Benchmarking Graph Neural Networks for FMRI analysis
- URL: http://arxiv.org/abs/2211.08927v1
- Date: Wed, 16 Nov 2022 14:16:54 GMT
- Title: Benchmarking Graph Neural Networks for FMRI analysis
- Authors: Ahmed ElGazzar, Rajat Thomas, Guido van Wingen
- Abstract summary: Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data.
We study and evaluate the performance of five popular GNN architectures in diagnosing major depression disorder and autism spectrum disorder.
We highlight that creating optimal graph structures for functional brain data is a major bottleneck hindering the performance of GNNs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from
graph-structured data. A paramount example of such data is the brain, which
operates as a network, from the micro-scale of neurons, to the macro-scale of
regions. This organization deemed GNNs a natural tool of choice to model brain
activity, and have consequently attracted a lot of attention in the
neuroimaging community. Yet, the advantage of adopting these models over
conventional methods has not yet been assessed in a systematic way to gauge if
GNNs are capable of leveraging the underlying structure of the data to improve
learning. In this work, we study and evaluate the performance of five popular
GNN architectures in diagnosing major depression disorder and autism spectrum
disorder in two multi-site clinical datasets, and sex classification on the
UKBioBank, from functional brain scans under a general uniform framework. Our
results show that GNNs fail to outperform kernel-based and structure-agnostic
deep learning models, in which 1D CNNs outperform the other methods in all
scenarios. We highlight that creating optimal graph structures for functional
brain data is a major bottleneck hindering the performance of GNNs, where
existing works use arbitrary measures to define the edges resulting in noisy
graphs. We therefore propose to integrate graph diffusion into existing
architectures and show that it can alleviate this problem and improve their
performance. Our results call for increased moderation and rigorous validation
when evaluating graph methods and advocate for more data-centeric approaches in
developing GNNs for functional neuroimaging applications.
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