Learning to Synthesize Cortical Morphological Changes using Graph Conditional Variational Autoencoder

Changes in brain morphology, such as cortical thinning are of great value for understanding the trajectory of brain aging and various neurodegenerative diseases. In this work, we employed a generative neural network variational autoencoder (VAE) that is conditional on age and is able to generate cor...

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Bibliographic Details
Main Authors: Chai, Yaqiong, Liu, Mengting, Duffy, Ben A., Kim, Hosung
Format: Conference Proceeding
Language:English
Subjects:
Aging
brain aging
Cortical thickness
deep neural network
graph
Morphology
Network topology
Predictive models
Surface morphology
Surface topography
Trajectory
variational autoencoders
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Summary:Changes in brain morphology, such as cortical thinning are of great value for understanding the trajectory of brain aging and various neurodegenerative diseases. In this work, we employed a generative neural network variational autoencoder (VAE) that is conditional on age and is able to generate cortical thickness maps at various ages given an input cortical thickness map. To take into account the mesh topology in the model, we proposed a loss function based on weighted adjacency to integrate the surface topography defined as edge connections with the cortical thickness mapped as vertices. Compared to traditional conditional VAE that did not use the surface topological information, our method better predicted "future" cortical thickness maps, especially when the age gap became wider. Our model has the potential to predict the distinctive temporo-spatial pattern of individual cortical morphology in relation to aging and neurodegenerative diseases.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI48211.2021.9433837