  Determining biomarkers for autism spectrum disorder (ASD) is crucial to
understanding its mechanisms. Recently deep learning methods have achieved
success in the classification task of ASD using fMRI data. However, due to the
black-box nature of most deep learning models, it's hard to perform biomarker
selection and interpret model decisions. The recently proposed invertible
networks can accurately reconstruct the input from its output, and have the
potential to unravel the black-box representation. Therefore, we propose a
novel method to classify ASD and identify biomarkers for ASD using the
connectivity matrix calculated from fMRI as the input. Specifically, with
invertible networks, we explicitly determine the decision boundary and the
projection of data points onto the boundary. Like linear classifiers, the
difference between a point and its projection onto the decision boundary can be
viewed as the explanation. We then define the importance as the explanation
weighted by the gradient of prediction $w.r.t$ the input, and identify
biomarkers based on this importance measure. We perform a regression task to
further validate our biomarker selection: compared to using all edges in the
connectivity matrix, using the top 10\% important edges we generate a lower
regression error on 6 different severity scores. Our experiments show that the
invertible network is both effective at ASD classification and interpretable,
allowing for discovery of reliable biomarkers.
