Identifying Autism Spectrum Disorder in fMRI Brain Scans
| dc.contributor.author | Enns, Keanelek | |
| dc.contributor.supervisor | Thomo, Alex | |
| dc.contributor.supervisor | Srinivasan, Venkatesh | |
| dc.date.accessioned | 2023-04-11T20:46:29Z | |
| dc.date.available | 2023-04-11T20:46:29Z | |
| dc.date.copyright | 2023 | en_US |
| dc.date.issued | 2023-04-11 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Autism Spectrum Disorder (ASD) affects a large portion of the global population both directly and indirectly. The biological etiology of the disorder is not sufficiently understood, and current diagnoses rely on behavioural indicators which do not provide a reliable basis for diagnosis until about 2 years of age. Identifying a biological marker of ASD would aid in understanding the disorder and potentially allow for earlier, more objective diagnoses and treatments to improve the quality of life of individuals possessing ASD. The analysis of functional connectivity in the brain using functional Magnetic Resonance Imaging (fMRI) has been identified as a promising method for discovering such biological markers. This study recreated the work of Lanciano et al. in their paper “Explainable Classification of Brain Networks via Contrast Subgraphs”, but found inconsistent results with what was claimed. The methods were modified in various ways to improve accuracy and performance. A new, simpler method named Discriminative Edges (DE) was developed which achieved similar accuracies with improved performance and explainability. DE was also adapted to receive raw correlation matrices as well as thresholded correlation matrices representing brain networks, and it was found that raw correlation matrices provided more useful information for classification. A replication package was provided to aid future researchers in validating and improving upon these results. Suggestions for future work based on the findings of this study were provided, the most important being to procure more datasets, discover data- driven subcategories of ASD, and maintain replicability in studies. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/14937 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Neuroscience | en_US |
| dc.subject | Autism Spectrum Disorder | en_US |
| dc.subject | Explainability | en_US |
| dc.subject | Interpretability | en_US |
| dc.subject | Diagnosis | en_US |
| dc.subject | fMRI | en_US |
| dc.subject | Brain | en_US |
| dc.subject | Graph | en_US |
| dc.title | Identifying Autism Spectrum Disorder in fMRI Brain Scans | en_US |
| dc.type | Thesis | en_US |