Abstract:
BACKGROUND:There is a growing need for analyzing medical data such as brain connectomes. However, the unavailability of large-scale training samples increases risks of model over-fitting. Recently, deep learning (DL) architectures quickly gained momentum in synthesizing medical data. However, such frameworks are primarily designed for Euclidean data (eg., images), overlooking geometric data (eg., brain connectomes). A few existing geometric DL works that aimed to predict a target brain connectome from a source one primarily focused on domain alignment and were agnostic to preserving the connectome topology. NEW METHOD:To address the above limitations, firstly, we adapt the graph translation generative adversarial network (GT GAN) architecture to brain connectomic data. Secondly, we extend the baseline GT GAN to a cyclic graph translation (CGT) GAN, allowing bidirectional brain network translation between the source and target views. Finally, to preserve the topological strength of brain regions of interest (ROIs), we impose a topological strength constraint on the CGT GAN learning, thereby introducing CGTS GAN architecture. COMPARISON WITH EXISTING METHODS:We compared CGTS with graph translation methods and its ablated versions. RESULTS:Our deep graph network outperformed the baseline comparison method and its ablated versions in mean squared error (MSE) using multiview autism spectrum disorder connectomic dataset. CONCLUSION:We designed a topology-aware bidirectional brain connectome synthesis framework rooted in geometric deep learning, which can be used for data augmentation in clinical diagnosis.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Sserwadda A,Rekik Idoi
10.1016/j.jneumeth.2020.108988subject
Has Abstractpub_date
2020-11-04 00:00:00pages
108988eissn
0165-0270issn
1872-678Xpii
S0165-0270(20)30411-8pub_type
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