Resting-state EEG biomarker of Children's Visual-motor Integration: a 4-year Longitudinal Study
Poster No:
1319
Submission Type:
Abstract Submission
Authors:
Zihan Yang1, Junzhe Wang2, Jie Chen1, Yun Nan3, Xiujie Yang1
Institutions:
1Faculty of Psychology, Beijing Normal University, Beijing, China, 2Department of Psychology, University of Chinese Academy of Sciences, Beijing, China, 3State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
First Author:
Co-Author(s):
Yun Nan
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Introduction:
Sustained oscillatory activity can be detected at rest in discrete frequency bands, mostly in the delta-theta, alpha and gamma domains (Giraud et al., 2007), and functional connectivity patterns can be unique to individuals and exhibit a relatively stable feature to predict cognitive abilities (Chhade et al., 2024; Shen et al., 2017). The visual-motor integration (VMI) skill has been thought as a fundamental factor of literacy acquisition (Santi et al., 2015; Ye et al., 2023). Numerous studies have shown that VMI ability is closely related to reading and the correlation increases with age in Chinese reading (Zhao et al., 2024), but little is known about how VMI relate to functional networks in EEG and the longitudinal role to predict reading ability.
Methods:
This study followed 70 children (41 boys, age: 7.36 ± 0.30 years) from grade 1 (T1) to grade 5 (T3) with 2-year intervals. Resting-state EEG data were recorded at T1, and VMI and reading abilities (including rapid automized naming (RAN) and reading fluency) were assessed at all three time points. We used Phase Lag Index (PLI) to index specific frequency band functional connectivity, while Connectome-based Predictive Modeling (CPM) was applied to examine whether and which frequency of functional connectivity could predict individual VMI ability at T2 and T3. Significant (p < .05) edges were selected, followed by positive and negative correlations considered in model construction separately or together (combined network; Kabbara et al., 2022). A total of 5,000 permutation tests were performed to determine the statistical significance of results. Additionally, we extracted FC edges which present efficient predictive role in CPM and conducted mediation analysis to assess whether specific frequency FC effective in CPM could predict reading abilities via VMI skill.
Results:
After nonparametric permutation test, positive network of alpha-band functional connectivity at T1 predicted children's VMI at T2 (63 children included, 7 excluded due to absences; positive edges: r = .27, p = .005; combined with negative edges: r = .27, p = .006; Figure 1A), as well as this ability at T3 (63 children; positive edges: r = .24, p = .008; combined with negative edges: r = .24, p = .01; Figure 1B). Besides, negative network of delta-band functional connectivity at T1 predicted children's VMI at T2 (negative edges: r = .30, p = .036; positive edges: r = .21, p = .093; combined: r = .40, p = .003; Figure 1C), along with this ability at T3 (negative edges: r = .34, p = .008; combined with positive edges: r = .29, p = .023; Figure 1D). Positive edges at alpha-band were primarily concentrated within wide regions involving the left temporoparietal, occipital and frontal area, while negative edges at delta-band were predominantly located in the right occipital and temporal regions, as well as interhemispheric connections.
Moreover, after controlling age, gender, SES, IQ, mediation analysis revealed that VMI at T2 mediated the relation between alpha-band positive network and RAN at both T2 (95% CI = [-.298, -.020]) and T3 (95% CI = [-.336, -.013]), as well as the relation between delta-band negative network and RAN at both T2 (95% CI = [.058, .326]) and T3 (95% CI = [.017, .422]). For the relation between alpha-band positive network and reading fluency at T3, VMI at T3 also played the mediating role respectively (95% CI = [.000, .328]; Figure 2).
Moreover, after controlling age, gender, SES, IQ, mediation analysis revealed that VMI at T2 mediated the relation between alpha-band positive network and RAN at both T2 (95% CI = [-.298, -.020]) and T3 (95% CI = [-.336, -.013]), as well as the relation between delta-band negative network and RAN at both T2 (95% CI = [.058, .326]) and T3 (95% CI = [.017, .422]). For the relation between alpha-band positive network and reading fluency at T3, VMI at T3 also played the mediating role respectively (95% CI = [.000, .328]; Figure 2).
·Figure 1. Results of the CPM model.
·Figure 2. Mediation models of between functional connectivity and reading abilities.
Conclusions:
Our study was the first to explore the association between multi-band functional connectivity and VMI ability in a developmental children sample using CPM. The present results indicated an across-time stable but different role of alpha and delta-band networks in predicting future VMI ability, underscoring the critical role of VMI ability in reading development. These findings refined the neural framework related to children's early VMI skill and its role in reading abilities from the perspective of functional connectivity which provides a new biomarker for cognitive development.
Language:
Reading and Writing
Modeling and Analysis Methods:
Classification and Predictive Modeling
EEG/MEG Modeling and Analysis 1
Motor Behavior:
Visuo-Motor Functions 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Keywords:
Development
Electroencephaolography (EEG)
Machine Learning
Modeling
Other - Longitudinal Study; Connectome Predictive Model (CPM); Visual-motor Integration; Reading; Resting; Multi-band Functional Network
1|2Indicates the priority used for review
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Kabbara, A., Robert, G., Khalil, M., Verin, M., Benquet, P., & Hassan, M. (2022). An electroencephalography connectome predictive model of major depressive disorder severity. Scientific Reports, 12(1), 6816.
Santi, K. L., Francis, D. J., Currie, D., & Wang, Q. (2015). Visual-Motor Integration Skills: Accuracy of Predicting Reading. Optometry and Vision Science, 92(2), 217–226.
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Ye, Y., Inoue, T., Maurer, U., & McBride, C. (Eds.). (2023). Routledge international handbook of visual-motor skills, handwriting, and spelling: Theory, research, and practice. Routledge, Taylor & Francis.
Zhao, Y., Li, J., & Bi, H.-Y. (2024). The development of the correlation between visual-motor integration and reading. Advances in Psychological Science, 32(12), 2091–2099.