# Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis

arXiv: https://arxiv.org/abs/2204.13188 · HTML: https://mlsanity.com/paper/2204.13188/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 27 Apr 2022.

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

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2010.05788/) (2020)
- [Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease](https://mlsanity.com/paper/1805.08801/) (2018)
- [BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis](https://mlsanity.com/paper/2107.05097/) (2021)

## Built on by (most cited first, 11 of 11)

- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [A Survey on Explainability of Graph Neural Networks](https://mlsanity.com/paper/2306.01958/) (2023)
- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion](https://mlsanity.com/paper/2409.19130/) (2024)
- [Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection](https://mlsanity.com/paper/2211.06161/) (2022)
- [GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2504.00946/) (2025)
- [Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progression](https://mlsanity.com/paper/2509.21735/) (2025)
- [Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts](https://mlsanity.com/paper/2512.10966/) (2025)
- [Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification](https://mlsanity.com/paper/2603.10253/) (2026)
- [GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging](https://mlsanity.com/paper/2606.13919/) (2026)

## Abstract (arXiv)

Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and MCI.