| Title: | SurfGNN: A robust surface-based prediction model with interpretability for coactivation maps of spatial and cortical features |
| Journal: | Medical Image Analysis |
| Published: | 8 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40934807/ |
| DOI: | https://doi.org/10.1016/j.media.2025.103793 |
Publication 19540
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Abstract
Current brain surface-based prediction models often overlook the variability of regional attributes at the cortical feature level. While graph neural networks (GNNs) excel at capturing regional differences, they encounter challenges when dealing with complex, high-density graph structures. In this work, we consider the cortical surface mesh as a sparse graph and propose an interpretable prediction model-Surface Graph Neural Network (SurfGNN). SurfGNN employs topology-sampling learning (TSL) and region-specific learning (RSL) structures to manage individual cortical features at both lower and higher scales of the surface mesh, effectively tackling the challenges posed by the overly abundant mesh nodes and addressing the issue of heterogeneity in cortical regions. Building on this, a novel score-weighted fusion (SWF) method is implemented to merge nodal representations associated with each cortical feature for prediction. We apply our model to a neonatal brain age prediction task using a dataset of harmonized MR images from 481 subjects (503 scans). SurfGNN outperforms all existing state-of-the-art methods, demonstrating an improvement of at least 9.0% and achieving a mean absolute error (MAE) of 0.827 ± 0.056 in postmenstrual weeks. Furthermore, it generates feature-level activation maps, indicating its capability to identify robust regional variations in different morphometric contributions for prediction. The codes will be available at https://github.com/ZhuoshL/SurfGNN.</p>
10 Authors
- Zhuoshuo Li
- Jiong Zhang
- Youbing Zeng
- Jiaying Lin
- Dan Zhang
- Jianjia Zhang
- Duan Xu
- Hosung Kim
- Bingguang Liu
- Mengting Liu
Enabling scientific discoveries that improve human health