  Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging
data has significant implications, as extracting useful information from
neuroimaging data for ASD detection is challenging. Even though machine
learning techniques have been leveraged to improve the information extraction
from neuroimaging data, the varying data quality caused by different meta-data
conditions (i.e., data collection strategies) limits the effective information
that can be extracted, thus leading to data-dependent predictive accuracies in
ASD detection, which can be worse than random guess in some cases. In this
work, we systematically investigate the impact of three kinds of meta-data on
the predictive accuracy of classifying ASD based on structural MRI collected
from 20 different sites, where meta-data conditions vary.
