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临床,基于病变和基于连接组的中风后抑郁症模型的比较:一项前性的纵向研究

Nicolas Borderies¹, Suhrit Duttagupta², Thomas Tourdias³

¹Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives 5293, Centre National de la Recherche Scientifique (CNRS), University of Bordeaux, Bordeaux 33076, France.

NeuroImage. Clinical

|November 21, 2025

概括

临床因素最好预测轻度缺血性中风后的中风后抑郁症 (PSD). 将这些与脑成像数据相结合,可显著提高个性化干预措施的预测准确性.

科学领域:

神经科学是一个神经科学.
神经学神经学
精神病学是一个精神病学.

背景情况:

脑卒中后抑郁症 (PSD) 是继缺血性脑卒中后的一个常见并发症.
尽管已经确立了临床风险因素,PSD中神经成像的预测价值仍然不清楚.

研究的目的:

对比临床,基于病变和基于网络的模型对3个月PSD的预测性能.
评估神经成像在轻度缺血性中风患者PSD预测中的附加值.

主要方法:

对263名轻度缺血性中风患者进行了前性纵向研究.
收集的临床,放射学 (MRI) 和心理测量数据.
使用LASSO回归开发了六种预测模型 (临床,放射,灰色/白色物质,功能网络,拓特征).

主要成果:

临床模型显示最佳个体预测 (R2=23%),确定女性性别,认知能力低,结果差以及社会经济地位.
仅仅成像模型的预测能力有限 (R2<6%),但突出显示了额头/小脑病变和网络变化.
综合临床和成像特征的等级模型显著改善了预测 (R2=31.6%,p<0.001).

结论:

临床因素是小缺血性中风中PSD的主要预测因素.
将神经成像 (损伤和网络) 与临床数据相结合,可以提高PSD预测的准确性.
支持用于PSD风险分层和早期干预的多式联运,个性化模型.

关键词:

在Connectome中使用Connectome.抑郁症抑郁症抑郁症图形理论 - 图形理论网络 - 症状映射映射预测模型是一个预测模型.一次性中风,中风.

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