Structural Networks for Brain Age PredictionDownload PDF

10 Dec 2021, 12:51 (edited 22 Jun 2022)MIDL 2022Readers: Everyone
  • Keywords: Graph Neural Network, Graph Signal Processing, Structural MRI, Brain Age
  • Abstract: Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural Networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal Processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. The results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. These models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem.
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  • Paper Type: both
  • Primary Subject Area: Integration of Imaging and Clinical Data
  • Secondary Subject Area: Application: Other
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  • Code And Data: Code: https://github.com/imatge-upc/structnet_aging Data: https://www.ukbiobank.ac.uk/
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