Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

中文翻译：

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个体差异的神经成像：潜在的变量建模观点。

神经影像数据正越来越多地用于解决个体差异的问题。当使用与任务相关的功能磁共振成像（t-fMRI）检查时，通常通过每个体素处的BOLD激活信号与特定行为量度之间的相关性来研究个体差异。这可能是有问题的，因为：1）相关设计需要评估t-fMRI的心理测量特性，但这些特性尚未得到很好的理解；2）在模拟大脑行为关系的复杂性时，双变量相关性受到严格限制。来自心理计量学理论的分析工具，例如潜变量模型（例如，结构方程模型）可以帮助同时解决这两个问题。这篇综述探讨了将心理测量理论和方法与认知神经科学相结合所获得的优势，这些优势用于评估和解释个体差异。第一部分提供了经典和现代心理计量学理论和分析的背景知识。第二部分详细介绍了当前t-fMRI个体差异分析的方法及其心理测量的局限性。最后一部分使用来自人类连接基因组计划的数据来提供说明性示例，说明如何利用潜变量模型使t-fMRI个体差异研究受益。