Neuroimaging of individual differences: A latent variable modeling perspective
Neuroimaging of individual differences: A latent variable modeling perspective
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DOI：
10.1016/j.neubiorev.2018.12.022
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发表时间：
2019-03-01
影响因子：
8.2
通讯作者：
Braver, Todd S.
中科院分区：
文献类型：
--
作者：
Cooper, Shelly R.;Jackson, Joshua J.;Braver, Todd S.
神经成像数据越来越多地被用来解决个体差异的问题。当用任务相关功能磁共振成像(t-fMRI)检查时，个体差异通常通过每个体素上的大胆激活信号与特定行为测量之间的相关性来调查。这可能是有问题的，因为：1)相关设计需要评估t-fMRI的心理测量学特性，但这些特性还没有得到很好的理解；2)在对大脑-行为关系的复杂性进行建模时，双变量相关性受到严重限制。心理测量学理论中的分析工具，如潜变量建模(例如，结构方程建模)，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学理论和方法与认知神经科学相结合来评估和解释个体差异所获得的优势。第一部分介绍了古典和现代心理测量学理论和分析的背景。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学的局限性。最后一节使用人类连接组项目的数据来提供例证，说明t-fMRI个体差异研究如何利用潜变量模型受益。
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 modem 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.