# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 5 Discussion and Conclusions  
A DMT-GIN model is proposed for the classification of Alzheimer's disease fMRI images. The novelty of this model is manifested in three aspects. Firstly, the node features of brain network data are learned by incorporating a GIN with a self-attention mechanism in the readout layer, effectively capturing the spatial information and topological structure in fMRI images. Secondly, the model adopts a multi-task training approach, outputting age and gender information as auxiliary tasks to guide the classification task of Alzheimer's disease. Lastly, the GradNorm algorithm is used to dynamically allocate weights for different tasks, adaptively learning the correlations between tasks, thus improving overall performance. To enhance the interpretability of the model, the SHAP method is employed to identify the key brain regions that contribute significantly to the Alzheimer's disease classification task. The method was compared with seven other baseline models, and ablation experiments were conducted on the proposed framework for extensive performance evaluation. By observing the accuracy, recall rate, and AUC evaluation metrics in the experimental results, it becomes evident that DMT-GIN significantly outperforms other baseline models in terms of effectiveness. Our research has certain limitations, the first being the limited scale of the dataset. We used some publicly available datasets for experimentation. However, these datasets are relatively small in size. To fully validate the performance of the model, future research could consider using larger-scale and more diverse datasets. While we have incorporated age and gender as auxiliary tasks in our model, we acknowledge the potential existence \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **Brain Region** & **SHAP Value** & **Model** \\ \hline
1 & Precentral gyrus & 0.234 & DMT-GIN \\
2 & Middle frontal gyrus & 0.201 & DMT-GIN \\
3 & Inferior frontal gyrus, opercular part & 0.176 & DMT-GIN \\
4 & Inferior frontal gyrus, triangular part & 0.152 & DMT-GIN \\
5 & Supplementary motor area & 0.138 & DMT-GIN \\ \hline
1 & Fusiform gyrus & 0.231 & GCN \\
2 & Superior parietal lobule & 0.203 & GCN \\
3 & Precentral gyrus & 0.191 & GCN \\
4 & Middle temporal gyrus & 0.165 & GCN \\
5 & Superior frontal gyrus & 0.143 & GCN \\ \hline
1 & Fusiform gyrus & 0.240 & GIN \\
2 & Precentral gyrus & 0.210 & GIN \\
3 & Superior parietal lobule & 0.200 & GIN \\
4 & Middle frontal gyrus & 0.173 & GIN \\
5 & Supplementary motor area & 0.140 & GIN \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top 5 brain regions with the highest relevance to the Alzheimer’s disease classification task based on SHAP values for different models. of other auxiliary tasks with higher informational value. These tasks could encompass areas such as occupation, family environment, genetic information, lifestyle, and certain medical conditions like smoking status, respiratory problems, hypertension, and diabetes, which are recognized as key risk factors for numerous health conditions. These variables have been successfully employed in past studies, as exemplified by the application of photoplethysmography signals for detecting cardiorespiratory disorders [57]. Including such a diversity of auxiliary tasks could not only enrich our understanding of the subjects, but also enhance the predictive capabilities of our model. Therefore, future research could explore more auxiliary tasks with potential information value to further optimize model performance, drawing inspiration from such methods. Moreover, we recognize the potential value of incorporating anomaly detection methodologies into our future research. Given the complexity and nonlinearity of the data structures we are dealing with, anomaly detection could provide valuable insights and opportunities for performance optimization. Conceptualization, X.W. ; Methodology, Z.W. and Z.L. ; Validation, S.L. ; Data curation, Y.W. and W.Z. ; Supervision, J.X.