Recent advances in the study of spontaneous brain activity have demonstrated 
activity patterns that emerge with no task performance or sensory stimulation; 
these discoveries hold promise for the study of higher-order associative network 
functionality. Additionally, such advances are argued to be relevant in 
pathological states, such as disorders of consciousness (DOC), i.e., coma, 
vegetative and minimally conscious states. Recent studies on resting state 
activity in DOC, measured with functional magnetic resonance imaging (fMRI) 
techniques, show that functional connectivity is disrupted in the task-negative 
or the default mode network. However, the two main approaches employed in the 
analysis of resting state functional connectivity data (i.e., hypothesis-driven 
seed-voxel and data-driven independent component analysis) present multiple 
methodological difficulties, especially in non-collaborative DOC patients. 
Improvements in motion artifact removal and spatial normalization are needed 
before fMRI resting state data can be used as proper biomarkers in severe brain 
injury. However, we anticipate that such developments will boost clinical 
resting state fMRI studies, allowing for easy and fast acquisitions and 
ultimately improve the diagnosis and prognosis in the absence of DOC patients' 
active collaboration in data acquisition.