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A key strength of twin studies arises from the fact that there are two typesof twins, monozygotic and dizygotic, that share differing amounts of geneticinformation. Accurate differentiation of twin types allows efficient inferenceon genetic influences in a population. However, identification of zygosity isoften prone to errors without genotying. In this study, we propose a novelpairwise feature representation to classify the zygosity of twin pairs ofresting state functional magnetic resonance images (rs-fMRI). For this, weproject an fMRI signal to a set of basis functions and use the projectioncoefficients as the compact and discriminative feature representation of noisyfMRI. We encode the relationship between twins as the correlation between thenew feature representations across brain regions. We employ hill climbingvariable selection to identify brain regions that are the most geneticallyaffected. The proposed framework was applied to 208 twin pairs and achieved94.19% classification accuracy in automatically identifying the zygosity ofpaired images.

Automatic Identification of Twin Zygosity in Resting-State Functional MRI