帕金森病的诊断支持基于静止BOLD信号和DTI特征的voxel融合,使用多模式预训练
Qingchuan Zhang1, Zhenqiao Liu1, Zexi Song1
1National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, No.11 and No.33 Fucheng Road, Haidian District, Beijing 100048, China.
Journal of neuroscience methods
|December 1, 2025
概括
一个新的框架,双流斯温变压器融合框架 (DSTFP),有效地分析帕金森病 (PD) 中的大脑结构和功能. 这种方法可以更好地理解PD患者的复杂变化,而不是单一的方法.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 生物医学工程 生物医学工程
背景情况:
- 帕金森病 (PD) 呈现出大脑功能活动和白质微观结构的复杂变化.
- 单模式分析难以捕捉这些复杂的变化,需要先进的多模式方法.
研究的目的:
- 引入一个新的voxel级双流Swin变压器融合框架 (DSTFP),用于研究PD中的多式结构-功能关系.
- 为了增强对帕金森病中同时发生的功能和结构性大脑变化的理解.
主要方法:
- DSTFP使用并行变压器分支来从静止状态的功能性MRI (rs-fMRI) 提取时间动态,以及从扩散张力图像 (DTI) 分数异构图的拓特征.
- 一个交叉模式的注意力融合模块在功能和结构特征之间建立了声音相对应.
- 该框架应用于帕金森氏症进展标志物倡议 (PPMI) 数据集.
主要成果:
- DSTFP在区分PD,prodromal和对照组方面表现出高度的稳定性.
- 结构解指数 (SDI) 和结构功能合 (SFC) 分析揭示了大脑分布区域内功能结构相互作用的特征性变化.
- 与传统单模和基线多模模型相比,该框架实现了更高的分类准确性和可解释性.
结论:
- DSTFP为帕金森病的多式神经成像分析提供了一个强大的和可解释的框架.
- 这项研究提供了更深入的洞察力,了解PD中的声素水平结构-功能关系.
- 所有的源代码都是公开的,以确保可重复性,并促进进一步的研究.
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