# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 2 Materials and Methods
### Multi-Site Anti-Interference Neural Network  
We propose a multi-site autism classification method with anti-interference neural networks (MS-AINN). MS-AINN is composed of representation learning module, site feature extraction module and anti-interference classification module, and adopts multi-task adversarial design. The overall architecture is shown in Figure 2.  
Figure 2: Multi-site anti-interference neural network classification model. The black arrows in the diagram represent the output of the previous module as the input of the next module. The red arrows represent the output features of the representation learning module as input vectors for the two tasks of the anti-interference classification module. The green arrow represents the site feature vector obtained by the site feature extraction module as the label of the two tasks of the anti-interference classification module.  
Firstly, multi-site fMRI images are extracted into functional connections after data preprocessing, and functional connections are used as input data for the model. Then, the site feature extraction module will extract site features from the functional connections of the subjects to quantify the heterogeneity between sites, and the site features will be used as labels in the multi-task. The specific process is to first extract low-dimensional subject level feature vectors through AE, then use site average pooling to calculate the mean vector of all subject level feature vectors in the site as site features, and finally select low-redundancy site features through phenotypic information correlation features.  
After the above two parts of the process, we have the input data functional connection required for MS-AINN multitask design, as well as two kinds of labels, disease type labels and site characteristics labels that quantify site heterogeneity. MS-AINN can then be used for ASD classification training. Firstly, ASD classification features are extracted from functional connections through the representation learning module. Specifically, large-scale one-dimensional volume nuclei are used for feature extraction, because the weight of the convolutional nuclei corresponds to brain regions, and the importance of each brain region to ASD classification can be analyzed. The convolution result will be further classified through the Fully Connected Layer (FC) for further feature extraction. Then, the obtained classification features will perform two tasks of MS-AINN through the anti-interference classification module, which are site feature regression task and anti-interference classification task. The reason is that the objective function of the anti-interference classification task is composed of ASD classification loss and site feature regression loss. However, the direction of regression loss optimization in the anti-interference classification task is opposite to that in the site feature regression task, that is, the anti-interference classification task will increase the regression loss but the site feature regression task will reduce the regression loss. The anti-interference classification module performs two kinds of tasks with ASD classification features as input through the FC layer. When it comes to site feature regression, both tasks also use the same FC layer for regression.  
MS-AINN adopts the training mode of alternating iteration of two tasks, and the updated parameters of each task are independent of each other and do not overlap each other. After the adversarial training of the two tasks, the model can effectively remove the influence of multi-site confounding effect (multi-site confounding effect refers to the confounding effect caused by the heterogeneity of multi-site data) on feature extraction of the representation learning module.