Demidenko Michael I, Mumford Jeanette A, Ram Nilam, Poldrack Russell A
Department of Psychology, Stanford University, Stanford, United States.
Department of Psychology, Stanford University, Stanford, United States.
Dev Cogn Neurosci. 2024 Feb;65:101337. doi: 10.1016/j.dcn.2023.101337. Epub 2023 Dec 28.
Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N = 346 [Age 12.0; 44 % Female]; N = 97 [19.3; 58 %]; N = 112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.
在神经发育研究中,解读任务功能磁共振成像(fMRI)期间引发的神经反应仍然是一项挑战。金钱激励延迟(MID)任务是一种fMRI奖励处理任务,在文献中被广泛使用。然而,现代心理测量工具尚未用于评估MID任务fMRI数据的测量属性。本研究使用了来自三个青少年样本(N = 346 [年龄12.0岁;44%为女性];N = 97 [19.3岁;58%为女性];N = 112 [20.2岁;38%为女性])的类似任务设计的数据,以评估MID任务中fMRI反应的多种测量属性。验证性因素分析(CFA)用于评估该任务的先验理论模型及其在三个样本中的测量不变性。探索性因素分析(EFA)用于确定样本间数据驱动的测量结构。CFA结果表明,先验模型不能很好地代表这些MID任务fMRI数据。在所有样本中,数据驱动的EFA模型一致确定了一个包含运行和双侧脑区因素的六到七因素结构。这个因素结构在样本间具有中等至高度的一致性。总之,这些发现表明有必要评估流行的fMRI任务设计的理论框架,以增进我们对脑-行为关联的理解和解释。