  Examining task-free functional connectivity (FC) in the human brain offers
insights on how spontaneous integration and segregation of information relate
to human cognition, and how this organization may be altered in different
conditions, and neurological disorders. This is particularly relevant for
patients in disorders of consciousness (DOC) following severe acquired brain
damage and coma, one of the most devastating conditions in modern medical care.
We present a novel data-driven methodology, connICA, which implements
Independent Component Analysis (ICA) for the extraction of robust independent
FC patterns (FC-traits) from a set of individual functional connectomes,
without imposing any a priori data stratification into groups. We here apply
connICA to investigate associations between network traits derived from
task-free FC and cognitive/clinical features that define levels of
consciousness. Three main independent FC-traits were identified and linked to
consciousness-related clinical features. The first one represents the
functional configuration it is associated to a sedative (sevoflurane), the
overall effect of the pathology and the level of arousal. The second FC-trait
reflects the disconnection of the visual and sensory-motor connectivity
patterns. It also relates to the time since the insult and to the ability of
communicating with the external environment. The third FC-trait isolates the
connectivity pattern encompassing the fronto-parietal and the default-mode
network areas as well as the interaction between left and right hemispheres,
which are also associated to the awareness of the self and its surroundings.
Each FC-trait represents a distinct functional process with a role in the
degradation of conscious states of functional brain networks, shedding further
light on the functional subcircuits that get disrupted in severe brain-damage.
