生命银行
神经影像学
大脑大小
协变量
阿尔茨海默病神经影像学倡议
联想(心理学)
荟萃分析
临床研究设计
认知
样本量测定
研究设计
纵向研究
心理学
认知心理学
计算机科学
医学
统计
认知障碍
神经科学
生物信息学
机器学习
生物
临床试验
数学
磁共振成像
内科学
病理
心理治疗师
放射科
作者
Kaidi Kang,Jakob Seidlitz,Richard A. I. Bethlehem,Jiangmei Xiong,Megan T. Jones,Kahini Mehta,Arielle S. Keller,Ran Tao,Anita Randolph,Bart Larsen,Brenden Tervo‐Clemmens,Eric Feczko,Óscar Miranda-Domínguez,Scott M. Nelson,Aaron Alexander‐Bloch,Damien A. Fair,Jonathan S. Schildcrout,Damien A. Fair,Theodore D. Satterthwaite,Aaron Alexander‐Bloch,Simon Vandekar
出处
期刊:Nature
[Nature Portfolio]
日期:2024-11-27
标识
DOI:10.1038/s41586-024-08260-9
摘要
Abstract Brain-wide association studies (BWAS) are a fundamental tool in discovering brain–behaviour associations 1,2 . Several recent studies have shown that thousands of study participants are required for good replicability of BWAS 1–3 . Here we performed analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional MRI studies from the Lifespan Brain Chart Consortium 4 (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized effect sizes and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability of the covariate and longitudinal studies have larger reported standardized effect size. Analysing age effects on global and regional brain measures from the UK Biobank and the Alzheimer’s Disease Neuroimaging Initiative, we showed that modifying study design through sampling schemes improves standardized effect sizes and replicability. To ensure that our results are generalizable, we further evaluated the longitudinal sampling schemes on cognitive, psychopathology and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrated that commonly used longitudinal models, which assume equal between-subject and within-subject changes can, counterintuitively, reduce standardized effect sizes and replicability. Explicitly modelling the between-subject and within-subject effects avoids conflating them and enables optimizing the standardized effect sizes for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.
科研通智能强力驱动
Strongly Powered by AbleSci AI