# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 4 Experimental Results and Discussion
### Comparison with Other State-of-the-Art Methods  
To demonstrate the superiority of the proposed LSTGINet age prediction framework, we compared it with the current advanced prediction methods on various datasets and provide corresponding analyses and discussions. The experimental results of different prediction methods are shown in Table 1.  
According to Table 1, we can draw the following conclusions:  
(1) On the CamCAN and NKI baseline datasets, the proposed LSTGINet age prediction framework achieves the best performance. That is, the MAE is \(6.33\) and \(6.28\), respectively. The main reason is that, on the one hand, the local spatial details and contextual global semantics are aggregated through the temporal and spatial branches, and the position attention mechanism establishes the static correlation between age and brain aging. At the same time, the complementarity and dependence between features at different levels are formed, improving the feature representation performance. On the other hand, the aggregation and transmission functions of the nodes in the graph inference module are used to obtain the dynamic correlation between them. At the same time, the local attention embedding method is used to reduce the use of irrelevant information, further strengthen \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**CamCAN**} & \multicolumn{4}{c}{**NKI**} \\ \cline{2-9}  & **R2** & **MAE** & **MSE** & **RMSE** & **R2** & **MAE** & **MSE** & **RMSE** \\ \hline CNN-BiLSTM [38] & 0.3253 & 11.02 & 241.59 & 10.71 & 0.3669 & 10.15 & 177.37 & 10.63 \\ FiANets [39] & 0.7001 & 7.26 & 113.05 & 10.44 & 0.7286 & 7.15 & 81.82 & 9.05 \\ GLTransformer [40] & 0.4932 & 9.98 & 166.97 & 9.40 & 0.6931 & 7.75 & 84.36 & 8.71 \\ BC-GCN-SE [35] & 0.6555 & 7.94 & 113.49 & 7.44 & 0.7231 & 6.68 & 61.36 & 7.50 \\ HRINets [41] & 0.6526 & 7.49 & 119.36 & 10.90 & 0.7629 & 7.05 & 86.85 & 9.31 \\ GCN [42] & 0.6453 & 8.05 & 116.85 & 8.46 & 0.6959 & 7.73 & 83.61 & 8.56 \\ LSTGINet (ours) & **0.7885** & **6.33** & **81.86** & **6.66** & **0.7859** & **6.28** & **56.09** & **6.74** \\ \hline \hline \end{tabular} The bolded parts of the table show the best results for the corresponding indicators.  
\end{table} 
Table 1: Experimental results of different prediction methods. MSE, RMSE, MAE, and R2 represent the model’s evaluation indicators. The larger the R2 value, the better the model can explain the data variation, that is, the better the model’s prediction performance. The smaller the MSE, RMSE, and MAE values, the better the model’s prediction performance. the interaction between different features, and establish practical long-term dependencies, thereby improving the overall prediction performance of the framework. (2) On the CamCAN and NKI datasets, the BC-GCN-SE, \(GCN\), and \(GLTransformer\) prediction methods achieved good competitiveness. For example, the MAE of BC-GCN-SE was 0.11 and 1.05 higher than that of GCN, respectively. The R2 of \(GLTransformer\) was 0.1521 and 0.0028 lower than that of the \(GCN\) method. This is because BC-GCN-SE has a good feature fusion strategy, excellent global semantic representation performance, and effective establishment of spatial relationships. Among them, the \(GLTransformer\) method mainly uses the local-global attention mechanism to refine the global semantics and spatial details, but due to the large number of network layers, a large amount of detailed information is lost, or problems such as fitting and gradient disappearance occur, resulting in a decrease in prediction performance. The GCN method mainly obtains deep discriminant features through convolution and uses graph nodes' transmission and aggregation capabilities to improve the representation of local and global semantics. (3) On the CamCAN dataset, the MAE of the FiANets method is 0.23 lower than that of the HRINets method.