Effect of cognitive training on brain dynamics

doi:10.1088/1674-1056/ad09c8

中国物理B ›› 2024, Vol. 33 ›› Issue (2): 28704-028704.doi: 10.1088/1674-1056/ad09c8

Effect of cognitive training on brain dynamics

Guiyang Lv(吕贵阳), Tianyong Xu(徐天勇), Feiyan Chen(陈飞燕), Ping Zhu(朱萍), Miao Wang(王淼), and Guoguang He(何国光)^†

School of Physics, Zhejiang University, Hangzhou 310027, China

收稿日期:2023-05-25 修回日期:2023-10-09 接受日期:2023-11-06 出版日期:2024-01-16 发布日期:2024-01-25
通讯作者: Guoguang He E-mail:gghe@zju.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 62276229 and 32071096).

Effect of cognitive training on brain dynamics

Guiyang Lv(吕贵阳), Tianyong Xu(徐天勇), Feiyan Chen(陈飞燕), Ping Zhu(朱萍), Miao Wang(王淼), and Guoguang He(何国光)^†

School of Physics, Zhejiang University, Hangzhou 310027, China

Received:2023-05-25 Revised:2023-10-09 Accepted:2023-11-06 Online:2024-01-16 Published:2024-01-25
Contact: Guoguang He E-mail:gghe@zju.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 62276229 and 32071096).

摘要/Abstract

摘要： The human brain is highly plastic. Cognitive training is usually used to modify functional connectivity of brain networks. Moreover, the structures of brain networks may determine its dynamic behavior which is related to human cognitive abilities. To study the effect of functional connectivity on the brain dynamics, the dynamic model based on functional connections of the brain and the Hindmarsh-Rose model is utilized in this work. The resting-state fMRI data from the experimental group undergoing abacus-based mental calculation (AMC) training and from the control group are used to construct the functional brain networks. The dynamic behavior of brain at the resting and task states for the AMC group and the control group are simulated with the above-mentioned dynamic model. In the resting state, there are the differences of brain activation between the AMC group and the control group, and more brain regions are inspired in the AMC group. A stimulus with sinusoidal signals to brain networks is introduced to simulate the brain dynamics in the task states. The dynamic characteristics are extracted by the excitation rates, the response intensities and the state distributions. The change in the functional connectivity of brain networks with the AMC training would in turn improve the brain response to external stimulus, and make the brain more efficient in processing tasks.

关键词: brian dynamics, functional brain networks, cognitive training, abacus-based mental calculation

Abstract: The human brain is highly plastic. Cognitive training is usually used to modify functional connectivity of brain networks. Moreover, the structures of brain networks may determine its dynamic behavior which is related to human cognitive abilities. To study the effect of functional connectivity on the brain dynamics, the dynamic model based on functional connections of the brain and the Hindmarsh-Rose model is utilized in this work. The resting-state fMRI data from the experimental group undergoing abacus-based mental calculation (AMC) training and from the control group are used to construct the functional brain networks. The dynamic behavior of brain at the resting and task states for the AMC group and the control group are simulated with the above-mentioned dynamic model. In the resting state, there are the differences of brain activation between the AMC group and the control group, and more brain regions are inspired in the AMC group. A stimulus with sinusoidal signals to brain networks is introduced to simulate the brain dynamics in the task states. The dynamic characteristics are extracted by the excitation rates, the response intensities and the state distributions. The change in the functional connectivity of brain networks with the AMC training would in turn improve the brain response to external stimulus, and make the brain more efficient in processing tasks.

Key words: brian dynamics, functional brain networks, cognitive training, abacus-based mental calculation

中图分类号: (Neuronal network dynamics)

87.19.lj

87.19.ll (Models of single neurons and networks) 87.19.le (EEG and MEG)

Guiyang Lv(吕贵阳), Tianyong Xu(徐天勇), Feiyan Chen(陈飞燕), Ping Zhu(朱萍), Miao Wang(王淼), and Guoguang He(何国光). Effect of cognitive training on brain dynamics[J]. 中国物理B, 2024, 33(2): 28704-028704.

Guiyang Lv(吕贵阳), Tianyong Xu(徐天勇), Feiyan Chen(陈飞燕), Ping Zhu(朱萍), Miao Wang(王淼), and Guoguang He(何国光). Effect of cognitive training on brain dynamics[J]. Chin. Phys. B, 2024, 33(2): 28704-028704.

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Effect of cognitive training on brain dynamics

Effect of cognitive training on brain dynamics

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