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多发性硬化症表型的基于扩散的结构连接模式.

Eloy Martinez-Heras1, Elisabeth Solana1, Francesc Vivó1

  • 1Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain.

Journal of neurology, neurosurgery, and psychiatry
|June 15, 2023
PubMed
概括
此摘要是机器生成的。

多发性硬化症 (MS) 中的大脑连接性变化因临床表型而异. 二级渐进性MS显示了最广泛的破坏,机器学习可以根据这些网络变化来区分MS类型.

关键词:
多发性硬化 多发性硬化神经免疫学神经免疫学

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科学领域:

  • 神经成像是一种神经成像.
  • 神经学 神经学
  • 医学物理 医学物理

背景情况:

  • 多发性硬化症 (MS) 是一种慢性神经疾病,其特征是脱髓化和轴突损伤.
  • 了解MS中大脑网络变化的进展对于诊断和治疗至关重要.
  • 不同的多发性硬化症表现形式表现出不同程度的神经障碍和疾病轨迹.

研究的目的:

  • 测量随着疾病的进展,多发性硬化症 (MS) 中基于扩散的脑连接变化的严重程度.
  • 确定与不同MS临床表型相关的微观结构网络特征.
  • 评估机器学习在基于大脑连接的MS表型分类中的实用性.

主要方法:

  • 脑磁共振成像 (MRI) 和临床数据从8个MAGNIMS中心的823名MS患者和221名健康对照患者中收集.
  • 患者被分为四种表型:临床隔离综合征,复发性复发性,次级渐进性和初级渐进性MS.
  • 采用先进的曲谱学来生成连接矩阵,然后对图形衍生度量和分数异构 (FA) 进行分析. 支持矢量机 (SVM) 算法用于组分类.

主要成果:

  • 与对照组相比,临床隔离综合征和复发性复发性多发性硬化症患者表现出类似的网络变化.
  • 二次进展性多发性硬化症患者在全球和本地网络属性上表现出显著差异,FA的广泛减少.
  • 与早期的MS阶段相比,初级进展性MS患者表现出较少的网络变化和有限的FA减少.
  • 在将多发性硬化症患者与对照患者区分时,SVM获得了81%的准确性,在区分多发性硬化症表型时达到64-74%的准确性.

结论:

  • 在MS中,大脑连接明显受到干扰,在不同的临床表型中观察到不同的模式.
  • 二次进展性MS的特点是更广泛的连接性改变.
  • 机器学习分类模型可以有效地区分MS类型,而皮下连接是关键的区分因素.