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Updated: Jan 7, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
A multi-modal neuroimaging dataset on long-term cognitive training in school children
Jiali Mu1, Tianyong Xu1, Ye Xie2
1School of Physics, Zhejiang University, Hangzhou, China.
None:
We present a dataset of healthy children (N = 99) to explore the impact of long-term cognitive training on multimodal brain structure, function, and cognitive performance. At the start of primary school, participants were randomly assigned to an experimental group (n = 53), which received five years of structured abacus-based mental calculation (AMC) training from Grade 1 to Grade 5, or a control group (n = 46) without additional training. Neuroimaging data, including resting-state functional MRI and T1-weighted structural MRI, were collected after the first training year (at the start of Grade 2). Behavioral data, including standardized mathematical ability tests and psychological assessments, were collected longitudinally across Grades 2 to 5. To promote open access, the Brain Imaging Data Structure (BIDS) formatted data and corresponding quality control reports are available on the Science Data Bank. It offers a unique opportunity to deepen our understanding of how long-term training shapes neural and behavioral development in childhood.
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