Harvard Medical School, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, USA.
Cortex. 2022 Jul;152:136-152. doi: 10.1016/j.cortex.2022.04.007. Epub 2022 Apr 15.
Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: 'A' = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); 'B' = theta (4-8 Hz) peak (severe thalamocortical disruption); 'C' = theta and beta (13-24 Hz) peaks (moderate thalamocortical disruption); or 'D' = alpha (8-13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated 'D' signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.
用于检测调节意识的神经网络的工具可能有助于追踪急性重度脑损伤后的恢复情况。ABCD 框架将静息状态脑电图分为反映丘脑皮质网络功能水平的类别，这些水平与心搏骤停后昏迷的结果相关。在这项纵向队列研究中，我们将 ABCD 框架应用于 20 名需要重症监护的急性重度创伤性脑损伤患者（其中 12 名也在损伤后至少 6 个月进行了研究）和 16 名健康对照者。我们检验了四个假设：1）脑电图 ABCD 分类在空间上不均匀，在时间上变化；2）ABCD 分类随着行为恢复程度的提高而纵向改善；3）ABCD 分类与行为意识水平相关；4）昏迷恢复量表修订版觉醒促进方案可改善 ABCD 分类。基于预先定义频带内的频谱峰值，对通道级 EEG 功率谱进行分类：“A”=无超过 delta（<4Hz）范围的峰（完全丘脑皮质破坏）；“B”=theta（4-8Hz）峰（严重丘脑皮质破坏）；“C”=theta 和 beta（13-24Hz）峰（中度丘脑皮质破坏）；或“D”=alpha（8-13Hz）和 beta 峰（正常丘脑皮质功能）。急性发作时，95%的患者在至少一个通道中显示“D”信号，但在同一时间段内，不同通道级 ABCD 分类的比例存在时间变化和空间异质性。相比之下，健康参与者和随访患者始终显示出对应于完整丘脑皮质网络功能的信号。患者的 ABCD 分类得到了改善（p<.05），并且 ABCD 分类在亚急性到慢性恢复阶段区分了有和无指令跟随的患者（p<.01）。在急性发作时接受研究的患者中，ABCD 分类在昏迷恢复量表修订版觉醒促进方案后得到改善（p<.05），但与行为意识水平无关。这些发现支持使用 ABCD 框架来描述通道级 EEG 动态，并以空间细节跟踪功能丘脑皮质网络完整性的波动。
