Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China.
Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
Cereb Cortex. 2021 May 10;31(6):3122-3135. doi: 10.1093/cercor/bhab010.
Human learning can be understood as a network phenomenon, underpinned by the adaptive reconfiguration of modular organization. However, the plasticity of community structure (CS) in resting-state network induced by cognitive intervention has never been investigated. Here, we explored the individual difference of intrinsic CS between children with 5-year abacus-based mental calculation (AMC) training (35 subjects) and their peers without prior experience in AMC (31 subjects). Using permutation-based analysis between subjects in the two groups, we found the significant alteration of intrinsic CS, with training-attenuated individual difference. The alteration of CS focused on selective subsets of cortical regions ("core areas"), predominantly affiliated to the visual, somatomotor, and default-mode subsystems. These subsystems exhibited training-promoted cohesion with attenuated interaction between them, from the perspective of individuals' CS. Moreover, the cohesion of visual network could predict training-improved math ability in the AMC group, but not in the control group. Finally, the whole network displayed enhanced segregation in the AMC group, including higher modularity index, more provincial hubs, lower participation coefficient, and fewer between-module links, largely due to the segregation of "core areas." Collectively, our findings suggested that the intrinsic CS could get reconfigured toward more localized processing and segregated architecture after long-term cognitive training.
人类学习可以被理解为一种网络现象，其基础是模块化组织的适应性重新配置。然而，认知干预引起的静息态网络社区结构（CS）的可塑性从未被研究过。在这里，我们探讨了具有 5 年珠心算（AMC）训练的儿童（35 名受试者）与其没有 AMC 经验的同龄人之间内在 CS 的个体差异（31 名受试者）。通过两组受试者之间的基于置换的分析，我们发现内在 CS 存在显著的变化，且训练可以减弱个体差异。CS 的变化集中在皮质区域的选择性子集上（“核心区域”），主要与视觉、躯体运动和默认模式子系统有关。从个体 CS 的角度来看，这些子系统表现出训练促进的内聚性，同时减弱了它们之间的相互作用。此外，视觉网络的内聚性可以预测 AMC 组的训练提高数学能力，但不能预测对照组。最后，整个网络在 AMC 组中显示出增强的分离，包括更高的模块性指数、更多的局部枢纽、更低的参与系数和更少的模块间连接，这主要归因于“核心区域”的分离。总的来说，我们的研究结果表明，经过长期认知训练，内在 CS 可以朝着更局部化的处理和分离的结构进行重新配置。
