  Recent neuroimaging studies have shown that functional connectomes are unique
to individuals, i.e., two distinct fMRIs taken over different sessions of the
same subject are more similar in terms of their connectomes than those from two
different subjects. In this study, we present significant new results that
identify, for the first time, specific parts of resting-state and task-specific
connectomes that code the unique signatures. We show that a very small part of
the connectome codes the signatures. A network of these features is shown to
achieve excellent training and test accuracy in matching imaging datasets. We
show that these features are statistically significant, robust to
perturbations, invariant across populations, and are localized to a small
number of structural regions of the brain. Furthermore, we show that for
task-specific connectomes, the regions identified by our method are consistent
with their known functional characterization. We present a new matrix sampling
technique to derive computationally efficient and accurate methods for
identifying the discriminating sub-connectome and support all of our claims
using state-of-the-art statistical tests and computational techniques.
