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We present an approach to model time series data from resting state fMRI forautism spectrum disorder (ASD) severity classification. We propose to adoptkernel machines and employ graph kernels that define a kernel dot productbetween two graphs. This enables us to take advantage of spatio-temporalinformation to capture the dynamics of the brain network, as opposed toaggregating them in the spatial or temporal dimension. In addition to theconventional similarity graphs, we explore the use of L1 graph using sparsecoding, and the persistent homology of time delay embeddings, in the proposedpipeline for ASD classification. In our experiments on two datasets from theABIDE collection, we demonstrate a consistent and significant advantage inusing graph kernels over traditional linear or non linear kernels for a varietyof time series features.

Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series