# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 3 Proposed Framework of LSTGINet
### Spatial Branch (SPM)  
The SPM module consists of an initial feature extraction component, a four-level backbone network, a patch attention module (PA), an average pooling layer, and a linear layer. Among them, the backbone network (ConvNeXt) [30,31] obtains multi-scale information of brain information through residual blocks of different scales, and uses cross-layer transmission to achieve information interaction. At the same time, the PA mainly performs delicate operations on multi-scale features and enriches the representation of global semantics. In addition, considering that the prior knowledge contains rich low-level semantic information, the initial feature extraction component is used to squeeze and excite the input brain spatial information to enrich the prior low-level semantics of brain information. The initial feature extraction component consists of continuous convolutions of different scales, batch normalization layers, and pooling operations. The steps for obtaining local spatial detail features are as follows:  
Step 1. The given input brain information is \(X_{A}\in\mathbb{R}^{C\times N\times N}\), where \(N,N,C=1\) represents the height, width, and channel dimensions of the input, respectively. Prior knowledge \(f_{a}\) can enrich the low-level semantic representation, while the backbone network can obtain high-level multi-scale local spatial details \(f^{\prime}_{a}\). \(f_{a}\) and \(f^{\prime}_{a}\) are shown in Equation (1). \[ \begin{split} f^{\prime}_{a}&=\varphi(f_{a})\\ &=\varphi(MaxPool(\
abla^{*}(BN(Conv_{7\times 7}(X_{A})))))\end{split}  \tag{1}\]  
where \(MaxPool(\cdot)\) represents the max pooling operation. \(Conv_{7\times 7}(\cdot)\) represents a \(7\times 7\) convolution operation. \(BN(\cdot)\) stands for the batch normalization operation. \(\
abla^{*}(\cdot)\) represents a set of continuous \(BN\) layer and \(3\times 3\) convolution layer operations, where the stride of the convolution operation is 2, and the padding is 2. \(\varphi(\cdot)\) indicates the backbone network of ConvNeXt. Step 2. To obtain more effective spatial details and refine multi-scale local spatial features [32]. First, a \(3\times 3\) convolution is used to squeeze high-level spatial features, and the PA is introduced to adaptively learn the spatial information on each path through weighted summation. The form alleviates the reuse of redundant information to improve the representation of salient features. Then, correlations are established between local  
Figure 1: The overall network structure of LSTGINet. \(X_{A}\in R^{1\times N\times N}\) and \(X_{B}\in R^{1\times N\times T}\) represent the input brain information. \(N\) indicates the number of ROIs. \(T\) indicates the length of time series. \(BN(\cdot)\) stands for the batch normalization operation. \(\gamma(\cdot)\) represents a \(7\times 7\) convolution operation. \(MaxPool(\cdot)\) stands for the maximum pooling operation. \(ConvNeXt\) represents the backbone network for spatial feature extraction. \(AvgPool(\cdot)\) stands for the average pooling operation. \(Linear(\cdot)\) represents a linear operation. \(PA\) represents the patch attention layer. \(PA+\) represents an enhanced patch attention layer. \(Sigmoid(\cdot)\) represents the activation function. \(\oplus\) represents feature matrix concatenation. \(\otimes\) means matrix multiplication. \(Weight\) represents the weight matrix. \(Recurrent\) stands for the recursive layer. \(ReLu\) represents the activation function. \(Reshape(\cdot)\) represents a matrix transformation operation. \(\mathcal{F}_{a}\) represents spatial features. \(\mathcal{F}_{b}\) represents timing characteristics. \(\mathcal{F}_{G}\) represents graph inference features. \(\mathcal{F}^{\prime}\) represents spatio-temporal fusion features. \(\bigodot\) represents feature splicing. \(Output\)s represents the prediction result. \(LAM\) indicates the local attention embedding module. \(GIM\) indicates the graph inference module. SPM indicates the spatial branch. TEM indicates the temporal branch.