Graph Convolutional Neural Networks for Brain Connectivity Analysis

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/301141
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Type: Examensarbete för masterexamen
Title: Graph Convolutional Neural Networks for Brain Connectivity Analysis
Authors: Jansson, Lars
Sandsröm, Tobias
Abstract: We explore the potential and limitation of Graph Convolutional Neural Networks (GCNs) for classification of brain graphs derived from MRI measurements of subjects with Alzheimer’s disease (AD). GCNs differ from regular Artificial Neural Networks (ANNs) in that they operate directly on graph structures by defining convolutional operators in a non-Euclidean space. We show that GCNs perform well on graphstructured data, where regular ANNs typically fail due to the arbitrary ordering of nodes. Different GCN architectures are examined and compared to a Fully Connected Feedforward Neural Network. Tests are initially performed on simulated graphs mimicking the human brain. The simulated brain graphs were generated by following an algorithmic approach parameterized by graph measures known to be important for characterizing brain graphs. We demonstrate that GCNs are vital in accurately classifying the simulated brain graphs. The GCNs’ performance is evaluated on structured MRI-data, displaying cortical thicknesses for 68 regions in the brain of patients with Alzheimer’s disease and a healthy control group. On the structured brain data, both GCNs and regular ANNs are shown to be able classifiers. Finally, we show that the performance of regular ANNs is completely dependent on a fixed ordering imposed by the brain graph derivation from the MRI-data. In contrast, we show that GCNs perform well independent of node order.
Keywords: Machine Learning;Graph Neural Networks;Graph Convolutional Neural Networks;Supervised Learning;Brain Connectivity;Alzheimer’s Disease
Issue Date: 2020
Publisher: Chalmers tekniska högskola / Institutionen för fysik
URI: https://hdl.handle.net/20.500.12380/301141
Collection:Examensarbeten för masterexamen // Master Theses



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