语言和执行功能的脑表型预测可以在各种现实数据中存活下来:数据集在发育期人口中的转移
Brendan D Adkinson1, Matthew Rosenblatt2, Javid Dadashkarimi3
1Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA.
Developmental cognitive neuroscience
|October 24, 2024
概括
对大脑行为联系的预测模型显示出可复制性的承诺. 不仅仅是在一个数据集内,还可以在不同的数据集上进行培训,可以改善不同人群和环境中的预测.
科学领域:
- 神经科学是一个神经科学.
- 数据科学数据科学数据科学
- 精神病学是一个精神病学.
背景情况:
- 神经成像脑表型关联的可复制性和通用性对于临床应用至关重要.
- 预测模型的外部验证未得到充分利用,尤其是在多样化,不协调的数据集中.
- 研究环境往往缺乏在现实世界应用中遇到的数据集特定变化.
研究的目的:
- 严格测试预测模型在三个多样化,不协调的发育神经成像数据集中的通用性.
- 评估数据集间异质性对大脑行为预测模型性能的影响.
- 探索基于功能连接体的预测模型在现实世界的场景中的潜力.
主要方法:
- 利用了三个不同的发育队列:费城神经发育队列 (n=1291),健康大脑网络 (n=1110) 和人类结合体在发育中的项目 (n=428).
- 在人口统计,临床因素和神经成像采集参数显著异质的数据集中评估模型概括.
- 采用先进的方法方法来评估可复制和可概括的大脑行为关联.
主要成果:
- 证明尽管数据集之间存在大量的变化,但可以实现可复制和可概括的大脑行为关联.
- 基于功能连接组的预测模型在各种数据集特征中显示出稳定性.
- 对于HCPD和HBN数据集,交叉数据集预测的性能优于数据集内交叉验证,这表明各种培训数据的好处.
结论:
- 即使具有高的数据集间异质性,也可以实现可复制和可概括的脑表型关联.
- 基于功能连接体的预测模型有可能在现实世界的临床环境中提供强大的性能.
- 在不同的数据集上训练预测模型可以在特定情况下提高预测准确性,挑战传统的交叉验证方法.
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