# Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma

arXiv: https://arxiv.org/abs/2508.09593 · HTML: https://mlsanity.com/paper/2508.09593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) Dataset](https://mlsanity.com/paper/2109.00356/) (2021)
- [Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome](https://mlsanity.com/paper/2211.07363/) (2022)
- [BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification](https://mlsanity.com/paper/2103.08494/) (2021)
- [Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks](https://mlsanity.com/paper/2109.01854/) (2021)
- [D-CoRP: Differentiable Connectivity Refinement for Functional Brain Networks](https://mlsanity.com/paper/2405.18658/) (2024)
- [Spatiotemporal Graph Neural Network Modelling Perfusion MRI](https://mlsanity.com/paper/2406.06434/) (2024)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration](https://mlsanity.com/paper/2603.20680/) (2026)

## Abstract (arXiv)

Isocitrate DeHydrogenase (IDH) mutation status is a crucial biomarker for glioma prognosis. However, current prediction methods are limited by the low availability and noise of functional MRI. Structural and morphological connectomes offer a non-invasive alternative, yet existing approaches often ignore the brain's hierarchical organisation and multiscale interactions. To address this, we propose Hi-SMGNN, a hierarchical framework that integrates structural and morphological connectomes from regional to modular levels. It features a multimodal interaction module with a Siamese network and cross-modal attention, a multiscale feature fusion mechanism for reducing redundancy, and a personalised modular partitioning strategy to enhance individual specificity and interpretability. Experiments on the UCSF-PDGM dataset demonstrate that Hi-SMGNN outperforms baseline and state-of-the-art models, showing improved robustness and effectiveness in IDH mutation prediction.