Variability in resting-state brain networks
Date
2020-02-03
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Abstract
Recently, new studies have detected that group average brain networks display distinct organization compared with individual subject networks. In particular, each subject network presents a distinctive topology. How this variability affects the individual resting-state networks is a question we aim to solve. This is particularly important since specific resting-state networks, as the default mode network (DMN) and frontoparietal network (FPN), play an important role in early detection of neurophysiological diseases such as Alzheimer's, Parkinson, and attention-deficit hyperactivity disorder. In the analysis presented we will determine the robustness of the networks, first, and then quantify the variability in the connectivity structures. By using two distinct data sets, mapped with the same brain atlas, and three different similarity measures to infer resting-state networks, we show that the backbone of connectivity within the resting-state networks, DMN and FPN, does not vary significantly. While weaker connections do vary, they largely correspond to the links between the DMN and FPN. Overall, we find that the resting-state networks present a robust topology when a fixed atlas is used, and the recordings are sufficiently long.
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Keywords
fMRI, resting-state networks, network inference, network topology
Citation
Oliver i Alabau, I. (2020). Variability in resting-state brain networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.