  It has been well established that Functional Connectomes (FCs), as estimated
from functional MRI (fMRI) data, have an individual fingerprint that can be
used to identify an individual from a population (subject-identification).
Although identification rate is high when using resting-state FCs, other tasks
show moderate to low values. Furthermore, identification rate is
task-dependent, and is low when distinct cognitive states, as captured by
different fMRI tasks, are compared. Here we propose an embedding framework,
GEFF (Graph Embedding for Functional Fingerprinting), based on group-level
decomposition of FCs into eigenvectors. GEFF creates an eigenspace
representation of a group of subjects using one or more task FCs (Learning
Stage). In the Identification Stage, we compare new instances of FCs from the
Learning subjects within this eigenspace (validation dataset). The validation
dataset contains FCs either from the same tasks as the Learning dataset or from
the remaining tasks that were not included in Learning. Assessment of
validation FCs within the eigenspace results in significantly increased
subject-identification rates for all fMRI tasks tested and potentially
task-independent fingerprinting process. It is noteworthy that combining
resting-state with one fMRI task for GEFF Learning Stage covers most of the
cognitive space for subject identification. In addition to
subject-identification, GEFF was also used for identification of cognitive
states, i.e. to identify the task associated to a given FC, regardless of the
subject being already in the Learning dataset or not (subject-independent
task-identification). In addition, we also show that eigenvectors from the
Learning Stage can be characterized as task-dominant, subject dominant or
neither, providing a deeper insight into the extent of variance in functional
connectivity across individuals and cognitive states.
