Davidsen, JörnOliver i Alabau, Isaura2020-02-042020-02-042020-02-03Oliver i Alabau, I. (2020). Variability in resting-state brain networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/111619Recently, 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.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.fMRIresting-state networksnetwork inferencenetwork topologyNeuroscienceStatisticsmaster thesis10.11575/PRISM/37556