  We present an approach to model time series data from resting state fMRI for
autism spectrum disorder (ASD) severity classification. We propose to adopt
kernel machines and employ graph kernels that define a kernel dot product
between two graphs. This enables us to take advantage of spatio-temporal
information to capture the dynamics of the brain network, as opposed to
aggregating them in the spatial or temporal dimension. In addition to the
conventional similarity graphs, we explore the use of L1 graph using sparse
coding, and the persistent homology of time delay embeddings, in the proposed
pipeline for ASD classification. In our experiments on two datasets from the
ABIDE collection, we demonstrate a consistent and significant advantage in
using graph kernels over traditional linear or non linear kernels for a variety
of time series features.
