# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 3 Results
### Visual Analysis  
Since CNN (1D) adopts a large-scale convolution kernel considering the topology of brain functional network, and there is a one-to-one correspondence between the weight of the convolution kernel and brain region, we use the results of the normalization of the absolute value of the model weight to represent the classification contribution of brain region, and visualized the CNN (1D) and MS-AINN-CNN (1D), respectively; the results are shown in Figure 7, where A is the result of CNN (1D), and B is the result of MS-AINN-CNN (1D). The redder the color, the higher the relative importance of the brain regions, and the bluer the lower. Brain regions of great relative importance in the two structures include  
Figure 5: Classification results of site features in different dimensions. Figure 6: Site feature selection results. vermis, frontal lobe, posterior cingulate gyrus, orbital part, fusiform gyrus, thalamus, temporal transverse gyrus, precuneus, horny gyrus, caudate nucleus, and hippocampus. These brain regions are the same as those reported in the literature for differences between ASD and TD [7; 27; 28; 29; 30; 31; 32]. This indicates that the representation learning module can actively identify the brain regions that change due to disease, and extract the difference features for classification. Moreover, the high-contribution brain areas in the MS-AINN-CNN (1D) were the same as CNN (1D), indicating that although the MS-AINN-CNN (1D) limited the feature extraction of the representation learning module to remove the confusion of sites, it did not affect the recognition of important brain areas by the model. At the same time, parts of the brain that were bluer became bluer and less important. It shows that the MS-AINN also makes the representation learning module pay more attention to the important brain regions that may have physiological differences and ignore the non-important brain regions. After reducing the weight value of non-critical brain areas, the features extracted contain the difference information of normal patients with important brain areas, and reduce the interference of confounding factors of non-important brain areas, and finally improve the classification performance. In order to more intuitively reflect the impact of multi-site data heterogeneity on feature extraction, t-SNE [29] is used to visualize the spatial distribution of feature vectors extracted by CNN (1D) representation learning module, as shown in Figure 8A,B. Each color in the figure represents data for one site. In order to display results more clearly, we choose four sites with the largest number of subjects in the ABIDE dataset for visualization, namely, UM, USM, NYU and UCLA. Figure 8A shows that the feature vectors extracted by CNN (1D) are relatively dispersed in spatial distribution, and the feature vectors between different stations have a certain degree of domain separation. From Figure 8B, It can be seen that the feature vectors extracted by MS-AINN-CNN (1D) are relatively dense in spatial distribution, and the domains of feature vectors at different sites overlap each other. The reason for the two different spatial distributions lies in the heterogeneity of the multi-site data. The feature extraction directly through the representation learning module will be affected by this and result in the separation of the feature vector domain between the sites. However, in order to extract the feature vectors affected by the hybridization to the sites, the MS-AINN restricts the feature extraction process of the representation learning module by means of  
Figure 7: Visualization of brain region contribution in CNN (1D). (**A**) shows the relative importance of each brain region after CNN (1D) visualization. (**B**) shows the relative importance of each brain region after visualization by MS-AINN-CNN (1D). In (**A**,**B**), the bluer the color, the less important the brain area, and the redder the color, the more important the brain area.