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Abstract:

Arithmetic is an essential skill for further mathematical and educational development, and comprises a large part of our daily lives. Even tough, over the past few years, an increase in both functional and structural developmental neuroscientific research on mathematical cognition and arithmetic has occurred, a lot is still not fully understood. This doctoral dissertation aimed to further identify which brain regions are important for typically developing children's arithmetic fluency. Neuroimaging work in adults has shown that strategy use (i.e., fact retrieval vs. procedures) modulates the arithmetic brain network, however, this was never clearly studied in children. A first fMRI study investigated children's neural activation associated with the use of these strategies in subtraction and multiplication, and observed distinct neural networks associated with each strategy, but no differences between operations when taking strategy into account. Next, within children's multiplication fact retrieval, performance can be influenced by various effects, such as the problem size and interference effect, as evidenced by behavioral research. A second fMRI study investigated the neural basis of both effects, and, concurring with previous studies, revealed clear behavioral effects of problem size and interference, but at the neural level, only a clear effect of problem size. The interference effect was not detected; no clear neural distinctions were observed between low and high interfering items. Other than its functionality, the structure of grey matter regions has also been associated to cognitive skills. Accordingly, a third study investigated the structural grey matter correlates of children's arithmetic fluency, by looking at both volume and cortical complexity, and observed associations with various cortical grey matter structures. Furthermore, as the grey matter regions of the arithmetic brain network are spatially distant, it is also crucial to study the structural white matter connections between these regions. In a fourth study, the white matter integrity of previously observed arithmetic-related white matter pathways was correlated to children's arithmetic fluency, implementing spherical deconvolution, a novel non-tensor method which goes beyond classic DTI to tackle its methodological constraints. Clear associations were observed between the right inferior longitudinal fasciculus and arithmetic. Finally, a fifth study investigated the added value of the collected structural brain imaging data on top of well-known behavioral measures in predicting individual differences in the children's arithmetic fluency, and revealed that the neuro-anatomical measures provided the best prediction of performance, highlighting the value of brain imaging measures for the prediction of cognitive skills and striving towards a bridge between cognitive neuroscience and education. In all, the combination of both functional and structural neuroimaging in these studies has led to results which further expand our understanding of the neural substrates of children's arithmetic fluency.