# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
First, considering that prior knowledge is beneficial to the representation of brain information, large kernel convolution is applied to the spatio-temporal branches to extract shallow local features and use them as prior knowledge containing rich low-level semantics. Secondly, for the spatial branch, we introduce a deep residual network (ConvNeXt) to capture multi-scale local spatial details, and the residual connection between different scales can reduce the loss of global semantics. For the temporal branch, a newly designed bidirectional independent recurrent network (BindRNN++) establishes long-distance dependencies from both positive and negative directions and focuses on representing global semantics and contextual details. Unlike simple LSTM or RNN, the neurons in the newly designed module are independent of each other and cross-layer connected, which can effectively alleviate the problems of gradient vanishing and explosion in upper distance dependencies. We embed two different path patch attention modules in the spatio-temporal branch to reduce the transmission of redundant information and enhance the embedding of contextual information in the spatio-temporal branch simultaneously. Finally, to reduce the differences between spatio-temporal features during the fusion process, a local attention embedding module is designed to achieve the aggregation of different features. The semantic information of temporal features is enriched by embedding the local focus of spatial features, and the representation of spatial semantic information can be enhanced without losing spatial information. Similarly, to explore the hidden correlation between spatio-temporal features and age, the transmission and aggregation functions of the reasoning nodes in the neighborhood of the graph reasoning module are used to enable information interaction, and the edge weights between the reasoning nodes represent the hidden relationship between age and spatio-temporal features. The age prediction framework of LSTGINet we propose improves feature representation by obtaining local, global, and contextual details in brain information and establishing practical long-term dependencies. At the same time, it makes up for the shortcomings of single features in describing brain information in the aging stage and establishes interactions between them to improve prediction performance. The main contributions of this study are as follows:A local attention spatio-temporal graph reasoning network (LSTGINet) is developed to explore the hidden associations between age and brain information by building a sizeable topological graph. The dynamic aggregation function of graph reasoning nodes forms an interactive relationship between age and brain information to highlight the differences in brain information at different ages. In addition, the local attention mechanism is embedded in the graph reasoning module to enrich the representation of global spatio-temporal semantics, establish dependencies between positions and channels, and maintain a balance between local, global, and contextual semantics. 2. A spatio-temporal branch feature extraction module is designed to model the local spatial details and global semantics of brain information from both temporal and spatial perspectives. An improved bidirectional independent recurrent neural network is used on the temporal branch to encode brain information from both positive and negative directions, improving the inadequacy of contextual semantic representation. In addition, a novel weighted loss function performs separate supervised learning on each module, prompting the prediction framework to achieve optimal performance. Finally, the optimal prediction performance is achieved on both baseline datasets. The rest of this study is organized as follows: Section 2 highlights a series of works related to the age prediction task. Section 3 elaborates on the proposed LSTGINet age prediction framework and describes the working functions of different components.