Uncertainty Modeling for Multi center Autism Spectrum Disorder Classification Using Takagi-Sugeno-Kang Fuzzy Systems

The resting state functional magnetic resonance imaging (rs-fMRI) is a pivotal tool that can reveal brain dysfunction in the computer aided diagnosis of autism spectrum disorder (ASD). However, the instability of data collecti on 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 uncertain ty of knowledge and expression, we build an ASD classification model based on TSK FISs and further propose a novel multi-center ASD classification method FCG-MTGS-TSK. Specifically, the correlation information of multliple imaging centers is con sidered by introducing multi-task 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 multi center ASD classification.