Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder

Abstract

Early diagnosis of autism spectrum disorder (ASD) is critical for timely medical intervention, for improving patient quality of life, and for reducing the financial burden borne by the society. A key issue in neuroimaging-based ASD diagnosis is the identification of discriminating features and then fusing them to produce accurate diagnosis. In this paper, we propose a novel framework for fusing complementary and discriminating features from different imaging modalities. Specifically, we integrate the Fisher discriminant criterion and local correlation information into the canonical correlation analysis (CCA) framework, giving a new feature fusion method, called Supervised Local CCA (SL-CCA), which caters specifically to local and global multimodal features. To alleviate the neighborhood selection problem associated with SL-CCA, we further propose a hierarchical SL-CCA (HSL-CCA), by performing SL-CCA with the gradually varying neighborhood sizes. Extensive experiments on the multimodal ABIDE database show that the proposed method achieves superior performance. In addition, based on feature weight analysis, we found that only a few specific brain regions play active roles in ASD diagnosis. These brain regions include the putamen, precuneus, and orbitofrontal cortex, which are highly associated with human emotional modulation and memory formation. These finding are consistent with the behavioral phenotype of ASD.