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基于机器学习的识别儿童间歇性外otropia使用多个静止状态的功能磁共振成像功能.

Mengdi Zhou1, Huixin Li2, Xiaoxia Qu1

  • 1Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.

Brain and behavior
|May 13, 2025
PubMed
概括

使用静止状态fMRI数据的机器学习模型有效地区分了间歇性外变性 (IXT) 的儿童和健康的对照. 低频波动 (fALFF) 的慢-5分幅参数显示为IXT的生物标志物具有前途.

关键词:
间歇性的外热性.机器学习是机器学习.静止状态功能磁共振成像技术自发性活动是一种自发性的活动.

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科学领域:

  • 神经成像是一种神经成像.
  • 机器学习 机器学习
  • 眼科医生 眼科 眼科

背景情况:

  • 间歇性外 (IXT) 是一个常见的儿童眼睛 misalignment.
  • 了解IXT的神经生物学基础对于诊断和治疗至关重要.
  • 休息状态功能磁共振成像 (rs-fMRI) 提供了对大脑功能的洞察.

研究的目的:

  • 评估机器学习 (ML) 模型的有效性,使用rs-fMRI参数来区分患有IXT的儿童与健康的对照 (HCs).
  • 确定特定的rs-fMRI参数和大脑区域,作为IXT的潜在生物标志物.

主要方法:

  • 分析了41名IXT儿童和36名HC的rs-fMRI数据.
  • 计算的关键参数包括低频波动的幅度 (ALFF),慢-4/慢-5频段的分数ALFF (fALFF) 和区域均性 (ReHo).
  • 机器学习分类器和特征选择方法被使用,用于性能评估的十倍交叉验证.

主要成果:

  • ML模型在区分IXT和HC方面表现良好.
  • 慢-5 fALFF参数产生了最好的分类结果.
  • 使用ANOVA特征选择的线性回归分类器实现了高精度 (0.957训练,0.804验证,0.818测试AUC) 使用五个关键大脑区域,包括右下侧状回环和左背侧前额叶皮质.

结论:

  • 使用特定皮质区域的慢-5fALFF值的线性回归模型有效地将IXT儿童与HC区分开来.
  • 慢-5 fALFF显示了作为IXT的神经成像生物标志物的潜力.
  • 参与立体,眼动和认知功能的大脑区域与IXT的病理生理学有关.