive a dynamic functional connectivity representation of the brain and subsequently quantify this using temporal network theory. Paper I: discusses diffrent interpretations about what can be considered “interesting” or “high” dynamic functional connectivity. The choices made here can prioritize diffrent edges. Paper II: discusses how the stability of the variance of dynamic connectivity time series can be achieved. This is an important preprocessing step in dynamic functional connectivity as it can bias the subsequent analysis if done incorrectly. Paper III: quantifis the degree of burstiness, the distribution of temporal connections, between diffrent edges in fMRI data. Paper IV: provides an introduction and application of metrics from temporal network theory onto fMRI activity. Paper V: multi-layer network analysis of resting state networks over diffrent frequencies of the BOLD response. This work shows that a full analysis of the network structure of the brain in fMRI may require considering networks over frequency. Paper VI: Investigates whether the functional connectivity at time of trauma for patient with traumatic brain injury (TBI) correlates with features related to long term cognitive outcome. Paper VII: is a mass meta-analysis using Neurosynth to cluster diffrent brain networks from diffrent tasks into a hierarchical network structure. This provides the start of a data driven hierarchical network atlas for diffrent tasks. Paper VIII: is a conceptual overview of the diffrent assumptions made in many popular methods to compute dynamic functional connectivity. Paper IX: aims to evaluate diffrent dynamic functional connectivity methods based on several simulations designed to track a signal covariance that flctuates over time.