关于GAMLSS模型的分析界限 规范性白物质的模型可变性 脑图 脑图
bioRxiv : the preprint server for biology
|February 9, 2026
概括
本研究使用分析方法和实证验证来评估脑图的稳定性. 结果显示,分析方法可能高估了模型稳定性,突出了在神经成像中对定位,尺度和形状 (GAMLSS) 的通用添加模型 (GAMLSS) 的强有力的验证方法的需求.
科学领域:
- 神经成像是一种神经成像.
- 发育神经科学的发展神经科学.
- 生物统计学 生物统计学
背景情况:
- 脑图提供了定量神经成像的规范模型,跟踪大脑发育和识别异常.
- 将脑图扩展到白质的微观结构和宏观结构特征需要对模型稳定性的差异评估.
研究的目的:
- 在现有的白质大脑图表中实施分析方法来表征参数变化.
- 通过实证验证分析模型与广泛的年龄范围 (0.2到90岁) 的启动方法对比.
主要方法:
- 用于大脑图表创建的位置,尺度和形状 (GAMLSS) 框架的一般化添加模型.
- 实施了一种分析方法来评估参数变化率 (变化系数 - COV).
- 分析COV估计与经验COV估计比较,这些估计来自启动.
主要成果:
- 分析变化系数 (COV) 在年龄大于0.25岁时保持在5%以下;经验COV在0.2岁时达到最大的7%.
- 分析和实证评估之间的高度一致 (皮尔森相关系数为0.776).
- 体积和表面积显示最大的平均COV;分析方法确定轴向扩散率具有最小的COV,而经验方法确定平均长度.
结论:
- 分析方法可能会高估白质脑图中的模型稳定性,特别是当COV低时.
- 经验验证对于评估神经成像中使用的GAMLSS模型的稳定性至关重要.
- 这两种方法都表明白质体积和表面积是最可变的特征.
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