Brain networks in time : deriving and quantifying dynamic functional connectivity

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 simplifications. Connectivity that varies over time has been called dynamic functional connectivity. This thesis considers different ways to derive a dynamic functional connectivity representation of the brain and subsequently quantify this using temporal network theory.

Paper I: discusses different interpretations about what can be considered “interesting” or “high” dynamic functional connectivity. The choices made here can prioritize different 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: quantify the degree of burstiness, the distribution of temporal connections, between different 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 different 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 different brain networks from different tasks into a hierarchical network structure. This provides the start of a data driven hierarchical network atlas for different tasks. Paper VIII: is a conceptual overview of the different assumptions made in many popular methods to compute dynamic functional connectivity. Paper IX: aims to evaluate different dynamic functional connectivity methods based on several simulations designed to track a signal covariance that fluctuates over time.

List of scientific papers

I. Thompson, W. H., & Fransson, P. (2015). The mean–variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI. Frontiers in Human Neuroscience. 9(398), 1–7.
https://doi.org/10.3389/fnhum.2015.00398

II. Thompson, W. H., & Fransson, P. (2016). On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series. Brain Connectivity. 6(10), 735–746.
https://doi.org/10.1089/brain.2016.0454

III. Thompson, W. H., & Fransson, P. (2016). Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity. Scientific Reports. 6, 39156.
https://doi.org/10.1038/srep39156

IV. Thompson, W. H., Brantefors, P., & Fransson, P. (2017). From static to temporal network theory – applications to functional brain connectivity. Network Neruoscience. 1(2), 1–37.
https://doi.org/10.1162/NETN_a_00011

V. Thompson, W. H., & Fransson, P. (2015). The frequency dimension of fMRI dynamic connectivity: network connectivity, functional hubs and integration in the resting brain. NeuroImage. 121, 227–242.
https://doi.org/10.1016/j.neuroimage.2015.07.022

VI. Thompson, W. H., Thelin, E. P., Lilja, A., Bellander, B. M., & Fransson, P. (2015). Functional resting-state fMRI connectivity correlates with serum levels of the S100B protein in the acute phase of traumatic brain injury. NeuroImage: Clinical. 12, 1004–1012.
https://doi.org/10.1016/j.nicl.2016.05.005

VII. Thompson, W. H., & Fransson, P. (2017). Spatial conflence of psychological and anatomical network constructs in the human brain revealed by a mass meta-analysis of fMRI activation. Scientific Reports. 7, 44259.
https://doi.org/10.1038/srep44259

VIII. Thompson, W.H., & Fransson, P. A unified framework for dynamic functional connectivity. [Manuscript]

IX. Thompson, W.H., Richter, C., Plavén-Sigray, P. & Fransson, P. A comparison of different dynamic functional connectivity methods. [Manuscript]