Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia.
Queensland Brain Institute, the University of Queensland, St Lucia, Queensland, Australia.
Hum Brain Mapp. 2020 Oct 1;41(14):4062-4076. doi: 10.1002/hbm.25109. Epub 2020 Jul 20.
The recent availability of large-scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex-wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey-matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case-control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex-wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex-wise complexity; (b) comparison of the information retained by different MRI processings.
最近大规模神经影像学队列的出现，促进了人类表型和大脑结构变异之间关系的更深入特征描述。在这里，我们研究了表型与 UKB（发现组 N=9497，复制组 N=4323）和人类连接组计划（HCP；N=1110）中大规模数据集中所有 652283 个皮质和皮质下形态学顶点测量值之间的关联（先前称为形态测量学）。我们使用线性混合模型，将个体的大脑测量值拟合为随机效应，并根据成像数据估计协方差关系。我们测试了 167 种行为、认知、精神或生活方式表型，发现 58 种表型具有显著的形态测量学意义（涵盖物质使用、血液检测结果、教育或收入水平、饮食、抑郁和认知领域），其中 23 种在 UKB 复制组或 HCP 中得到复制。然后，我们将模型扩展到双变量分析，以估计表型之间的灰质相关性，这表明身体大小（即身高、体重、BMI、腰围和臀围、体脂肪百分比）可以解释形态测量学的很大一部分（使用条件分析确认），为以前与体重指数相关的精神障碍的 MRI 病例对照结果提供了可能的见解。我们的 LMM 框架还允许在两个独立的样本中从顶点测量值预测一些相关表型。最后，我们展示了我们方法的其他新应用（a）保留顶点复杂性的感兴趣区域（ROI）分析；（b）比较不同 MRI 处理保留的信息。
