Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France.
Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France.
Crit Care Med. 2020 Aug;48(8):e639-e647. doi: 10.1097/CCM.0000000000004406.
Recovery from coma might critically depend on the structural and functional integrity of frontoparietal networks. We aimed to measure this integrity in traumatic brain injury and anoxo-ischemic (cardiac arrest) coma patients by using an original multimodal MRI protocol.
Prospective cohort study.
Three Intensive Critical Care Units affiliated to the University in Toulouse (France).
We longitudinally recruited 43 coma patients (Glasgow Coma Scale at the admission < 8; 29 cardiac arrest and 14 traumatic brain injury) and 34 age-matched healthy volunteers. Exclusion criteria were disorders of consciousness lasting more than 30 days and focal brain damage within the explored brain regions. Patient assessments were conducted at least 2 days (5 ± 2 d) after complete withdrawal of sedation. All patients were followed up (Coma Recovery Scale-Revised) 3 months after acute brain injury.
None.
Functional and structural MRI data were recorded, and the analysis was targeted on the posteromedial cortex, the medial prefrontal cortex, and the cingulum. Univariate analyses and machine learning techniques were used to assess diagnostic and predictive values. Coma patients displayed significantly lower medial prefrontal cortex-posteromedial cortex functional connectivity (area under the curve, 0.94; 95% CI, 0.93-0.95). Cardiac arrest patients showed specific structural disturbances within posteromedial cortex. Significant cingulum architectural disturbances were observed in traumatic brain injury patients. The machine learning medial prefrontal cortex-posteromedial cortex multimodal classifier had a significant predictive value (area under the curve, 0.96; 95% CI, 0.95-0.97), best combination of subregions that discriminates a binary outcome based on Coma Recovery Scale-Revised).
This exploratory study suggests that frontoparietal functional disconnections are specifically observed in coma and their structural counterpart provides information about brain injury mechanisms. Multimodal MRI biomarkers of frontoparietal disconnection predict 3-month outcome in our sample. These findings suggest that fronto-parietal disconnection might be particularly relevant for coma outcome prediction and could inspire innovative precision medicine approaches.
从昏迷中恢复可能严重依赖于额顶网络的结构和功能完整性。我们旨在通过使用原始的多模态 MRI 方案来测量创伤性脑损伤和缺氧缺血（心脏骤停）昏迷患者的这种完整性。
前瞻性队列研究。
法国图卢兹大学附属的三个重症监护病房。
我们纵向招募了 43 名昏迷患者（入院时格拉斯哥昏迷量表评分<8；29 例心脏骤停和 14 例创伤性脑损伤）和 34 名年龄匹配的健康志愿者。排除标准为意识障碍持续超过 30 天和在探索的脑区有局灶性脑损伤。患者评估在镇静完全停药后至少 2 天（5±2 天）进行。所有患者在急性脑损伤后 3 个月进行了随访（修订后的昏迷恢复量表）。
无。
记录了功能和结构 MRI 数据，分析针对后内侧皮质、内侧前额叶皮质和扣带。使用单变量分析和机器学习技术评估诊断和预测价值。昏迷患者的内侧前额叶皮质-后内侧皮质功能连接明显降低（曲线下面积，0.94；95%CI，0.93-0.95）。心脏骤停患者在后内侧皮质显示出特定的结构紊乱。创伤性脑损伤患者观察到扣带结构明显改变。内侧前额叶皮质-后内侧皮质多模态分类器的机器学习具有显著的预测价值（曲线下面积，0.96；95%CI，0.95-0.97），是基于昏迷恢复量表评分的最佳组合）。
这项探索性研究表明，额顶叶功能连接中断在昏迷中特异性出现，其结构对应物提供了关于脑损伤机制的信息。额顶叶分离的多模态 MRI 生物标志物可预测我们样本中 3 个月的结果。这些发现表明，额顶叶分离可能与昏迷结果预测特别相关，并可能激发创新的精准医疗方法。
