Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series
This work addresses ASD diagnosis, a critical medical challenge, but is incremental as it builds on existing kernel methods with new graph-based features.
The authors tackled autism spectrum disorder (ASD) severity classification by modeling resting state fMRI time series with graph kernels to capture spatio-temporal brain dynamics, achieving a consistent and significant advantage over traditional kernels on two ABIDE datasets.
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.