Abacus Training Affects Brain Hemispheric Asymmetry of Connectivity Gradients during Childhood
Poster No:
1110
Submission Type:
Abstract Submission
Authors:
Jiali Mu1, Feiyan CHEN1
Institutions:
1Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, China
First Author:
Co-Author:
Introduction:
Hemispheric asymmetries are influenced by various factors, among which development and cognitive training are the most influencing ones (Toga et al. 2003). However, it remains largely unknown the impact of these factors on the children's hemispheric asymmetry of functional hierarchical topology. Previous study has adopted a framework to identify whole-brain functional gradients that capture the similarity of connectivity patterns in different cortical regions (Margulies et al. 2016). The current study extended such framework to explore the effect of abacus-based mental calculation (AMC) training on brain asymmetry during child development.
Methods:
2.1 Participants
This study recruited a total of 283 children who were randomly assigned into AMC and control groups at the beginning of primary school. The AMC group received 2 h per week of training from the 1st to the 6th grade, while the control group did not receive any physical or mental abacus instructions throughout this experiment. Four resting fMRI sessions were administered in the longitudinal study, including 1st, 2nd, 4th, and 6th grade.
2.2 Functional connectome gradients
We calculated within-hemispheric functional connectivity (FC) in voxel-level. Specifically, the atlas of intrinsic connectivity of homotopic areas (AICHA, 4mm) was used to parcellate the brain regions, yielding 9316 voxels in the left hemisphere and 9198 voxels in the right hemisphere. For each hemisphere, we utilized the BrainSpace toolbox to estimate functional gradients (Vos et al. 2020). Notably, the current study focused on the unimodal-transmodal gradient.
2.3 Statistical analysis
To quantify hemisphere asymmetry, we chose left-right as the asymmetry index (AI) (Wan et al. 2022). A positive AI-score meant the hemispheric leftwards, while a negative AI-score dominated rightwards. To test group and development effects on the AI of the unimodal-transmodal gradient, we applied a linear mixed model. Then we examined hemispheric differences across Yeo's seven network computing their network's gradient average. Temperament was assessed through scale, with two main levels of exuberance and BI.
This study recruited a total of 283 children who were randomly assigned into AMC and control groups at the beginning of primary school. The AMC group received 2 h per week of training from the 1st to the 6th grade, while the control group did not receive any physical or mental abacus instructions throughout this experiment. Four resting fMRI sessions were administered in the longitudinal study, including 1st, 2nd, 4th, and 6th grade.
2.2 Functional connectome gradients
We calculated within-hemispheric functional connectivity (FC) in voxel-level. Specifically, the atlas of intrinsic connectivity of homotopic areas (AICHA, 4mm) was used to parcellate the brain regions, yielding 9316 voxels in the left hemisphere and 9198 voxels in the right hemisphere. For each hemisphere, we utilized the BrainSpace toolbox to estimate functional gradients (Vos et al. 2020). Notably, the current study focused on the unimodal-transmodal gradient.
2.3 Statistical analysis
To quantify hemisphere asymmetry, we chose left-right as the asymmetry index (AI) (Wan et al. 2022). A positive AI-score meant the hemispheric leftwards, while a negative AI-score dominated rightwards. To test group and development effects on the AI of the unimodal-transmodal gradient, we applied a linear mixed model. Then we examined hemispheric differences across Yeo's seven network computing their network's gradient average. Temperament was assessed through scale, with two main levels of exuberance and BI.
Results:
3.1 Group and grade effects on the AI of the hemispheric gradient
The linear mixed linear model revealed a significant group × grade interaction effect of the unimodal-transmodal gradient AI (p=0.027). In 1st grade, there was no significant difference between the AMC group and the control group; In 6th grade, the two groups had a significant difference (p = 0.003).
From a developmental perspective, in 1st grade, there was no significant hemispheres asymmetry both the two group. However, the control group showed significant leftward asymmetry in following 2nd, 4th and 6th grade (p < 0.001), while the AMC group always showed no significant lateralization in these time points.
3.2 Frontoparietal network asymmetry and temperament
From 1st grade to 6th grade, both groups of children showed significantly rightward asymmetry in the frontoparietal network. There was a positive correlation between the AI of frontoparietal network in the 1st grade and their 3rd grade exuberance in the control group (r = 0.489, p = 0.008), but not in the AMC group. In addition, there was a positive correlation between the asymmetry in the 1st grade and their 3rd grade another temperament BI in the AMC group (r = 0.406, p = 0.009).
The linear mixed linear model revealed a significant group × grade interaction effect of the unimodal-transmodal gradient AI (p=0.027). In 1st grade, there was no significant difference between the AMC group and the control group; In 6th grade, the two groups had a significant difference (p = 0.003).
From a developmental perspective, in 1st grade, there was no significant hemispheres asymmetry both the two group. However, the control group showed significant leftward asymmetry in following 2nd, 4th and 6th grade (p < 0.001), while the AMC group always showed no significant lateralization in these time points.
3.2 Frontoparietal network asymmetry and temperament
From 1st grade to 6th grade, both groups of children showed significantly rightward asymmetry in the frontoparietal network. There was a positive correlation between the AI of frontoparietal network in the 1st grade and their 3rd grade exuberance in the control group (r = 0.489, p = 0.008), but not in the AMC group. In addition, there was a positive correlation between the asymmetry in the 1st grade and their 3rd grade another temperament BI in the AMC group (r = 0.406, p = 0.009).
·Fig 1. Group and grade effects on the AI of the hemispheric gradient
Conclusions:
In current study, we investigated the effects of developmental and cognitive training on the hemispheric asymmetry of connectivity gradient. Our study showed that both the development and cognitive training play important roles in hemispheric asymmetry of connectivity gradient. Additionally there was a significant correlation between the asymmetry of the frontoparietal network and temperament, which was distinct across these two groups. These findings provide novel insights of the impact of long-term cognitive training on hemispheric asymmetry of connectivity gradients.
Learning and Memory:
Skill Learning 1
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Development
FUNCTIONAL MRI
Other - Brain asymmetry; Abacus-based mental calculation; Functional gradient; Functional connectivity
1|2Indicates the priority used for review
Provide references using author date format
Toga A W. (2003), ‘Mapping Brain Asymmetry’, Nature Reviews Neuroscience, vol. 4, no. 1, pp. 37–48.
Vos de Wael R. (2020), ‘BrainSpace: A Toolbox for the Analysis of Macroscale Gradients in Neuroimaging and Connectomics Datasets’, Communications Biology, vol. 3, no. 1, pp. 103.
Wan B. (2022), ‘Heritability and cross-species comparisons of human cortical functional organization asymmetry’, eLife, vol. 11, pp. e77215.