  So far, fingerprinting studies have focused on identifying features from
single-modality MRI data, which capture individual characteristics in terms of
brain structure, function, or white matter microstructure. However, due to the
lack of a framework for comparing across multiple modalities, studies based on
multi-modal data remain elusive. This paper presents a multi-modal analysis of
genetically-related subjects to compare and contrast the information provided
by various MRI modalities. The proposed framework represents MRI scans as bags
of SIFT features, and uses these features in a nearest-neighbor graph to
measure subject similarity. Experiments using the T1/T2-weighted MRI and
diffusion MRI data of 861 Human Connectome Project subjects demonstrate strong
links between the proposed similarity measure and genetic proximity.
