Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, USA.
Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, USA.
Brain Inj. 2021 Feb 23;35(3):299-303. doi: 10.1080/02699052.2020.1861480. Epub 2021 Feb 2.
: Electrocorticographic (ECoG) measurement of spreading depolarization (SD) has led to significant advances in understanding of injury progression in neuro ICU patients. However, SD can be difficult to recognize in ECoG regions with high artifact. Heuristics for ECoG analysis within these regions would be highly valuable.: Patients requiring craniotomy following subarachnoid hemorrhage, malignant hemispheric stroke, or traumatic brain injury were enrolled in this study. ECoG leads were placed intraoperatively and scoring of SDs was completed twice; once using traditional criteria and again with the intention of finding SD patterns. Utilizing covariance structures, graphical overlay and various measures surrounding DC shift, SDs were evaluated for patterns.: SD patterns were consistently observed and were unique to each patient and lead placement. No more than five different patterns were noted for any given patient, and statistical analysis utilizing covariance structures revealed high intra-pattern consistency.: This validation of internal patient specific patterns offers more insight into ECoG readings of high artifact regions. This, in addition to traditional SD scoring heuristics, offers another scoring tool for the neuro-ICU care of patient experiencing SD. Furthermore, description of neurologic disease by its SD patterns may offer a new direction for precision medicine.
电皮质描记术 (ECoG) 测量扩散性去极化 (SD) ,使我们对神经 ICU 患者的损伤进展有了更深入的了解。然而,在高伪影的 ECoG 区域,SD 可能难以识别。在这些区域内进行 ECoG 分析的启发式方法将具有很高的价值。
本研究纳入了因蛛网膜下腔出血、恶性大脑半球卒中或创伤性脑损伤而需要开颅手术的患者。在手术期间放置 ECoG 导联,并进行两次 SD 评分;一次使用传统标准,另一次旨在寻找 SD 模式。利用协方差结构、图形叠加和围绕 DC 偏移的各种测量方法,评估了 SD 模式。
SD 模式始终存在,且每个患者和导联放置都具有独特性。对于任何给定的患者,最多只有五种不同的模式,并且利用协方差结构进行的统计分析显示出高内模式一致性。
这种对患者特定内部模式的验证为高伪影区域的 ECoG 读数提供了更多的见解。除了传统的 SD 评分启发式方法外,这为经历 SD 的神经 ICU 患者的护理提供了另一种评分工具。此外,通过 SD 模式描述神经疾病可能为精准医疗提供一个新的方向。