Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model
IntroductionTranscranial 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 observe...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Network Physiology |
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| author | Han Lu Han Lu Han Lu Han Lu Lukas Frase Claus Normann Claus Normann Stefan Rotter Stefan Rotter Stefan Rotter |
| author_facet | Han Lu Han Lu Han Lu Han Lu Lukas Frase Claus Normann Claus Normann Stefan Rotter Stefan Rotter Stefan Rotter |
| author_sort | Han Lu |
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| description | IntroductionTranscranial 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.MethodsWe 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.ResultsOur 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.DiscussionOur 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. |
| format | Article |
| id | doaj-art-e12665c336ed4db5b84610577cb29d33 |
| institution | Kabale University |
| issn | 2674-0109 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Frontiers in Network Physiology |
| spelling | doaj-art-e12665c336ed4db5b84610577cb29d332025-08-20T03:33:07ZengFrontiers Media S.A.Frontiers in Network Physiology2674-01092025-07-01510.3389/fnetp.2025.15658021565802Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity modelHan Lu0Han Lu1Han Lu2Han Lu3Lukas Frase4Claus Normann5Claus Normann6Stefan Rotter7Stefan Rotter8Stefan Rotter9Bernstein Center Freiburg, University of Freiburg, Freiburg, GermanyFaculty of Biology, University of Freiburg, Freiburg, GermanyBrainLinks-BrainTools, University of Freiburg, Freiburg, GermanyCentre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR3212, Strasbourg, FranceDepartment of Psychosomatic Medicine and Psychotherapy, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, GermanyDepartment of Psychiatry and Psychotherapy, Medical Center, University of Freiburg-Faculty of Medicine, Freiburg, GermanyCenter for Basics in Neuromodulation, Freiburg, GermanyBernstein Center Freiburg, University of Freiburg, Freiburg, GermanyFaculty of Biology, University of Freiburg, Freiburg, GermanyBrainLinks-BrainTools, University of Freiburg, Freiburg, GermanyIntroductionTranscranial 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.MethodsWe 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.ResultsOur 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.DiscussionOur 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.https://www.frontiersin.org/articles/10.3389/fnetp.2025.1565802/fulltDCSmotor learninghomeostatic structural plasticityspiking neural networkcell assembly |
| spellingShingle | Han Lu Han Lu Han Lu Han Lu Lukas Frase Claus Normann Claus Normann Stefan Rotter Stefan Rotter Stefan Rotter Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model Frontiers in Network Physiology tDCS motor learning homeostatic structural plasticity spiking neural network cell assembly |
| title | Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model |
| title_full | Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model |
| title_fullStr | Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model |
| title_full_unstemmed | Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model |
| title_short | Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model |
| title_sort | resolving inconsistent effects of tdcs on learning using a homeostatic structural plasticity model |
| topic | tDCS motor learning homeostatic structural plasticity spiking neural network cell assembly |
| url | https://www.frontiersin.org/articles/10.3389/fnetp.2025.1565802/full |
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