  A key strength of twin studies arises from the fact that there are two types
of twins, monozygotic and dizygotic, that share differing amounts of genetic
information. Accurate differentiation of twin types allows efficient inference
on genetic influences in a population. However, identification of zygosity is
often prone to errors without genotying. In this study, we propose a novel
pairwise feature representation to classify the zygosity of twin pairs of
resting state functional magnetic resonance images (rs-fMRI). For this, we
project an fMRI signal to a set of basis functions and use the projection
coefficients as the compact and discriminative feature representation of noisy
fMRI. We encode the relationship between twins as the correlation between the
new feature representations across brain regions. We employ hill climbing
variable selection to identify brain regions that are the most genetically
affected. The proposed framework was applied to 208 twin pairs and achieved
94.19% classification accuracy in automatically identifying the zygosity of
paired images.
