mlsanityData to 14 Oct 2026

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

arXiv 2204.13188 ↗First posted 27 Apr 2022cs.LG cs.CV eess.IVcited by 11

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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.

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

8 of 19 references.

References · 8

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 754
  4. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  5. 2020Parameterized Explainer for Graph Neural Networkcited by 426
  6. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
  7. 2018Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Diseasecited by 19
  8. 2021BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysiscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 3; 2025: 3; 2026: 2.

11 papers.

  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
  2. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  3. 2023A Survey on Explainability of Graph Neural Networkscited by 29
  4. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  5. 2024Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusioncited by 9
  6. 2022Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detectioncited by 2
  7. 2025GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networkscited by 1
  8. 2025Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progressioncited by 1
  9. 2025Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Expertscited by 0
  10. 2026Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classificationcited by 0
  11. 2026GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imagingcited by 0