TG-DGM: Clustering Brain Activity using a Temporal Graph Deep Generative ModelDownload PDF

06 Apr 2021, 11:58 (modified: 22 Jul 2022, 20:09)MIDL Short 2021 PosterReaders: Everyone
Keywords: Temporal Graph, Generative Model, Deep Learning, Functional Magnetic Resonance Imaging
Abstract: Spatiotemporal graphs are a natural representation of dynamic brain activity derived from functional magnetic imaging (fMRI) data. Previous works, however, tend to ignore time dynamics of the brain and focus on static graphs. In this paper, we propose a temporal graph deep generative model (TG-DGM) which clusters brain regions into communities that evolve over time. In particular, subject embeddings capture inter-subject variability and its impact on communities using neural networks. We validate our model on the UK Biobank data. Results of up to 0.81 AUC ROC on the task of biological sex classification demonstrate that injecting time dynamics in our model outperforms a static baseline.
Paper Type: both
Primary Subject Area: Unsupervised Learning and Representation Learning
Secondary Subject Area: Application: Other
Paper Status: original work, not submitted yet
Source Code Url: We are working on a full journal publication and would rather submit the code then.
Data Set Url: We are working on a full journal publication and would prefer to open source the data then.
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