Low-rank Domain Adaptive Method with Inter-class difference Constraint for Multi-site Autism Spectrum Disorder Identification

Abstract

Autism spectrum disorder (ASD) is an incurable neurodevelopmental disorder with a wide range of clinical symptoms that mainly include social and communication deficits. Unfortunately, there is still no effective method for ASD diagnosis. Recently, researchers have presented a number of machine learning methods for ASD identification based on multi-site data, and these methods have achieved remarkable results. However, multi-site data is directly used, ignoring the heterogeneity between different sites. To address this issue, we propose a low-rank domain adaptive method with inter-class difference constraint (LRDAIC) for multi-site ASD identification based on resting-state functional magnetic resonance imaging (rs-fMRI). Firstly, we treat one site as the target domain and the remaining sites as the source domains. Then, data from these domains is transformed into a common space while considering inter-class difference, and the inter-class difference constraint term is further introduced to maximize the distance between different classes to enhance data discrimination ability. Moreover, each class of data from each source domain is linearly represented by all the data of the corresponding class from the target domain in this space. Finally, we evaluate the performance of our method on the basis of the ABIDE1 dataset, and the results demonstrate that our method is superior to several state-of −the-art low-rank domain adaptation methods.