Graph Convolutional Neural Networks for Brain Connectivity Analysis
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2020
Författare
Jansson, Lars
Sandsröm, Tobias
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Machine Learning , Graph Neural Networks , Graph Convolutional Neural Networks , Supervised Learning , Brain Connectivity , Alzheimer’s Disease