Oravecz Zita, Sliwinski Martin, Kim Sharon H, Williams Lindy, Katz Mindy J, Vandekerckhove Joachim
The Pennsylvania State University, Harrisburg, PA 16802 USA.
Department of Neurology, Albert Einstein College of Medicine, New York, NY 10461 USA.
Comput Brain Behav. 2025;8(3):410-420. doi: 10.1007/s42113-025-00238-8. Epub 2025 Mar 24.
Repeated assessments of cognitive performance yield rich data from which we can extract markers of cognitive performance. Computational cognitive process models are often fit to repeated cognitive assessments to quantify individual differences in terms of substantively meaningful cognitive markers and link them to other person-level variables. Most studies stop at this point and do not test whether these cognitive markers have utility for predicting some meaningful outcomes. Here, we demonstrate a modeling approach that can fill this gap. Using this approach, we can simultaneously extract cognitive markers from repeated assessment data and use these together with demographic covariates for predictive modeling of a clinically interesting outcome in a Bayesian multilevel modeling framework. We describe this approach by constructing a predictive process model in which features of learning are combined with demographic variables to predict mild cognitive impairment and demonstrate it using data from the Einstein Aging Study.
对认知表现的重复评估能产生丰富的数据，我们可以从中提取认知表现的指标。计算认知过程模型通常适用于重复的认知评估，以便根据具有实质意义的认知指标来量化个体差异，并将这些差异与其他个体层面的变量联系起来。大多数研究到此为止，并没有测试这些认知指标是否有助于预测某些有意义的结果。在此，我们展示了一种能够填补这一空白的建模方法。使用这种方法，我们可以从重复评估数据中同时提取认知指标，并在贝叶斯多层次建模框架中，将这些指标与人口统计学协变量一起用于对临床上有意义的结果进行预测建模。我们通过构建一个预测过程模型来描述这种方法，在该模型中，学习特征与人口统计学变量相结合，以预测轻度认知障碍，并使用爱因斯坦衰老研究的数据对其进行了验证。
