Sotero Diaz, Roberto C.Kazeminejad, Amirali2019-07-052019-07-052019-07-03Kazeminejad, A. (2019). Using Brain Topological Features Extracted from Resting State fMRI to Classify Autism Spectrum Disorder (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/110591Autism Spectrum Disorder is a neurodevelopmental disease manifesting in early childhood and hindering the social and behavioral outlooks of individuals suffering from it. Early identification of this disorder leads to better patient outcome. Many imaging studies have been conducted in order to gather insight into the inner workings of this disorder with some using machine learning in autism diagnosis. The success of this approach is heavily dependent on the features that are used for the classification task. Graph theoretical measures, extracted from resting state functional MRI, have already proven useful in classifying other neurological disorders. I hypothesized that by using these features for Autism Spectrum Disorder classification, the model performance (accuracy) will improve over previously reported imaging-based methods. Furthermore, this allowed me to identify possible biomarkers for the disorder based on the importance of features selected. This thesis shows that graph theoretical features may help improve classification accuracies and extracting biomarkers relevant to ASD.engUniversity 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.Autism Spectrum DisorderfMRIResting StateMachine LearningSVMNeural NetworkGraph TheoryNeuroscienceArtificial IntelligenceEngineering--Biomedicalmaster thesis10.11575/PRISM/36711