How do different measures of brain structure correlate with individual differences in arithmetic fluency? This paper builds on two previously published studies in which individual differences in children's arithmetic fluency were correlated with measures of white (Polspoel et al., 2019) and grey matter (Polspoel et al., 2020) in one sample of children. We combined the brain imaging data of these two studies with measures of cognitive abilities that have been shown to be predictive of arithmetic fluency, i.e., numerical magnitude processing, working memory and rapid automatized naming (RAN). This allowed us to investigate to which extend the observed structural brain imaging measures uniquely correlated with children's arithmetic fluency, on top of each other as well as on top of the abovementioned cognitive variables. Participants were 43 typically developing 9-10-year-olds. All measures were added to a hierarchical multiple regression model. This regression model showed that the white matter integrity of the right inferior longitudinal fasciculus and the cortical complexity of the left postcentral gyrus remained unique predictors of individual differences in arithmetic when the abovementioned cognitive variables were taken into account. This indicates that structural neuroimaging measures can explain individual differences in arithmetic performance that are not merely accounted for by relevant cognitive predictors.

大脑结构的不同测量值如何与算术流畅度的个体差异相关联？本文基于之前发表的两项研究，其中儿童算术流畅度的个体差异与一个儿童样本中的白色（Polspoel 等人，2019 年）和灰质（Polspoel 等人，2020 年）的测量值相关。我们将这两项研究的脑成像数据与已被证明可以预测算术流畅性的认知能力测量相结合，即数值处理、工作记忆和快速自动命名（RAN）。这使我们能够研究观察到的结构性脑成像测量与儿童的算术流畅度唯一相关的范围，以及在上述认知变量之上。参与者是 43 岁，通常是 9-10 岁的孩子。所有测量都添加到分层多元回归模型中。该回归模型表明，当考虑到上述认知变量时，右侧下纵束的白质完整性和左侧中央后回的皮质复杂性仍然是算术个体差异的唯一预测因子​​。这表明结构性神经影像学测量可以解释算术表现的个体差异，这些差异不仅仅由相关的认知预测因子解释。该回归模型表明，当考虑到上述认知变量时，右侧下纵束的白质完整性和左侧中央后回的皮质复杂性仍然是算术个体差异的唯一预测因子​​。这表明结构性神经影像学测量可以解释算术表现的个体差异，这些差异不仅仅由相关的认知预测因子解释。该回归模型表明，当考虑到上述认知变量时，右侧下纵束的白质完整性和左侧中央后回的皮质复杂性仍然是算术个体差异的唯一预测因子​​。这表明结构性神经影像学测量可以解释算术表现的个体差异，这些差异不仅仅由相关的认知预测因子解释。