Uncertainty Modeling for Multicenter Autism Spectrum Disorder Classification Using Takagi–Sugeno–Kang Fuzzy Systems

Abstract

The resting-state functional magnetic resonance imaging (rs-fMRI) is a pivotal tool that can reveal brain dysfunction in the computer-aided diagnosis of the autism spectrum disorder (ASD). However, the instability of data collection devices, complexity of pathogenesis, and ambiguity in the causes of the disease always introduce considerable uncertainty in identifying ASD using rs-fMRI. Due to the strong ability of Takagi–Sugeno–Kang fuzzy inference systems (TSK FISs) in handling the uncertainty of knowledge and expression, we build an ASD classification model based on TSK FISs and further propose a novel multicenter ASD classification method FCG-MTGS-TSK. Specifically, the correlation information of multiple imaging centers is considered by introducing multitask group sparse learning, and the features across multiple imaging centers are thus jointly selected. An augmented lagrange multiplier (ALM) method is further developed to find the optimal solution of the model. Compared with the other existing methods, the proposed method has the advantages of strong interpretability and high classification accuracy. The experimental results also identify the most discriminative functional connectivity in multicenter ASD classification.