Improved Classification of Optic Neuritis Patients Using Brain Visual Network Transfer Functions

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dc.contributor.advisorSmith, Michael
dc.contributor.advisorGoodyear, Bradley
dc.contributor.authorShahrabi Farahani, Ehsan
dc.contributor.committeememberSesay, Abu
dc.contributor.committeememberFapojuwo, Abraham
dc.date2018-06
dc.date.accessioned2018-01-05T17:55:43Z
dc.date.available2018-01-05T17:55:43Z
dc.date.issued2017-12-22
dc.description.abstractOne method to investigate how information propagates throughout brain networks is the transfer function (TF), which determines the amplification or attenuation of frequency components of signals from one brain region to another. Previous functional magnetic resonance imaging (fMRI) studies have demonstrated a disrupted cortical visual network (CVN) in the presence of optic neuritis (ON), which is often associated with the development of multiple sclerosis (MS). In this thesis, new approaches were developed to optimize TF metrics for resting state fMRI data for the purpose of distinguishing between the CVNs of healthy volunteers and ON patients. TF metrics were validated using receiver operating characteristics. Further development permitted the ability to distinguish CVNs between patients experiencing ON as a clinically isolated syndrome and ON patients with relapsing-remitting multiple sclerosis. Such a distinction has implications for the understanding of MS development and progression. Artificial neural networks were also explored as a potential tool to combine several TF metrics to further increase accuracy.en_US
dc.identifier.citationShahrabi Farahani, E. (2017). Improved Classification of Optic Neuritis Patients Using Brain Visual Network Transfer Functions (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/5238
dc.identifier.urihttp://hdl.handle.net/1880/106240
dc.language.isoenen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity 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.subjectImage processingen_US
dc.subjectTransfer functionen_US
dc.subjectOptic Neuritisen_US
dc.subjectMultiple Sclerosisen_US
dc.subjectResting-state fMRIen_US
dc.subjectMachine Learningen_US
dc.subjectROC analysisen_US
dc.subject.classificationComputer Scienceen_US
dc.subject.classificationEngineering--Electronics and Electricalen_US
dc.titleImproved Classification of Optic Neuritis Patients Using Brain Visual Network Transfer Functionsen_US
dc.typemaster thesisen_US
thesis.degree.disciplineEngineering – Electrical and Computeren_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrue
ucalgary.thesis.checklistI confirm that I have submitted all of the required forms to Faculty of Graduate Studies. (See <a href="http://grad.ucalgary.ca/current/thesis/ethesis">http://grad.ucalgary.ca/current/thesis/ethesis</a> for more details)en_US
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