# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 1 Introduction  
Autism spectrum disorder (ASD) is a generalized neurodevelopmental disorder characterized by difficulty in social communication, repetitive patterns of behavior, and narrow interests [1]. Recent studies show that 23 out of every 1000 eight-year-olds in the United States have ASD [2]. Early diagnosis and intervention are key to preventing the exacerbation of symptoms in ASD patients, improving the quality of life of patients and easing the burden on families [3]. At present, the diagnosis of autism is based on symptom-based clinical criteria, which requires a large number of behavioral assessments and requires high professional knowledge of doctors. Moreover, the diagnosis results are affected by doctors' subjectivity, which may lead to misdiagnosis and delayed diagnosis [4]. Therefore, it is necessary to develop an objective, accurate and rapid diagnostic method for ASD. Neuroimaging has been shown to be useful in diagnosing brain diseases and explaining underlying pathologic mechanisms [5]. Resting-state functional magnetic resonance imaging (rs-fMRI) has become one of the commonly used imaging methods in ASD research due to its non-invasiveness, easy acquisition, low patient effort and high generalization ability [6; 7; 8]. The amount of rs-fMRI data at a single site is usually small, leading to the possibility of disputable reproducibility and universality of studies [9]; using multi-site data to compose big data integration is an effective method to solve this problem. Autism Brain Imaging Data Exchange (ABIDE) aggregates functional and structural brain imaging collected by laboratories around the world, providing a public multi-site dataset for ASD-related research. In specific ASD classification methods, the high dimension of the original image may lead to model overfitting, and the derived functional connectivity dimension is low and can reflect the specific characteristics of ASD [10], so most methods detect autism spectrum disorders from functional connectivity. For example, [PERSON] et al. [11] designed a joint learning program of autoencoder (AE) and single-layer perceptrons, connected these networks with low-dimension representations in a mixed way with learning functions, and completed classification on ABIDE multi-site data to reach an accuracy of 70.3%. [PERSON] et al. [12] first defined the graph structure based on functional connections, and proposed a graph convolutional network (cGCN) based on functional connections for ASD classification. This method can extract the spatial features of the neighbor domain of the target brain region from the functional connections. The neighbors of the target brain area are calculated by group functional connections, and each convolution result is the feature of the target brain area. Therefore, the feature finally extracted is the result of all brain areas after considering the functional neighbor information, which can be consistent with the functional organization of the brain. The purpose of the design is to make the convolution operation have brain physiological significance, so as to extract features more efficiently. Finally, the accuracy rate reaches 71.6%. However, different sites usually have differences in collection equipment, collection parameters and collection population, which results in the natural heterogeneity of multi-site datasets [9; 13]. The confounding effect caused by multiple sites will affect the relationship between input data and output variables, leading to the correlation between site differences and biological predictions. Such correlations can hamper estimates of true biological changes, or extrapolate non-biological differences to biological differences, resulting in false or biased predictions from models [14]. For example, when the collection equipment is different, the fMRI images obtained by the same subject will be different, which will affect the analysis of physiological differences such as gender differences [15].