## 4 Background Study
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Model** & **Challenges and Limitations** \\ \hline
[49] & ADNI, OASIS, UK Biobank & LDM + 3D DenseNet121 & High computational cost, requires careful fine-tuning, limited by resolution/memory constraints. \\ \hline
[50] & Alzheimer MRI (6400 images) & DDIM (Diffusion Model) + DenseNet & Diffusion models are computationally intensive; VAE had low image quality; fine-tuning reduced accuracy in some cases; computational cost vs. performance tradeoff. \\ \hline
[51] & OASIS-3 & GAN for data augmentation + EfficientNet CNN & GAN training instability; performance drop when synthetic data exceeds real data; no separate sensitivity/specificity metrics reported. \\ \hline
[52] & ADNI-3, In-house & Conditional LDM + ML classifier & No ground truth M0 for Siemens data; SNR difference between PASL/pCASL; class imbalance; vendor variability. \\ \hline
[53] & ADNI & Conditional Diffusion Model + U-Net & Small sample size; no classification metrics reported; requires broader validation with more diverse data. \\ \hline
[54] & OASIS & GAN + EfficientNet & GAN training instability; overfitting in CNN; limited dataset size; scope to explore alternate GAN models for robustness. \\ \hline
[55] & ADNI & Dual GAN with Pyramid Attention and CNN & Dependent on ADNI dataset quality; generalization affected by population diversity; limited interpretability; reliance on image features for AD detection. \\ \hline
[56] & ADNI & Residual Diffusion Model with CLIP guidance & Dependent on accurate prior info (e.g., age, gender); high computational cost; needs optimization for broader demographic generalization. \\ \hline
[57] & Kaggle & DSR-GAN + CNN & High computational complexity; SR trained on only 1700 images; generalizability and real-time scalability remain open challenges. \\ \hline
[58] & ADNI & BG-GAN + InnerGCN & Difficulty in precise structure-function mapping due to fMRI variability; biological coordination model can be improved. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._[PERSON] et al. (2025) [58] developed BG-GAN, a generative model that captures relationships between brain structure (sMRI/DTI) and function (fMRI) for Alzheimer's diagnosis. The model combines a Bidirectional Graph GAN, Inner Graph Convolutional Network (InnerGCN), and a Balancer module to stabilize training and improve data generation. Trained on the ADNI dataset across five subject categories, Normal Control (NC), Subjective Memory Complaint (SMC), Early Mild Cognitive Impairment (EMCI), Late MCI (LMCI), and Alzheimer's Disease (AD). Results showed that BG-GAN outperformed baseline GCN, GAE, and GAT models, achieving classification accuracy above 96%, with precision, recall, and F1-scores nearing 0.98-1.00 on multi-modal inputs. Interestingly, synthetic data generated by BG-GAN even yielded better classification performance than real empirical data, indicating the effectiveness of the generative modeling approach. However, challenges remain in fully capturing structural-functional brain mappings due to fMRI variability. The authors recommend integrating more biologically grounded coordination models and testing applicability across other brain disorders.