# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 4 Experimental Results and Discussion
### Ablation Experiment  
To demonstrate whether each module in the proposed LSTGINet of age prediction framework plays a positive role in the overall performance, a series of ablation experiments were conducted on each component of the CamCAN and NKI datasets. The experimental results are shown in Tables 2 and 3. \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 SPM & 0.6882 & 7.86 & 127.13 & 10.34 & 0.7775 & 6.74 & 114.01 & 10.75 \\ SPM + TEM & 0.6483 & 7.12 & 120.85 & 8.33 & 0.7823 & 6.73 & 62.31 & 7.49 \\ SPM + TEM + LAM & 0.6943 & 7.09 & 111.93 & 7.42 & 0.7825 & 6.46 & 58.19 & 7.31 \\ SPM + TEM + GIM & 0.7617 & 6.42 & 107.03 & 7.25 & 0.7845 & 6.39 & 56.13 & 7.05 \\ SPM + TEM + LAM + GIM (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 2: Experimental results of different modules. LAM represents the local attention embedding module. TEM represents the temporal branch. SPM represents the spatial branch. GIM represents the graph inference module. 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. Figure 3: The changing curves of training and validation loss functions of different prediction methods. Among them, ‘CamCAN’ and ‘NKI’ represent different datasets. The colored area represents the fluctuation error of the loss function during the training and validation stages, namely, the standard deviation of all loss values after 10-fold cross-validation. \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 \(\mathcal{L}_{m}\) & 0.7551 & 6.99 & 106.90 & 7.33 & 0.7406 & 6.63 & 72.66 & 7.51 \\ \(\mathcal{L}_{m}+\mathcal{L}_{j}\) & 0.7623 & 6.61 & 111.31 & 10.01 & 0.7802 & 6.30 & 56.15 & 7.45 \\ \(\mathcal{L}_{m}+\mathcal{L}_{s}+\mathcal{L}_{t}+\mathcal{L}_{j}\) (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 3: Experimental results of different loss function. \(\mathcal{L}_{m}\) represents the main loss function. \(\mathcal{L}_{j}\) represents the graph inference loss function. 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. Referencing Tables 2 and 3, we draw the following findings:  
(1) The SPM branch showed the worst performance on datasets such as CamCAN and NKI. This method focuses on spatial detail features and ignores the temporal relationship contained in the brain dataset. The MAE of SPM + TEM + GIM is 0.77 and 0.07 lower than that of SPM + TEM + LAM, respectively. This shows that GIM is more beneficial to the network modeling of temporal and spatial details than LAM. At the same time, using the transfer and aggregation capabilities of inference nodes improves the representation of global contextual semantics. On the CamCAN dataset, SPM + TEM + LAM improves R2 by 0.0061 and 0.046, respectively, compared with SPM and SPM + TEM methods. This shows that temporal and spatial features complement each other, which is beneficial for representing brain information.