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三重纵向面具自编码器用于预测婴儿时期个性化的功能连接体发展.

Weiran Xia1, Xin Zhang2, Dan Hu3

  • 1Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Lampe Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA; School of Future Technology, South China University of Technology, Guangzhou, Guangdong, China.

Medical image analysis
|November 22, 2025
PubMed
概括

由于缺少数据,预测婴儿大脑功能连接 (FC) 发展具有挑战性. 我们的新三重长度面具自编码器 (TL-MAE) 方法准确预测动态FC轨迹,改善对神经发育的理解.

关键词:
功能连接性的功能连接性.婴儿婴儿婴儿婴儿婴儿婴儿纵向轨迹预测的方法

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科学领域:

  • 神经科学是一个神经科学.
  • 发育神经科学的发展神经科学.
  • 医疗成像医学成像

背景情况:

  • 休息状态功能性MRI (rs-fMRI) 是婴儿大脑功能连接 (FC) 研究的关键.
  • 从不完整的纵向数据中预测婴儿FC轨迹对于理解大脑发育和识别疾病至关重要.
  • 目前的深度学习方法在婴儿FC预测中扎着时间不一致和缺失数据.

研究的目的:

  • 开发一种新的方法来准确预测婴儿大脑功能连接的完整动态发育轨迹.
  • 为应对婴儿神经成像中稀缺的纵向数据和不规则的缺失扫描所带来的挑战.
  • 提高婴儿期FC预测的时间一致性和准确性.

主要方法:

  • 提出了三重长度掩盖自编码器 (TL-MAE) 用于动态婴儿FC轨迹预测.
  • 实施了对强大的FC发电进行纵向一致的预测策略.
  • 使用了一种特定于FC的蒙面自动编码器,该自动编码器在大型数据集上进行了预训练.
  • 开发了一种带有身份条件模块的双三联网络,用于个性化基于年龄的预测.

主要成果:

  • 该TL-MAE方法证明了更准确和时间一致的FC发展轨迹的预测.
  • 该模型成功地捕获了婴儿大脑发育中的个性化特征.
  • 696个婴儿纵向fMRI扫描的实验结果验证了该方法在最先进技术上的优越性.

结论:

  • TL-MAE在预测动态婴儿大脑功能连接方面取得了重大进展.
  • 这种方法增强了对正常和异常神经发育轨迹的理解.
  • TL-MAE提供了一个强大的工具,可以从rs-fMRI数据中对婴儿大脑发育的个性化预测.