Recent personality neuroscience research in large samples suggests that personality traits tend to bear null-to-small relations to morphometric (i.e., brain structure) regions of interest (ROIs). In this preregistered, two-part study using Human Connectome Project data (N = 1,105), we address the possibility that these null-to-small relations are due, in part, to the "level" (i.e., hierarchical placement) of personality and/or morphometry examined. We used a Five-Factor Model framework and operationalized personality in terms of meta-traits, domains, facets, and items; we operationalized morphometry in terms of omnibus measures (e.g., total brain volume), and cortical thickness and area in the ROIs of the Desikan and Destrieux atlases. First, we compared the patterns of effect sizes observed between these levels using mixed effects modeling. Second, we used a machine learning framework for estimating out-of-sample predictability. Results highlight that personality-morphometry relations are generally null-to-small no matter how they are operationalized. Relatively, the largest mean effect sizes were observed at the domain level of personality, but the largest individual effect sizes were observed at the facet and item level, particularly for the Ideas facet of Openness and its constituent items. The largest effect sizes observed were at the omnibus level of morphometry, and predictive models containing only omnibus variables were comparably predictive to models including both omnibus variable and ROIs. We conclude by encouraging researchers to search across levels of analysis when investigating relations between personality and morphometry and consider prioritizing omnibus measures, which appear to yield the largest and most consistent effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

中文翻译：

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在高低中寻找有意义且可复制的人格形态测量相关性。

最近在大样本中进行的人格神经科学研究表明，人格特征往往与形态测量（即大脑结构）感兴趣区域（ROI）具有零到小关系。在这项使用 Human Connectome Project 数据 (N = 1,105) 的预先注册的两部分研究中，我们解决了这些零到小关系部分归因于人格的“级别”（即分层放置）的可能性和/或形态测量检查。我们在元特征、领域、方面和项目方面使用了五因素模型框架和可操作的个性；我们根据综合测量（例如，总脑容量）以及 Desikan 和 Desrieux 地图集的 ROI 中的皮质厚度和面积来操作形态测量。第一的，我们使用混合效应模型比较了在这些水平之间观察到的效应大小模式。其次，我们使用机器学习框架来估计样本外的可预测性。结果强调，无论如何操作，人格-形态测量关系通常都是零到小。相对地，最大的平均效应量在人格的领域层面被观察到，但最大的个体效应量在方面和项目层面被观察到，特别是对于开放性的想法方面及其组成项目。观察到的最大效应量处于形态测量的综合水平，并且仅包含综合变量的预测模型与包括综合变量和 ROI 的模型具有相当的预测性。最后，我们鼓励研究人员在调查人格和形态测量之间的关系时搜索跨层次的分析，并考虑优先考虑综合措施，这似乎产生最大和最一致的影响。（PsycInfo 数据库记录 (c) 2022 APA，保留所有权利）。