Using Brain Topological Features Extracted from Resting State fMRI to Classify Autism Spectrum Disorder

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dc.contributor.advisorSotero Diaz, Roberto C.
dc.contributor.authorKazeminejad, Amirali
dc.contributor.committeememberBray, Signe
dc.contributor.committeememberGreenberg, Matthew
dc.date2019-11
dc.date.accessioned2019-07-05T18:20:07Z
dc.date.available2019-07-05T18:20:07Z
dc.date.issued2019-07-03
dc.description.abstractAutism 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.en_US
dc.identifier.citationKazeminejad, 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.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/36711
dc.identifier.urihttp://hdl.handle.net/1880/110591
dc.language.isoengen_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.subjectAutism Spectrum Disorderen_US
dc.subjectfMRIen_US
dc.subjectResting Stateen_US
dc.subjectMachine Learningen_US
dc.subjectSVMen_US
dc.subjectNeural Networken_US
dc.subjectGraph Theoryen_US
dc.subject.classificationNeuroscienceen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationEngineering--Biomedicalen_US
dc.titleUsing Brain Topological Features Extracted from Resting State fMRI to Classify Autism Spectrum Disorderen_US
dc.typemaster thesisen_US
thesis.degree.disciplineEngineering – Biomedicalen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
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