帕金森病不同运动亚型中运动丘脑的结构和功能变化:个体研究
Shuting Bu1, Huize Pang1, Xiaolu Li1
1Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China (S.B., H.P., X.L., M.Z., J.W., Y.L., G.F.).
Academic radiology
|October 20, 2023
概括
前运动体 (mPMtha) 的功能连接性改变区分了帕金森病 (PD) 的运动亚型. 这些变化与运动性能相关,可以帮助使用机器学习对PD亚型进行分类.
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:
- 帕金森病 (PD) 呈现出多样化的运动症状,通常分为震主导 (TD) 和姿势不稳定和步行困难 (PIGD) 亚型.
- 了解这些亚型的神经支对于有针对性的治疗策略至关重要.
研究的目的:
- 在帕金森病的不同运动亚型中研究双边前运动体 (mPMtha) 的结构和功能变化.
- 探索这些变化与运动性能之间的关系.
- 评估将这些发现用于PD电机子类型的自动分类的潜力.
主要方法:
- 休息状态功能磁共振成像 (rs-fMRI) 和3D T1加权 (3DT1) 扫描从61名PIGD患者,60名TD患者和66名健康对照 (HC) 中获得.
- 在mPMtha.上进行了功能连接 (FC) 和基于voxel的形态学 (VBM) 分析.
- 支持矢量机 (SVM) 模型被用于基于FC和体积数据的分类.
主要成果:
- 与HCs和PIGD相比,TD亚型在mPMtha和左中椎和左下叶 (IPL) 之间表现出增加的FC.
- PIGD亚型显示了右mPMtha和前中心 (PreCG),上边,IPL和上顶叶之间降低的FC.
- 肌肉中心的变化与PD患者的运动性能得分有显著的相关性,而mPMtha体积在不同组之间没有差异.
- 基于FC的SVM模型实现了高分类效率 (AUC=0.874).
结论:
- 在mPMtha中显著的功能连接模式是帕金森病的TD和PIGD亚型的特征.
- 这些FC变化与运动性能缺陷有关.
- 利用FC数据的机器学习模型显示出对PD电机子类型的客观,自动化分类的希望.
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