Work

A multi-modal neuroimaging dataset on long-term cognitive training in school children

Year: 2025

Type: data-paper

Abstract: 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.

Source: Scientific Data

DOI: 10.1038/s41597-025-06500-9

Authors: Jiali Mu, Tianyong Xu, Ye Xie, Feiyan Chen

Institutions: Zhejiang University, Shenzhen University

Language: English

Topic: Cognitive and developmental aspects of mathematical skills

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDGs: Good health and well-being, Quality education

Open Access status: gold

Estimated APC paid: $2,490