نبذة مختصرة : National audience ; Disorders of Consciousness (DOC) are prolonged states of altered consciousness, classified into coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS) based on neurobehavioral function. One of the biggest current challenges is distinguishing between different levels in DOC, since they affect the patient's recovery. Machine learning (ML) classifies patients based on graph-theoretical models of resting-state brain functional connectivity data, but algorithms reduce interpretability of results. A methodology combining data from multiple imaging modalities would provide a comprehensive understanding of DOC. We propose and apply, for the first time, a graph-theoretical model that integrates data from multiple imaging modalities, potentially providing a thorough understanding of DOC.
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