Outcome Prediction of Unconscious Patients Based on Weighted Sparse Brain Network Construction

It is quite challenging to establish a prompt and reliable prognosis assessment for acquired brain injury (ABI) patients with persistent severe disorders of consciousness (DOC) like unconscious comatose and unresponsive wakefulness syndrome (a.k.a., vegetative state). Recent advances in brain functional imaging and functional net-work analysis have demonstrated its potential in determining the consciousness level and prognostic outcome for ABI patients with DOC. However, the diagnostic and prognostic usefulness of the whole-brain functional connectome based on advanced machine learning techniques has not been fully evaluated. The first aim of this study is to predict the outcome of individual unconscious ABI patients during a three-month follow-up. The second aim is to conduct precise individualized differentiation among different consciousness levels for exploring the neurobiological mechanisms underlying DOC. Based on resting-state fMRI, we construct large-scale functional networks by using a weighted sparse model, which ensures sparsity and interpretability by preserving strong functional connections. The functional connection strengths are exploited as features for outcome prediction and consciousness level differentiation. We achieve significantly improved consciousness level classification (accuracy: 84.78%) and recovery outcome prediction (accuracy: 89.74%) compared to other network construction methods. More importantly, we reveal the contributive connections across the entire brain in both tasks. These connections could serve as the potential biomarkers for better understanding of consciousness and further provide new insight into the development of diagnostic, prognostic, and effective therapeutic guidelines for ABI patients with DOC.