Evidence map›Paper›PMID 38421061›Full record

ArticleDevelopmental science2024

Long-term abacus training gains in children are predicted by medial temporal lobe anatomy and circuitry.

Ye Xie, Hyesang Chang, Yi Zhang, Chunjie Wang, Yuan Zhang, Lang Chen, Fengji Geng, Yixuan Ku, Vinod Menon, Feiyan Chen

Abstract read
In one paragraph

Article in Developmental science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. What Does 'Human-Centred AI' Mean?Behavioral sciences (Basel, Switzerland) · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ye XieBio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, PR China.
Hyesang ChangDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-2231-1112
Yi ZhangBio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, PR China.
Chunjie WangBio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, PR China.
Yuan ZhangDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.
Lang ChenDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-2118-5601
Fengji GengDepartment of Curriculum and Learning Sciences, Zhejiang University, Hangzhou, PR China.
Yixuan KuCenter for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou, PR China.
Vinod MenonDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.
Feiyan ChenBio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, PR China.ORCID https://orcid.org/0000-0003-3730-1651

Funding

Interventions in Math Learning Disabilities: Cognitive and Neural CorrelatesR01HD059205 · NICHD · STANFORD UNIVERSITY · PI Vinod Menon · 2009 to 2026
$10.8M
Longitudinal Neurocognitive Studies of Mathematical Disabilities: trajectories and outcomesR37HD094623 · NICHD · STANFORD UNIVERSITY · PI Vinod Menon · 2018 to 2026
$6.8M
Mathematical Cognition in Autism: A Cognitive and Systems Neuroscience ApproachR01MH084164 · NIMH · STANFORD UNIVERSITY · PI MENON, VINOD · 2011 to 2021
$6.7M
National Natural Science Foundation of China 32071096National Social Science Foundation of China 17ZDA323NICHD NIH HHS R01 HD059205NICHD NIH HHS R37 HD094623NIMH NIH HHS R01 MH084164United States National Institutes of Health HD059205United States National Institutes of Health HD094623United States National Institutes of Health MH084164
6 · The paper itself

Abstract

Abacus-based mental calculation (AMC) is a widely used educational tool for enhancing math learning, offering an accessible and cost-effective method for classroom implementation. Despite its universal appeal, the neurocognitive mechanisms that drive the efficacy of AMC training remain poorly understood. Notably, although abacus training relies heavily on the rapid recall of number positions and sequences, the role of memory systems in driving long-term AMC learning remains unknown. Here, we sought to address this gap by investigating the role of the medial temporal lobe (MTL) memory system in predicting long-term AMC training gains in second-grade children, who were longitudinally assessed up to fifth grade. Leveraging multimodal neuroimaging data, we tested the hypothesis that MTL systems, known for their involvement in associative memory, are instrumental in facilitating AMC-induced improvements in math skills. We found that gray matter volume in bilateral MTL, along with functional connectivity between the MTL and frontal and ventral temporal-occipital cortices, significantly predicted learning gains. Intriguingly, greater gray matter volume but weaker connectivity of the posterior parietal cortex predicted better learning outcomes, offering a more nuanced view of brain systems at play in AMC training. Our findings not only underscore the critical role of the MTL memory system in AMC training but also illuminate the neurobiological factors contributing to individual differences in cognitive skill acquisition. A video abstract of this article can be viewed at https://youtu.be/StVooNRc7T8. RESEARCH HIGHLIGHTS: We investigated the role of medial temporal lobe (MTL) memory system in driving children's math learning following abacus-based mental calculation (AMC) training. AMC training improved math skills in elementary school children across their second and fifth grade. MTL structural integrity and functional connectivity with prefrontal and ventral temporal-occipital cortices predicted long-term AMC training-related gains.

Indexed as

LearningTemporal LobeChildFemaleGray MatterHumansMagnetic Resonance ImagingMaleMathematicsMemoryabacusindividual differencelongitudinal cognitive interventionmedial temporal lobemental calculationmulti‐modal neuroimaging

Identifiers

PMID38421061
PMCPMC11161333

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.