  The dynamic characteristics of functional network connectivity have been
widely acknowledged and studied. Both shared and unique information has been
shown to be present in the connectomes. However, very little has been known
about whether and how this common pattern can predict the individual
variability of the brain, i.e. "brain fingerprinting", which attempts to
reliably identify a particular individual from a pool of subjects. In this
paper, we propose to enhance the individual uniqueness based on an autoencoder
network. More specifically, we rely on the hypothesis that the common neural
activities shared across individuals may lessen individual discrimination. By
reducing contributions from shared activities, inter-subject variability can be
enhanced. Results show that that refined connectomes utilizing an autoencoder
with sparse dictionary learning can successfully distinguish one individual
from the remaining participants with reasonably high accuracy (up to 99:5% for
the rest-rest pair). Furthermore, high-level cognitive behavior (e.g., fluid
intelligence, executive function, and language comprehension) can also be
better predicted using refined functional connectivity profiles. As expected,
the high-order association cortices contributed more to both individual
discrimination and behavior prediction. The proposed approach provides a
promising way to enhance and leverage the individualized characteristics of
brain networks.
