Network analysis on neuro-imaging data using functional connectivity

• المؤلف الرئيسي: Chen, Hongpo
• مؤلفون آخرون: Ke Yiping, Kelly
• التنسيق: Final Year Project
• اللغة: English
• منشور في: Nanyang Technological University 2024
• الموضوعات: Computer and Information Science; fMRI; ASD; ML
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/181206

This study explores the use of network analysis on resting-state functional MRI (fMRI) data to identify distinct brain connectivity patterns in individuals with Autism Spectrum Disorder (ASD) compared to healthy control subjects, and to be able to distinguish them. The Brain Imaging Data Exchange (ABIDE) dataset was used, comprising of a similar number of ASD and control subjects. Within the fMRI scans, the brain regions were modeled as nodes, and their interactions as edges. A data preprocessing pipeline was used to extract time series data from the Regions of Interest (ROIs) of the brain defined by a parcellation atlas. Functional connectivity matrices were then derived from fMRI scans, whereby graph metrics such as degree centrality and clustering coefficient were computed and visualized. Multiple machine learning classifiers, such as a Support Vector Machine (SVM), a Graph Neural Network(GNN) and a Convolutional Neural Network(CNN) and more, were then used to distinguish between the two groups based on their brain connectivity patterns, and to classify subjects based on their diagnostic labels. The models achieved varying results and classification accuracy, with certain models performing better than the others, revealing differences in long-range connectivity between frontal and parietal regions in ASD subjects. These findings highlight the potential of graph-based methods in neuroimaging research and for understanding the neural mechanisms of ASD. Furthermore, this study provide a foundation for future work in early diagnosis and understanding of neurological disorders.