# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
However, they are prone to gradient explosion and gradient disappearance during the training stage. Furthermore, the network cannot effectively solve long-term dependencies. The long short-term memory network (LSTM) [11] can effectively solve long-term dependencies in brain age data through memory units and better extract global and contextual semantic details. Transformer [12] can better capture long-distance dependencies through the self-attention mechanism, enabling it to perform well in processing long sequences and tasks involving long-distance semantic relationships. At the same time, parallel computation of the multi-head attention mechanism in this structure improves operational efficiency, but probably due to limitations of the positional coding and attention mechanism, the prediction performance and efficiency will be greatly reduced when the semantics of the brain age information is more complex. GNNs [13] improve the prediction performance by effectively modeling age and related aging attributes. However, GNNs only uses edges as a means of propagation but do not distinguish the functions of different edges. At the same time, the efficiency of updating the hidden state of nodes by iterative methods for fixed points is not ideal, and the convergence of static points may cause more information sharing between hidden states of the nodes, resulting in the node state being too smooth, losing the feature information originally belonging to the node itself. These methods that use a single network structure to achieve age prediction often focus on local details when learning feature representation. When a specific type of feature representation is insufficient, it will seriously affect the final prediction performance. To compensate for the problem of insufficient single feature representation, some researchers and scholars have introduced temporal information on the basis of spatial information FC and designed various fusion models to further improve the prediction performance of age. For example, [PERSON] et al. [14] embedded dynamic FC into ROI to construct spatio-temporal features, and MCI diagnosis and age prediction are adaptively performed. A bidirectional gate recurrent unit (BidGRU) filters out excess noise for dynamic functional connectivity. [PERSON] et al. (2015) used a directed spatio-temporal graph network to model brain age information in order to reduce the impact of redundant information on brain age prediction models, and introduced an attentional mechanism to learn local dependencies between high-level features of FC in the spatio-temporal feature fusion stage. Similarly, to obtain practical spatial details on a time series dataset, [PERSON] et al. (2016) combined a one-dimensional convolution and a gated recurrent unit (GRU) so that the time series data have both temporal and spatial features, capturing significant features of different time intervals from the time input. To enable the interaction of spatio-temporal features ([PERSON] et al., 2016), a guided co-attention (GCA) module is introduced in the feature fusion stage to model the inter-modal interaction of spatial and temporal signal patterns, and a new sliding clustering attention module is developed to embed spatial features in temporal features in response to the global feature dependency problem of the self-attention mechanism in fMRI time series. These spatio-temporal fusion methods focus on local feature information on spatial datasets while ignoring the representation of global semantics and contextual details. For temporal data, the focus is on global semantics while ignoring the representation of contextual semantics. Although introducing the attention mechanism strengthens their fusion and interactivity, it does not consider the profound differences in representation ability and distribution between different information. To solve the limitations of the above-mentioned single feature or fusion feature on brain information representation, we propose the age prediction framework LSTGINet.