Structural brain connectomes assessment using portable low-field MRI at the bedside of anoxo-ischemic coma patients – CUBE

Standard predictors of outcome after cardiac arrest (CA) have substantial limitations in terms of reliability and generalizability. By providing brain structural connectivity maps, or connectomes, advanced MRI techniques, operating through high-strength magnetic field (HF; 1.5 to 3-T), have precisely revealed structural brain damages induced by CA, and have demonstrated the high sensitivity and specificity of these indicators for predicting neurological outcome after CA. However, HF MRI requires patient’s transport to dedicated hospital imaging suites, hindering the implementation of these promising neuroimaging techniques in the setting of critical illness.
Interestingly, a recent report demonstrates the capability of a proof-of-concept (POC) very low-field (VLF; 0.064-T) portable MRI to obtain neuroimaging at the bedside in critically ill patients. Nevertheless, the spatial resolution of VLF-MRI seems low and there is no available evidence about the use of VLF-MRI to extract highly needed new predictors of neurological recovery based on brain structural connectomes.
Based on previous studies from our group, we hypothesize that VLF MRI brain data carries potentially game-changing information that can be used to significantly improve neuroprognostication in this clinically challenging setting. The current proposal is a POC study which aims to compare for the first time, HF, VLF and enhanced VLF (recon-VLF) structural connectomes from anoxo-ischemic coma patients and healthy subjects across the time. To obtain recon-VLF data, we will use an ensemble of ground-breaking methods to increase the native spatial resolution of VLF-MRI data. A numerical solution will be developed to provide robust indicators of CA brain impact across the time at patient’s bedside, allow data sharing (FAIR data) and pave the way for future large scale neuroprognostication clinical studies that will combine standard predictors and VLF / recon-VLF MRI brain data.