  The evaluation of the individual 'fingerprint' of a human functional
connectome (FC) is becoming a promising avenue for neuroscientific research,
due to its enormous potential inherent to drawing single subject inferences
from functional connectivity profiles. Here we show that the individual
fingerprint of a human functional connectome can be maximized from a
reconstruction procedure based on group-wise decomposition in a finite number
of brain connectivity modes. We use data from the Human Connectome Project to
demonstrate that the optimal reconstruction of the individual FCs through
connectivity eigenmodes maximizes subject identifiability across resting-state
and all seven tasks evaluated. The identifiability of the optimally
reconstructed individual connectivity profiles increases both at the global and
edgewise level, also when the reconstruction is imposed on additional
functional data of the subjects. Furthermore, reconstructed FC data provide
more robust associations with task-behavioral measurements. Finally, we extend
this approach to also map the most task-sensitive functional connections.
Results show that is possible to maximize individual fingerprinting in the
functional connectivity domain regardless of the task, a crucial next step in
the area of brain connectivity towards individualized connectomics.
