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多模式神经成像特征预测多发性硬化症的认知衰退:5年纵向研究

Oun Al-Iedani¹, Stasson Lea², A Alshehri³

¹School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia.

Multiple sclerosis and related disorders

|December 16, 2023

概括

多模态MRI准确地预测了MS (pwMS) 患者在五年内认知衰退的情况. 这种方法可以识别个性化管理的预测特征,即使没有复发.

科学领域:

神经成像是一种神经成像.
神经学神经学
机器学习机器学习

背景情况:

认知障碍是多发性硬化症 (MS) 的常见但未被记录的症状.
预测MS (pwMS) 患者的认知衰退对于有效的治疗策略至关重要.
现有研究通常使用单个MRI模式或横截面设计.

研究的目的:

评估多模磁共振 (MR) 成像在五年内对pwMS认知衰退的预测价值.
为了识别预测认知衰退的多模态MRI签名.
为了比较多模态MRI与传统体积测量的预测能力.

主要方法:

在基线和5年随访时评估了43名pwMS队列.
使用了70个多模态MRI测量 (MRS,DTI,体积测量) 和临床数据 (年龄,性别,疾病持续时间,治疗).
采用机器学习 (GLMnet惩罚回归) 来识别认知结果的预测特征 (ARCS,SDMT).

主要成果:

多模态MRI方法准确地预测了pwMS的认知衰退.
最好的ARCS变化模型包括16个特征 (MRS,体积,DTI) 并解释了54%的变化.
最好的SDMT变化模型解释了39%的变化;多模态MRI表现优于传统体积测量 (R2=0.26).

结论:

多模态MRI签名可以在5年内预测pwMS的认知衰退,准确度高 (>90%AUC).
未来的研究应该纳入额外的MR模式和其他预测因素 (遗传,环境).
验证的签名可以在临床试验中个性化MS认知衰退管理.

关键词:

扩散张力成像的成像方法多模态磁共振成像多模态磁共振成像多发性硬化症是多发性硬化症.认知能力下降认知能力下降机器学习是机器学习.

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