Disorders of consciousness (DoC) are states of altered consciousness stemming from an acquired injury to brain networks regulating arousal and awareness, characterized by a loss of excitatory neural activity across corticocortical, thalamocortical and thalamostriatal connections. DoC may be due to several causes including traumatic brain injury, anoxic brain injury from cardiac arrest, stroke, severe brain infections, prolonged seizures, or others. A primary challenge in the clinical management of DoC is accurately determining the type of DoC and level of consciousness, as well as the potential for recovery. This is crucial for decision-making and clinical management, with misdiagnosis rates as high as 40% due to limitations of clinical evaluation. Recent research has demonstrated that consciousness not detectable at bedside evaluation may be present in as many as 15-20% patients with DoC. Thus, to improve the clinical assessment of DoC and ultimately develop targeted interventions to promote neurological recovery, better methods of assessing DoC in individual patients must be developed, validated, and translated to clinical use. The use of rs-fMRI in the study of DoC has led to important insights into the biological mechanisms of coma as well as recovery of consciousness, yet the translation of rs-fMRI to the clinical management of individual patients with impaired consciousness has been hampered by several factors. First, BOLD fMRI reflects a complex combination of signals derived not only from neural activity but also from non-neurological sources that ultimately comprise as much as 25% of the BOLD signal obscuring true neural connectivity and introducing spurious connectivity. This limitation has usually been circumvented by analyzing fMRI at the group level, leveraging the statistical benefits of averaging data across individuals. Better strategies to distinguish between neural and non-neural contributors to the BOLD signal are needed to translate rs-fMRI to clinical use in individual patients. Additionally, conventional acquisition and analysis strategies have generally been shown to produce reliable estimates of functional connectivity only at clinically intractable scan times. Recent evidence suggests that highly reliable measures of functional connectivity can be obtained at the individual level at clinically feasible scan times using more advanced imaging protocols, outperforming even several-fold longer conventional acquisitions. The measured BOLD signal can be represented as a function of the signal intensity at excitation (S0), the echo time, and signal decay constant. Neural activity affects the BOLD signal by inducing an increase in blood-flow, which reduces the local concentration of deoxyhemoglobin and in turn decreases the local magnetic susceptibility thereby reducing the rate of transverse magnetization decay. By examining the signal decay across multiple echo times, changes in T2\* can be distinguished from changes in S0 thereby allowing neural and non-neural signals from BOLD data to be differentiated. With sensitivity to BOLD signal being maximal near the T2\*, multi-echo acquisitions can also enhance BOLD sensitivity by placing greater weight on those echoes closest to the T2\* of a particular voxel ("optimally combining"), which is known to vary across the brain. Lastly, the process of combining BOLD data across echoes carries the advantage of dampening thermal noise which is randomly organized and tend to cancel out upon averaging. When combined with additional strategies to remove global motion-related signals, the influence of motion on BOLD data can be nearly eliminated. Only recently with the integration of other technical advances such as simultaneous multi-slice has it become possible to acquire multi-echo rs-fMRI without major compromises in spatial or temporal resolution acquisition. Practical challenges to obtaining advanced MRI in critically ill patients include the need to transport patients to research MRI instrumentation capable of the latest acquisition methods that have traditionally been located on different floors or even different buildings than intensive care units (ICUs). As a result, advanced MRI methods such as multi-echo rs-fMRI have mainly been applied to healthy subjects. This project will take advantage of a newly installed research-dedicated state-of-the-art 3 Tesla MRI system equipped with fast gradients a 32-channel receiver array embedded within the neurointensive care unit (NICU). This system is capable of multi-echo rs-fMRI at high spatiotemporal resolution and a key goal of this project is to determine whether functional network integrity in patients with DoC can be more reliably detected using multi-echo rs-fMRI than with standard rs-fMRI. Optimal methods would be unsupervised, capable of accommodating interindividual differences in functional topology, and capable of reducing complex connectivity data to discrete, clinically actionable insights. Another key goal of this project is to compare three approaches to connectome construction and characterization in their reliability at the individual patient level and degree of association with clinical measures of neurologic dysfunction and outcome. The first and most traditional approach will rely upon transformation of each data to a template space and node definition based upon group-level meta- analysis. The second approach will use a previously-described method of subject-level parcellation based upon connectivity gradients and ultimately compared across individuals based upon the degree of similarity to template networks. The final approach will use a radiological interpretation scheme in which expert neuroimagers trained to recognize typical spatial patterns of resting state networks will render an interpretation of the degree network maps obtained from independent component analysis.