与认知障碍并发性PTSD的机械特征暗示皮质醇诱导的神经毒性
Zeming Kuang1,2,3, Anthony G Chesebro1,2,3, Sophia-Gisela Strey1
1Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, USA.
概括
9/11救援人员显示早期认知障碍与PTSD有关. 机器学习识别了不同的大脑特征,揭示了PTSD患者认知衰退特有的神经毒性途径.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 基因组学就是基因组学.
背景情况:
- 9/11救援人员表现出过早认知障碍 (CI).
- 创伤后应激障碍 (PTSD) 是该人群中CI的主要危险因素.
- 导致CI的压力,PTSD和神经变化之间的联系机制尚不清楚.
研究的目的:
- 识别不同的大脑功能特征,区分受创伤暴露的健康对照 (TEHC),没有CI的PTSD (PTSD-CI) 和具有CI的PTSD (PTSD+CI).
- 通过机器学习和结构方程建模 (SEM) 来研究与压力相关的认知衰退背后的机械级联.
- 为了将功能性大脑特征与mRNA表达相关联,并探索特定的神经生物学途径.
主要方法:
- 功能磁共振成像 (fMRI) 用于捕获大脑特征.
- 机器学习用于研究组之间的分类 (TEHC,PTSD-CI,PTSD+CI).
- 结构方程建模 (SEM) 以推断机械路径并与mRNA表达数据相关联.
主要成果:
- 机器学习在区分PTSD+CI与TEHC (0.73) 和PTSD-CI (0.85) 中取得了很高的准确性.
- 在PTSD+CI组中,与ZNF48,TOMM40和GRIN1基因表达相关的明显的大脑特征.
- 在PTSD+CI中确定了皮质醇诱导的神经毒性途径,而p53信号在所有PTSD组中都存在.
结论:
- 功能性大脑生物标志物可以帮助识别PTSD和与PTSD相关的CI.
- SEM分析表明,PTSD和与PTSD相关的CI可能有不同的机械路径.
- 神经毒性似乎是驱动PTSD认知障碍的特定机制.
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