  Autism spectrum disorder (ASD) is one of the major developmental disorders
affecting children. Recently, it has been hypothesized that ASD is associated
with atypical brain connectivities. A substantial body of researches use
Pearson's correlation coefficients, mutual information, or partial correlation
to investigate the differences in brain connectivities between ASD and typical
controls from functional Magnetic Resonance Imaging (fMRI). However,
correlation or partial correlation does not directly reveal causal influences -
the information flow - between brain regions. Comparing to correlation,
causality pinpoints the key connectivity characteristics and removes redundant
features for diagnosis.
  In this paper, we propose a two-step method for large-scale and cyclic causal
discovery from fMRI. It can identify brain causal structures without doing
interventional experiments. The learned causal structure, as well as the causal
influence strength, provides us the path and effectiveness of information flow.
With the recovered causal influence strength as candidate features, we then
perform ASD diagnosis by further doing feature selection and classification. We
apply our methods to three datasets from Autism Brain Imaging Data Exchange
(ABIDE).
  From experimental results, it shows that with causal connectivities, the
diagnostic accuracy largely improves. A closer examination shows that
information flows starting from the superior front gyrus to default mode
network and posterior areas are largely reduced. Moreover, all enhanced
information flows are from posterior to anterior or in local areas. Overall, it
shows that long-range influences have a larger proportion of reductions than
local ones, while local influences have a larger proportion of increases than
long-range ones. By examining the graph properties of brain causal structure,
the group of ASD shows reduced small-worldness.
