Functional resting state networks characterization through global network measurements for patients with disorders of consciousness

摘要: Disorders of consciousness (DOC) is a consequence severe brain injuries. DOC diagnosis quite challenging because it may require patient collaboration. Investigations activity in resting conditions propose that healthy organized into large-scale state networks (RSNs) sensory/cognitive relevance. The complete set RSN together with their corresponding interaction induce functional network connectivity (FNC). Recently, the pattern between pairs RSNs have been explored as biomarker loss consciousness. role this FNC remains poorly understood. In work, we to use analysis method explore complex properties induced by among RSNs. particular, aim characterize communication quality nodes, which suggested be linked altered states proposed approach was evaluated on population 27 controls and 49 subjects conditions. fMRI data obtained processed for each subject built at individual level. nodes quantified using global efficiency, average characteristic path, diameter, radius, strength clustering coefficient. Our results suggests information efficiency transfer level decrease severity condition. These highlight importance graph based features complexity, phenomena emergence. addition, our can potentially used development novel methods support patients