在自闭症,注意力缺陷/多动症和强迫症中,大脑形态的集群结构中的可复制性
Younes Sadat-Nejad1,2, Marlee M Vandewouw1,3, Jessica Brian1
1Holland Bloorview Kids Rehabilitation Hospital, 150 Kilgour Rd, Toronto, ON, M4G1R8, Canada.
Translational psychiatry
|August 30, 2025
概括
神经多样性的儿童大脑结构可以在独立的数据集中复制. 然而,这些数据驱动的集群与诊断标签不一致,强调需要谨慎解释.
科学领域:
- 神经科学
- 发展心理学
- 生物统计学
背景情况:
- 神经发育障碍在诊断中表现出显著的异质性,并且在诊断中重叠.
- 传统的病例对照设计可能无法完全捕捉神经发育状况的复杂性.
- 数据驱动的集群方法为理解神经发育异质提供了一个有希望的替代方案.
研究的目的:
- 在两个独立的数据集中调查神经多样性儿童大脑形态聚类结构的可复制性.
- 评估不同大脑测量和人口学子集 (例如男性/女性) 之间的聚类结构的对齐.
- 通过集群方法检查来自大脑的行为关联的一致性.
主要方法:
- 使用了来自安大略省神经发育障碍 (POND) 网络和健康大脑网络 (HBN) 的结构磁共振成像 (MRI) 数据.
- 应用主要成分分析 (PCA) 和聚类技术来测量皮质体积,表面积,皮质厚度和皮质下体积.
- 检查主要组件之间的相关性,集群性和大脑测量,子集和数据集之间的对齐性.
主要成果:
- 通过对不同数据集进行的大脑测量,发现主要组件之间存在显著的相关性 (82. 1%).
- 在数据集,大脑测量和性别子集中复制了一致的两组结构.
- 在不同的大脑测量中,群组组合的调整有所不同;群组间差异的区域效应大小高度相关 (R2=0. 93).
- 基于数据的集群与已知的诊断标签不一致.
- 大脑与行为之间的关联仅在男性子组和皮质下体积中复制.
结论:
- 证据支持独立队列中神经多样性的儿童大脑形态中的数据驱动集群结构的可复制性.
- 这些发现强调了聚类方法的潜力,但强调了聚类与诊断类别的局限性.
- 在整合多个大脑测量以集群和解释相关的大脑行为关系时,建议谨慎.
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