# Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity

arXiv: https://arxiv.org/abs/1806.01764 · HTML: https://mlsanity.com/paper/1806.01764/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 21 of 21)

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)
- [DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI](https://mlsanity.com/paper/2307.09547/) (2023)
- [Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data](https://mlsanity.com/paper/2311.03520/) (2023)
- [Deep reinforcement learning guided graph neural networks for brain network analysis](https://mlsanity.com/paper/2203.10093/) (2022)
- [Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks](https://mlsanity.com/paper/2306.14375/) (2023)
- [Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model](https://mlsanity.com/paper/2207.07650/) (2022)
- [Multiplex Graph Networks for Multimodal Brain Network Analysis](https://mlsanity.com/paper/2108.00158/) (2021)
- [DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks](https://mlsanity.com/paper/2405.15805/) (2024)
- [Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures](https://mlsanity.com/paper/2112.04266/) (2021)
- [Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution](https://mlsanity.com/paper/2201.04318/) (2022)
- [Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer](https://mlsanity.com/paper/2206.01930/) (2022)
- [Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series](https://mlsanity.com/paper/2202.02393/) (2022)
- [A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data](https://mlsanity.com/paper/2312.01994/) (2023)
- [Functional2Structural: Cross-Modality Brain Networks Representation Learning](https://mlsanity.com/paper/2205.07854/) (2022)
- [Multi-Head Graph Convolutional Network for Structural Connectome Classification](https://mlsanity.com/paper/2305.02199/) (2023)
- [TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis](https://mlsanity.com/paper/2309.07947/) (2023)
- [AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field](https://mlsanity.com/paper/2406.17822/) (2024)
- [Atlas-free Brain Network Transformer](https://mlsanity.com/paper/2510.03306/) (2025)

## Abstract (arXiv)

Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs. Medical imaging and, in particular, neuroscience studies often rely on such graph representations, with brain connectivity networks being a characteristic example, while ultimately seeking the locus of phenotypic or disease-related differences in the brain. These regions of interest (ROIs) are, then, considered to be closely associated with function and/or behaviour. Driven by this, we explore GCNs for the task of ROI identification and propose a visual attribution method based on class activation mapping. By undertaking a sex classification task as proof of concept, we show that this method can be used to identify salient nodes (brain regions) without prior node labels. Based on experiments conducted on neuroimaging data of more than 5000 participants from UK Biobank, we demonstrate the robustness of the proposed method in highlighting reproducible regions across individuals. We further evaluate the neurobiological relevance of the identified regions based on evidence from large-scale UK Biobank studies.