# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 1 Introduction
For example, when the purpose of a neuroimaging study is to distinguish between healthy individuals and patients with ASD, if the number of patients at one site is significantly higher than that at another site, the characteristics of the differences in the data at that site may be the criteria for the model to identify the disease. This heterogeneity has the result that although many methods can obtain relatively high accuracy at a single site, when applied to datasets at other sites, the trained models usually fail to achieve an acceptable performance [16; 17], which is not conducive to early intervention and auxiliary diagnosis of ASD. For example, [PERSON] et al. [16] achieve a maximum classification accuracy of 90% in the single-site ASD classification experiment of ABIDE dataset, but only a maximum classification accuracy of 60% in the multi-site ASD classification experiment of 17 sites. There are two methods to solve multi-site data heterogeneity: data processing and model-based. The method based on data processing solves the problem of multiple sites by eliminating the difference of data domain distribution between sites. For example, [PERSON] et al. [18] proposed a multi-site adaptive framework based on low-rank representation decomposition, in which the data of one site are regarded as the target domain and the data of other sites as the source domain, and the data in these domains are converted into a low-rank representation public space, so as to reduce the difference in data distribution among different sites. Model-based studies remove the influence of sites through the model itself. For example, [PERSON] et al. [19] proposed a training scheme based on deep learning, which uses iterative updating to remove scanner information and generate unchanged shared features of sites, so as to realize the prediction of a site-free model. In addition, in the study on removing the influence of confounding effects, [PERSON] et al. [20] proposed an end-to-end method, which quantifies the statistical dependence between the features extracted from the model and confounding factors, so as to guide the elimination of confounding effects in the process of feature extraction. The experimental results show that the model has a high accuracy in AIDS classification after removing confounding factors. In the above methods to solve the multi-site problem, the site data domain mapping and classifier training based on data processing are independent of each other, which may reduce the learning performance, so it is necessary to design a new framework to make the two interrelated. The model-based approach may delete information related to disease classification while removing scanner information, so it is necessary to improve the architecture balance feature extraction and scanner information removal. To solve the confounding effect problem, it is necessary to establish the statistical dependence of confounding factors and other features. However, confounders from multiple sites cannot be directly correlated with classification features. In order to solve the confounding effect caused by data heterogeneity of multi-sites, we propose a multi-site anti-interference neural network (MS-AINN) for multi-site autism classification. First, MS-AINN extracts site features from functional connections through AE, site average aging pool and feature selection, and instantiates abstract site confounding factors into vector form to establish a mapping relationship between feature extraction and site confounding. Secondly, the representation learning module designed by convolutional neural network (CNN) uses a large-scale one-dimensional convolutional kernel, which can directly extract features from functional connections and fully consider the physiological significance of brain functional networks. The reason is that the receptive field of the convolution kernel contains a whole row in the functional connection matrix, and each convolution can extract domain information of all neighboring brain regions of the target brain region.