Persistent spectral based machine learning method for autism spectrum disorder classification

Background: Autism spectrum disorder (ASD) is a widespread and intricate neurodevelopmental condition. The increasing prevalence of ASD creates a very significant burden on both society and families. Functional magnetic resonance imaging (fMRI) contributes to a deeper understanding of ASD while also facilitating the development of early diagnosis and effective treatment strategies. This study aims to provide new and more reliable tools for early diagnosis of ASD and gain deeper insights into its neural mechanisms through the combination of topology and persistent spectral theory with functional connectivity. Methods: We proposed a persistent spectral machine learning model based on the simplicial complex for characterizing the functional connectivity in the Autism Brain Imaging Data Exchange I dataset. Simplicial complexes were used to characterize the functional connectivity with coefficients no less than 0.3. We arranged a filtration value for each simplex and persistent Laplacian matrices were calculated through a filtration process. The corresponding persistent attributes, after removing covariates, were used as inputs of classifiers. Results: Achieving an accuracy of 87.5%, our model outperformed other models that applied functional connectivity, similar sample sizes and the same preprocessing pipelines. We found that the numbers and distribution of connected components and loops of the global functional connectivity are important for classification. Conclusions: This study provided a feature extraction method based on persistent spectral theory for ASD research. Our model offers a different perspective on the research of related conditions and has great and notable potential in diagnosis.