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Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
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根据功能连接特征预测针治疗部疼痛的疗效:机器学习研究

Zhen Gao1, Mengjie Cui2, Cheng Xu3

  • 1Shanxi University of Traditional Chinese Medicine Experimental Management Center, Taiyuan, China.

Annals of medicine
|August 23, 2025
PubMed
概括

神经成像和机器学习预测针治疗部疼痛的有效性. 这可以确定个性化治疗策略和潜在的针反应生物标志物.

关键词:
针治疗机器学习部疼痛休息状态的功能连接

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科学领域:

  • 神经科学
  • 医学成像
  • 机器学习

背景情况:

  • 部疼痛是一种普遍的疾病,有各种治疗方案.
  • 针是一种常见的部疼痛治疗方法,
  • 了解针引起的疼痛缓解机制对于优化治疗至关重要.

研究的目的:

  • 探索针引发的部疼痛缓解的机制.
  • 通过神经成像来确定预测针反应的潜在生物标志物.
  • 开发一个用于患者分层的机器学习模型.

主要方法:

  • 80名部疼痛患者接受了功能磁共振成像 (fMRI) 和临床评估.
  • 在治疗前的脑功能连接数据上训练了一个支持矢量机器 (SVM) 模型.
  • 纵向分析比较了针反应者与不反应者的功能性连接性变化.

主要成果:

  • SVM模型准确地预测了针反应率,准确度为85%.
  • 117个功能连接边缘被确定为预测生物标志物.
  • 与没有反应者相比,受试者在治疗后的预测特征中表现出更有针对性的变化.

结论:

  • 治疗前的神经成像功能可以预测针治疗部疼痛的有效性.
  • 这种方法有助于个性化针策略.
  • 它有助于识别适合针的候选人,并指导不响应治疗的替代治疗方法.