# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 4 Discussion  
In this study, we propose an MS-AINN-CNN (1D) deep learning model, to classify ASD and TDS on large multi-site rs-fMRI data based on whole-brain functional connections. MS-AINN is composed of two key parts: (1) Site feature learning, which extracts site features from the functional connections of subjects by using AE, site average pooling and phenotypic information feature selection, so as to reflect the heterogeneity information among sites, and then uses it to build a mapping relationship with the representation learning features to provide necessary conditions for adversarial training against the impact of site confounding. (2) Anti-interference model training, using two tasks of site feature regression and anti-interference classification to reduce the site heterogeneity information contained in the representation learning features, so that the model classification is less affected by the multi-site confounding effect. The results show that when the representation learning module is CNN (1D), MS-AINN can reduce the accuracy of site classification of representation learning features, indicating that the impact of the representation learning module on the site confounders is weakened, which proves that MS-AINN can realize the representation learning to the site confounders. Moreover, all classification indexes of MS-AINN-CNN (1D) were improved, which proved that MS-AINN's confounder remove representation learning successfully improved the model classification performance. In addition, the results of the representation learning module replacement experiment show that MS-AINN can also bring about the same disaggregation effect and obvious classification performance improvement when using MLP and CNN (2D) representation learning modules, indicating that anti-interference ideas play a key role in the entire, while the representation learning module provides a baseline performance for classification. Among them, MLP and CNN (2D) have lower classification effect than CNN (1D), which proves that large-scale convolutional kernel design considering physiological significance has superior performance in ASD classification research. In the optimization results of AE hidden layer, the overall accuracy rate increases first and then decreases with the decrease in site feature dimension, because different parameters will obtain site features of different dimensions. When the dimension is large, there are more redundant features, and when the dimension is small, site differences cannot be fully expressed, and the optimal results cannot be achieved in both cases. In the site feature selection threshold experiment, the results show that with the change of feature selection threshold from low to high, the accuracy and auc increase first, then decrease and  
Figure 8: CNN (1D) Feature vector visualization. (**A**) is the result of CNN (1D), and (**B**) is the result of MS-AINN-CNN (1D). then increase, and the highest accuracy is 75.56%, auc is 78.99% when the threshold is 0.3. The reason is that when the threshold is low, the features selected are high-scale related features, which are composed of site image features and potentially contain phenotypic information, and have a strong ability to reflect the difference between sites. Therefore, the increase in such features can enable MS-AINN to more accurately realize the impact of site confounding, and the classification accuracy will therefore increase. The threshold value of 0.3 to 0.7 is the decline range of accuracy. At this time, there are fewer features related to the high scale in the site features. With the increase in the threshold value, the redundant features begin to increase, and the effect of the confounding remove begins to decline, and the accuracy also declines. The threshold value of 0.7 to 1.0 is the interval where the accuracy rate rises again.