Recent evidence suggests that healthy brain is organized on large-scale in regions spatially distant and partially temporally synchronized. These regions commonly are called Resting State Networks (RSNs). Many RSNs has been identified in multiples spatial scales in healthy subjects and their interactions has been used to define the functional network connectivity (FNC). The main idea in FNC is that the dynamic shown in the interactions among RSNs in control subjects, can change in pathological and pharmacological conditions. However, this hypothesis assumes that functional structure of healthy brain, remains in other brain states or conditions. In this work, we proposed a novel methodology in order to find the new brain functional structure for disorders of consciousness conditions, based on multi-objective optimization approach. Particularly, we find the best partition of RSNs set, that maximize two modularity measures (Kapur and Otsu measures). Our results suggest that the brain segregation level, may be linked to consciousness level.
Disorders of consciousness (DOC) are a consequence of a variety of severe brain injuries. DOC commonly results in anatomical brain modifications, which can affect cortical and sub-cortical brain structures. Postmortem studies suggest that severity of brain damage correlates with level of impairment in DOC. In-vivo studies in neuroimaging mainly focus in alterations on single structures. Recent evidence suggests that rather than one, multiple brain regions can be simultaneously affected by this condition. In other words, DOC may be linked to an underlying cerebral network of structural damage. Recently, geometrical spatial relationships among key sub-cortical brain regions, such as left and right thalamus and brain stem, have been used for the characterization of this network. This approach is strongly supported on automatic segmentation processes, which aim to extract regions of interests without human intervention. Nevertheless, patients with DOC usually present massive structural brain changes. Therefore, segmentation methods may highly influence the characterization of the underlying cerebral network structure. In this work, we evaluate the level of characterization obtained by using the spatial relationships as descriptor of a sub-cortical cerebral network (left and right thalamus) in patients with DOC, when different segmentation approaches are used (FSL, Free-surfer and manual segmentation). Our results suggest that segmentation process may play a critical role for the construction of robust and reliable structural characterization of DOC conditions.