急性神经缺陷的损伤网络映射及其在缺血性中风后的预后价值
Aslı Akdeniz1, Ana Sofía Ríos2, Uchralt Temuulen3
1Max Planck Institute for Human Cognitive and Brain Sciences, Max Planck School of Cognition, Leipzig, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), 12203 Berlin, Germany.
NeuroImage. Clinical
|November 2, 2025
概括
预测中风恢复非常重要. 损伤网络映射 (LNM) 模拟大脑网络,但在本研究中,除了临床因素之外,没有改善功能性结果预测.
科学领域:
- 神经科学是一个神经科学.
- 神经学 神经学
- 医疗成像医学成像
背景情况:
- 预测缺血性中风后的功能恢复对于指导临床护理至关重要.
- 损伤网络映射 (LNM) 是一种用于建模特定症状的大脑网络的技术.
研究的目的:
- 调查病变网络映射 (LNM) 是否可以改善对中风后长达一年的功能恢复的预测.
主要方法:
- 汇集了来自两个潜在中风队列的565名患者的数据.
- 根据入院NIHSS子得分,使用LNM生成了七个NIHSS衍生症状网络.
- 通过将病变面具与症状网络交叉来计算单个网络损伤得分.
- 使用顺序逻辑回归来比较带有和没有LNM衍生得分的预测模型.
主要成果:
- LNM揭示了明显的,特定于症状的网络,在相应缺陷的患者中损伤得分更高.
- 包括LNM衍生网络损伤得分并没有提高与单独临床变量相比,功能结果的预测准确性.
- 模型比较显示预测能力没有显著改善 (伪R2:0.0468对比0.0159;AIC:1730.598对比1769.222).
结论:
- 来自LNM的得分反映了症状的地形,但不能改善功能恢复的预测.
- 目前,基于LNM的损伤指标对预后的临床实用性有限.
- 需要进一步验证,以确定LNM在临床中风预测结果中的作用.
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