Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model.

IF 3
Frontiers in network physiology Pub Date : 2025-07-07 eCollection Date: 2025-01-01 DOI:10.3389/fnetp.2025.1565802
Han Lu, Lukas Frase, Claus Normann, Stefan Rotter
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引用次数: 0

Abstract

Introduction: Transcranial direct current stimulation (tDCS) is increasingly used to modulate motor learning. Current polarity and intensity, electrode montage, and application before or during learning had mixed effects. Both Hebbian and homeostatic plasticity were proposed to account for the observed effects, but the explanatory power of these models is limited. In a previous modeling study, we showed that homeostatic structural plasticity (HSP) model can explain long-lasting after-effects of tDCS and transcranial magnetic stimulation (TMS). The interference between motor learning and tDCS, which are both based on HSP in our model, is a candidate mechanism to resolve complex and seemingly contradictory experimental observations.

Methods: We implemented motor learning and tDCS in a spiking neural network subject to HSP. The anatomical connectivity of the engram induced by motor learning was used to quantify the impact of tDCS on motor learning.

Results: Our modeling results demonstrated that transcranial direct current stimulation applied before learning had weak modulatory effects. It led to a small reduction in connectivity if it was applied uniformly. When applied during learning, targeted anodal stimulation significantly strengthened the engram, while targeted cathodal or uniform stimulation weakened it. Applied after learning, targeted cathodal, but not anodal, tDCS boosted engram connectivity. Strong tDCS would distort the engram structure if not applied in a targeted manner.

Discussion: Our model explained both Hebbian and homeostatic phenomena observed in human tDCS experiments by assuming memory strength positively correlates with engram connectivity. This includes applications with different polarity, intensity, electrode montage, and timing relative to motor learning. The HSP model provides a promising framework for unraveling the dynamic interaction between learning and transcranial DC stimulation.

利用稳态结构塑性模型解决tDCS对学习的不一致影响。
简介:经颅直流电刺激(tDCS)越来越多地用于调节运动学习。电流极性和强度、电极蒙太奇以及学习前或学习期间的应用效果好坏参半。Hebbian和稳态可塑性都被用来解释观察到的效应,但这些模型的解释力有限。在之前的建模研究中,我们发现稳态结构可塑性(HSP)模型可以解释tDCS和经颅磁刺激(TMS)的持久后效。在我们的模型中,运动学习和tDCS之间的干扰都是基于HSP的,这是解决复杂和看似矛盾的实验观察结果的候选机制。方法:对HSP下的尖峰神经网络进行运动学习和tDCS。通过运动学习引起的印迹的解剖连通性来量化tDCS对运动学习的影响。结果:模拟结果表明,学习前经颅直流电刺激具有微弱的调节作用。如果它被统一应用,它会导致连通性的小幅降低。在学习过程中,有针对性的阳极刺激显著增强了记忆印迹,而有针对性的阴极或均匀刺激则削弱了记忆印迹。在学习后应用,针对阴极,而不是阳极,tDCS增强了印痕连接。如果不有针对性地应用强tDCS,则会扭曲印痕结构。讨论:我们的模型通过假设记忆强度与印痕连通性正相关来解释在人类tDCS实验中观察到的Hebbian和稳态现象。这包括与运动学习相关的不同极性、强度、电极蒙太奇和时间的应用。HSP模型为揭示学习和经颅直流刺激之间的动态相互作用提供了一个有希望的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.70
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