SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis

• URL: http://arxiv.org/abs/2410.07201v1
• Date: Tue, 24 Sep 2024 18:35:57 GMT
• Title: SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis
• Authors: Camila González, Yanis Miraoui, Yiran Fan, Ehsan Adeli, Kilian M. Pohl,
• Abstract summary: Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rsfMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision.
• Score: 8.489318619991534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision. Instead of extracting post-hoc feature attributions to uncover functional connections that are important to the target task, we identify a small subset of highly informative connections during training and occlude the rest. To this end, we jointly train a (1) sparse input mask, (2) variational autoencoder (VAE), and (3) downstream classifier in an end-to-end fashion. While we need a portion of labeled samples to train the classifier, we optimize the sparse mask and VAE with unlabeled data from additional acquisition sites, retaining only the input features that generalize well. We evaluate our method - Sparsely Reconstructed Graphs (SpaRG) - on the public ABIDE dataset for the task of sex classification, training with labeled cases from 18 sites and adapting the model to two additional out-of-distribution sites with a portion of unlabeled samples. For a relatively coarse parcellation (64 regions), SpaRG utilizes only 1% of the original connections while improving the classification accuracy across domains. Our code can be found at github.com/yanismiraoui/SpaRG.

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