Department of Psychology, University of Oregon, 1227 University St, Eugene, OR 97403, United States.
Department of Psychological and Brain Sciences, Washington University in Saint Louis, 1 Brookings Drive, Saint Louis, MO 63130, United States.
Neuroimage. 2021 Dec 15;245:118656. doi: 10.1016/j.neuroimage.2021.118656. Epub 2021 Oct 19.
Studies of working memory (WM) function have tended to adopt either a within-subject approach, focusing on effects of load manipulations, or a between-subjects approach, focusing on individual differences. This dichotomy extends to WM neuroimaging studies, with different neural correlates being identified for within- and between-subjects variation in WM. Here, we examined this issue in a systematic fashion, leveraging the large-sample Human Connectome Project dataset, to conduct a well-powered, whole-brain analysis of the N-back WM task. We first demonstrate the advantages of parcellation schemes for dimension reduction, in terms of load-related effect sizes. This parcel-based approach is then utilized to directly compare the relationship between load-related (within-subject) and behavioral individual differences (between-subject) effects through both correlational and predictive analyses. The results suggest a strong linkage of within-subject and between-subject variation, with larger load-effects linked to stronger brain-behavior correlations. In frontoparietal cortex no hemispheric biases were found towards one type of variation, but the Dorsal Attention Network did exhibit greater sensitivity to between over within-subjects variation, whereas in the Somatomotor network, the reverse pattern was observed. Cross-validated predictive modeling capitalizing on this tight relationship between the two effects indicated greater predictive power for load-activated than load-deactivated parcels, while also demonstrating that load-related effect size can serve as an effective guide to feature (i.e., parcel) selection, in maximizing predictive power while maintaining interpretability. Together, the findings demonstrate an important consistency across within- and between-subjects approaches to identifying the neural substrates of WM, which can be effectively harnessed to develop more powerful predictive models.
工作记忆（WM）功能的研究倾向于采用两种方法：一种是采用被试内方法，专注于负荷操作的影响；另一种是采用被试间方法，专注于个体差异。这种二分法也延伸到 WM 神经影像学研究中，不同的神经相关物被确定为 WM 内和 WM 间变异的差异。在这里，我们通过利用大型人类连接组计划数据集，以系统的方式检查了这个问题，对 N 回 WM 任务进行了一项强大的全脑分析。我们首先展示了分块方案在负荷相关效应大小方面的维度降低优势。然后，通过相关和预测分析，这种基于包裹的方法被用来直接比较负荷相关（被试内）和行为个体差异（被试间）效应之间的关系。结果表明，被试内和被试间变异之间存在很强的联系，较大的负荷效应与更强的大脑行为相关性相关。在前顶叶皮层中，没有发现偏向于一种变异的半球偏见，但背侧注意网络确实表现出对被试间变异的敏感性高于被试内变异，而在躯体运动网络中，观察到相反的模式。利用这两种效应之间的紧密关系进行的交叉验证预测建模表明，对于激活的负荷包裹比去激活的负荷包裹具有更高的预测能力，同时还表明，负荷相关的效应大小可以作为有效指导特征（即包裹）选择的方法，在最大化预测能力的同时保持可解释性。总的来说，这些发现证明了在识别 WM 的神经基质方面，被试内和被试间方法之间存在重要的一致性，这可以有效地被利用来开发更强大的预测模型。
