# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 5 Conclusions and Future Research Plans  
This study takes into account the importance of spatio-temporal features in brain information, as well as the fact that traditional deep learning prediction methods are limited by receptive fields and poor modeling of long-term dependencies and cannot fully capture the rich local, global, and contextual spatio-temporal information. A local attention spatio-temporal graph inference network (LSTGINet) for age prediction was developed, which uses a feature extractor with pre-trained weights to obtain multi-scale local spatial details, establish practical dependencies between features at different scales, and utilize the designed bidirectional independent recurrent neural network models temporal information from both forward and reverse directions to strengthen the association between contextual semantics and global details. Then, these spatio-temporal features are refined, and the interaction between temporal and spatial features is enhanced with the help of a local attention embedding module. Effective long-term dependencies are also established between them. In addition, the transferability of features between nodes in the graph inference module is used to mine the hidden correlation between brain information and age. At the same time, the feature aggregation function between nodes helps to complement different features and make up for single structural features. Problems include insufficient  
Figure 6: Age prediction curves using different methods. ‘CamCAN’ and ’NKI’ represent different datasets. The red line ‘Mean’ in the figure represents the mean of the absolute value of the difference between the predicted brain age and the actual brain age. The red line ‘Medium’ in the figure represents the median of the absolute value of the difference between the predicted brain age and the actual brain age. \(\mathcal{L}_{m}\) represents the main loss function. \(\mathcal{L}_{\xi}\) represents the graph inference loss function.  
information representation and avoiding interference between features at different levels. Finally, experimental results obtained on different baseline datasets show that the network proposed in this study has good prediction performance and robustness.  
During the experiment, we found that this method still needs to improve, such as regarding the complicated setting of the learnable factors in the weighted loss function and the high model complexity of this method. The prediction efficiency could be better. Therefore, in subsequent work, we will solve the above limitations and develop a more efficient and faster spatio-temporal semantic embedding model to improve age prediction performance further.  
Conceptualization, Y.L., X.W. and R.C. ([PERSON]); methodology, formal analysis, and data curation, Y.L., X.W., P.W. and Y.G.; visualization, Y.L.; resources and supervision, Y.H., R.C. ([PERSON]) and C.G.; funding acquisition, Y.H. and R.C. ([PERSON]); writing--original draft, Y.L.; writing--review and editing, Y.L., X.W., and R.C. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shanxi (202103021223035, 202303021221001) Science and Technology Innovation Program for Higher Education Institutions in Shanxi Province (RD2300004062).  
Not applicable.  
The fMRI data used in this research are publicly available at [36; 37].  
We are very grateful to the experts for their valuable opinions and to the classmates and teachers who helped with tasks such as paper writing and experimental design. At this moment, we declare that all those involved in the writing and design of this paper agree to its publication.  
The authors declare no conflicts of interest.