一个代表自闭症个体在自闭症谱系表现型异质性的规范模型
Joana Portolese1,2, Catarina Santos Gomes1,2, Vinicius Daguano Gastaldi1
1Laboratório de Psicopatologia e Terapêutica Psiquiátrica (LIM23), Faculdade de Medicina FMUSP, Universidade de São Paulo, Sao Paulo 05403-010, SP, Brazil.
Brain sciences
|January 8, 2025
概括
一个新的多维通用功能评分 (MGFS) 使用表型数据的主要组件分析量化自闭症谱异质性. 这一分数有助于比较自闭症患者和评估临床发展轨迹.
科学领域:
- 神经发育障碍 神经发育障碍
- 量化心理学 量化心理学
- 生物统计学 生物统计学
背景情况:
- 自闭症谱系诊断依赖于社会沟通和受限制的行为,但需要额外的修饰者,如智商和并发症,以进行全面的评估.
- 目前用于识别自闭症子组的现有方法在评估个人与规范样本相比方面存在局限性.
- 基于表型变异性的定量得分是个人比较,子组同质性评估和轨迹跟踪所需的.
研究的目的:
- 开发一种定量方法来评估自闭症谱异质性.
- 将表型变异的测量与标准建模结合起来,用于个人评估.
- 创建一个工具来比较自闭症个体和评估临床轨迹.
主要方法:
- 主要组件分析 (PCA) 用于来自西蒙斯简单集合 (n=2744) 的表型数据.
- 用ADI-R,VABS和CBCL评估了导致临床异质性的关键特征.
- 总智商 (IQ) 用于认知评估.
主要成果:
- 三个主要组成部分 (PC) 解释了72%的规范样本差异.
- 用PCA预测的尺寸促进了使用多变量正常分布计算百分位数的规范建模.
- 基于这些百分值,开发了一个多维通用功能评分 (MGFS),用于评估个人.
结论:
- 拟议的方法为比较个人随着时间的推移提供了基础.
- 它可以评估表型临床表现的一致性.
- 这种方法有可能指导临床试验的研究样本选择.
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