Table 3:
Summary: of Notable GCN-Based Research in AD Study.
| Work | Dataset and sample size | Input graph | Model | Performance (accuracy) |
|---|---|---|---|---|
| Parisot et al. (2018) | ADNI: 289pMCI, 251sMCI | Graph: population. Node: Single subject with imaging based (T1) feature vectors. Edge: weighted using phenotypic information. | Vanilla spectral GCNs (Defferrard et al., 2016) | pMCI/sMCI: 0.80 |
| Zhang et al. (2019) | ADNI: 116NC, 93MCI | Graph: individual. Node: atlas-based brain region with rs-fMRI signal as features. Edge: SC. | GCRNN: combination of RNN and GCN | NC/MCI: 0.935 |
| Zhang et al. (2020a) | ADNI: 116NC, 93MCI | Graph: individual. Node: atlas-based brain region with rs-fMRI signal as features. Edge: learnable, integration of SC and rs-fMRI signal. | Deep cross-model attention network, which combine RNN, GCN, and attention layer | NC/MCI: 0.983 |
| Jiang et al. (2020) | ADNI: 99MCI, 34AD | Graph: both individual and population. Individual: FC. Population: node is single subject; edge is the learned embedding from FC. | Hi-GCN: a hierarchical GCN framework | AD/MCI: 0.785 |
| Li et al. (2021b) | ADNI: 99MCI, 34AD ABIDE: 403ASD, 468 NC | Graph: both individual and population. Individual: FC. Population: node is single subject; edge is the learned embedding from FC. | TE-HI-GCN: an ensemble of transfer hierarchical GCN. Transfer is conducted between different diseases: AD and ASD | ASD/NC: 0.765 AD/NC: 0.894 |
| Zhang et al. (2021) | ADNI: 116NC, 98MCI | Graph: individual, dynamic. Node: atlas-based brain region with FC as features. Edge: deep fusion of both structural and functional data, dynamically updated. | Deep brain connectome, multi-modal dynamic GCN | NC/MCI: 0.927 |
| Huang and Chung (2022) | ADNI: 289pMCI, 251sMCI TADPOLE: 557(NC + MCI + AD) | Graph: population. Node: Single subject. Edge: learnable variational edges using imaging and nonimaging data. | Vanilla spectral GCNs (Defferrard et al., 2016) | pMCI/sMCI: 0.7940 NC/MCI/AD: 0.8779 |
| Zhu et al. (2022) | ADNI: 51AD, 52NC, 43pMCI, 56sMCI | Graph: population, dynamic. Node: Single subject with gray matter volume as initial feature and dynamically updated. Edge: correlation of samples, and dynamically updated. | Dynamic GCN, coupling interpretable feature learning with dynamic graph learning | Refer to Fig. 2 in the paper |
| Li et al. (2022) | ADNI: 226NC, 226pMCI, 163sMCI, 186AD | Graph: population. Node: Single subject with gray matter volume as features. Edge: correlation of samples. | FSNet: a novel dual interpretable GCN, can simultaneously select significant features and samples | AD/NC: 0.844 AD/MCI: 0.736 NC/MCI: 0.718 sMCI/pMCI: 0.702 |
Abbreviations: sMCI: stable mild cognitive impairment; pMCI: progressive MCI; ASD: autism spectrum disorder.