Variability in resting-state brain networks

Oliver i Alabau, Isaura

Variability in resting-state brain networks

dc.contributor.advisor	Davidsen, Jörn
dc.contributor.author	Oliver i Alabau, Isaura
dc.contributor.committeemember	Bray, Signe L.
dc.contributor.committeemember	Jackel, Brian J.
dc.contributor.committeemember	Wieser, Michael E.
dc.date	2020-06
dc.date.accessioned	2020-02-04T16:31:50Z
dc.date.available	2020-02-04T16:31:50Z
dc.date.issued	2020-02-03
dc.description.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.	en_US
dc.identifier.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.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/37556
dc.identifier.uri	http://hdl.handle.net/1880/111619
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University 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.	en_US
dc.subject	fMRI	en_US
dc.subject	resting-state networks	en_US
dc.subject	network inference	en_US
dc.subject	network topology	en_US
dc.subject.classification	Neuroscience	en_US
dc.subject.classification	Statistics	en_US
dc.title	Variability in resting-state brain networks	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Physics & Astronomy	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US