Smith, MichaelGoodyear, BradleyShahrabi Farahani, Ehsan2018-01-052018-01-052017-12-22Shahrabi 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.http://hdl.handle.net/1880/106240One 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.enUniversity 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.Image processingTransfer functionOptic NeuritisMultiple SclerosisResting-state fMRIMachine LearningROC analysisComputer ScienceEngineering--Electronics and Electricalmaster thesis10.11575/PRISM/5238