Towards Effective Functional Connectome Fingerprinting

Abstract

The ability to uniquely characterize individual subjects based on their functional connectome (FC) is a key requirement for progress towards precision neuroscience. The recent availability of dense scans from individuals has enabled the neuroscience community to investigate the possibility of individual characterization. FC fingerprinting is a new and emerging problem where the goal is to uniquely characterize individual subjects based on FC. Recent studies reported near 100% accuracy suggesting that unique characterization of individuals is an accomplished task. However, there are multiple key aspects of the problem that are yet to be investigated. Specifically, (i) the impact of the number of subjects on fingerprinting performance needs to be studied, (ii) the impact of granularity of parcellation used to construct FC needs to be quantified, (iii) approaches to separate subject-specific information from generic information in the FC are yet to be explored. In this study, we investigated these three directions using publicly available resting-state functional magnetic resonance imaging data from the Human Connectome Project. Our results suggest that fingerprinting performance deteriorates with increase in the number of subjects and with the decrease in the granularity of parcellation. We also found that FC profiles of a small number of regions at high granularity capture subject-specific information needed for effective fingerprinting.