Time-resolved Resting-state fMRI of Brain States Associated with Drug-resistant Epilepsy

Rashnavadi, Tahereh

Time-resolved Resting-state fMRI of Brain States Associated with Drug-resistant Epilepsy

dc.contributor.advisor	Goodyear, Bradley
dc.contributor.advisor	Federico, Paolo
dc.contributor.author	Rashnavadi, Tahereh
dc.contributor.committeemember	Levan, Pierre
dc.contributor.committeemember	Klein, Karl Martin
dc.date	2025-07
dc.date.accessioned	2024-12-03T17:48:42Z
dc.date.available	2024-12-03T17:48:42Z
dc.date.issued	2024-12-02
dc.description.abstract	This thesis presents a novel analytical framework for time-resolved analysis of resting-state functional magnetic resonance imaging (fMRI) data with the aim of elucidating brain patterns of aberrant activity in individuals with drug-resistant epilepsy. Specifically, a dynamic functional connectivity analysis method is first used to determine the temporal dynamics of brain activity, followed by two unsupervised machine learning algorithms, k-means and hierarchical clustering, to identify patterns of activity that define temporarily stable brain states. The limitations of these algorithms are addressed with supportive techniques, leading to an integrated framework that combines dynamic connectivity analysis with machine learning based clustering. The approach is first validated using simulated resting-state fMRI data, comparing two dynamic connectivity analysis methods: sliding-window cross-correlation (SWC) and a newly developed technique. Performance is assessed in terms of successfully detecting state transitions and their timings. The framework is then applied to fMRI data obtained from a small group of individuals with frontal lobe epilepsy (FLE) patients as well as control participants, focusing on the somatomotor and default mode networks (DMN); consistent results are obtained across clustering methods. Finally, the analysis is applied to a larger group of individuals with temporal lobe epilepsy (TLE) who underwent simultaneous resting-state fMRI and intracranial EEG (iEEG), with a focus on the DMN and its interaction with five other major networks. The relationship between the occurrences of iEEG-recorded interictal epileptiform discharges (IEDs) and occupied brain state is examined to better understand how IED activity impacts brain network activity. The findings of this thesis demonstrate the analytical framework’s potential to enhance the detection and characterization of brain state transitions associated with epilepsy, offering a valuable tool for better identification of seizure foci and improving our understanding of the brain network dynamics associated with drug-resistant epilepsy.
dc.identifier.citation	Rashnavadi, T. (2024). Time-resolved resting-state fMRI of brain states associated with drug-resistant epilepsy (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/120153
dc.language.iso	en
dc.publisher.faculty	Schulich School of Engineering
dc.publisher.institution	University of Calgary
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.
dc.subject	Functional MRI
dc.subject	Brain Networks
dc.subject	Functional Connectivity
dc.subject	Clustering
dc.subject	Epilepsy
dc.subject.classification	Engineering--Biomedical
dc.subject.classification	Computer Science
dc.title	Time-resolved Resting-state fMRI of Brain States Associated with Drug-resistant Epilepsy
dc.type	doctoral thesis
thesis.degree.discipline	Engineering – Biomedical
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.