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

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.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Engineering – Biomedical

Grantor dc:publisher.institution

Schulich School of Engineering

Year dc:date.issued

2024

Author dc:creator

• Rashnavadi, Tahereh

Advisors dc:contributor.advisor

• Goodyear, Bradley
• Federico, Paolo

Committee members dc:contributor.committeemember

• Levan, Pierre
• Klein, Karl Martin

• Functional MRI
• Brain Networks
• Functional Connectivity
• Clustering
• Epilepsy

Statement 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.

Language dc:language.iso

en

DOI

https://dx.doi.org/10.11575/PRISM/47764

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/120153