Neurology and Imaging of Cognition (LabNIC), Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland.
Swiss Center for Affective Science, Campus Biotech, Geneva, Switzerland.
Nat Hum Behav. 2019 Sep;3(9):897-905. doi: 10.1038/s41562-019-0681-8. Epub 2019 Aug 26.
Explaining and predicting individual behavioural differences induced by clinical and social factors constitutes one of the most promising applications of neuroimaging. In this Perspective, we discuss the theoretical and statistical foundations of the analyses of inter-individual differences in task-related functional neuroimaging. Leveraging a five-year literature review (July 2013-2018), we show that researchers often assess how activations elicited by a variable of interest differ between individuals. We argue that the rationale for such analyses, typically grounded in resource theory, offers an over-large analytical and interpretational flexibility that undermines their validity. We also recall how, in the established framework of the general linear model, inter-individual differences in behaviour can act as hidden moderators and spuriously induce differences in activations. We conclude with a set of recommendations and directions, which we hope will contribute to improving the statistical validity and the neurobiological interpretability of inter-individual difference analyses in task-related functional neuroimaging.
解释和预测临床和社会因素引起的个体行为差异是神经影像学最有前途的应用之一。在本观点中,我们讨论了任务相关功能神经影像学中个体间差异分析的理论和统计基础。利用为期五年的文献综述(2013 年 7 月至 2018 年),我们表明研究人员经常评估由感兴趣的变量引起的激活在个体之间如何不同。我们认为,这种分析的基本原理通常基于资源理论,提供了过大的分析和解释灵活性,从而破坏了其有效性。我们还回顾了在广义线性模型的既定框架中,个体间行为差异如何作为隐藏的调节变量并错误地引起激活差异。我们最后提出了一系列建议和方向,我们希望这将有助于提高任务相关功能神经影像学中个体间差异分析的统计有效性和神经生物学可解释性。