Dissertations and Theses

Date of Award


Document Type



Biomedical Engineering

First Advisor

Lucas Parra


tDCS, synaptic plasticity, LTP, LTD, learning, Hebbian


Transcranial direct current stimulation (tDCS) is a technique where a weak direct electrical current is applied to the scalp with the goal of stimulating the brain. There is tremendous interest in the use of tDCS for treating brain disorders and improving brain function. However, the effects of tDCS have been highly variable across studies, leading to a debate over its efficacy. A major challenge is therefore to design tDCS protocols that yield predictable effects, which will require a better understanding of its basic mechanisms of action. One commonly discussed mechanism is that tDCS may alter synaptic plasticity, but the biophysics that support this interaction between tDCS and synaptic plasticity remain unclear.

This dissertation is centered around a fundamental hypothesis; that tDCS can modulate the brain’s ongoing endogenous synaptic plasticity by altering the voltage dynamics in postsynaptic neurons. In chapters 1 and 2, I discuss how this hypothesis is built on decades of research characterizing effects of weak electric fields on neuronal membrane potential and the dependence of synaptic plasticity on membrane potential. In chapters 3 and 4, several experimental predictions of this theory are tested using a canonical model system for studying synaptic plasticity, the hippocampal brain slice. The theory accounts for the dependence of DCS effects on the temporal pattern of synaptic inputs and their location along a dendritic arbor, which may be sources of unexplained variability in human tDCS studies.

An essential part of the proposed theory is that the effects of tDCS are mediated by the same cellular machinery that implements Hebbian synaptic plasticity. In chapter 4, we show that the effects of DCS therefore exhibit Hebbian properties, such as pathway specificity and associativity, whose role in associative learning has been studied extensively. These results suggest that tDCS can enhance associative learning and remain functionally specific by interacting with endogenous plasticity mechanisms. We further propose that clinical tDCS should be paired with tasks that induce plasticity to harness this phenomenon.

In chapters 4 and 5, I present a computational model that incorporates established biophysical mechanisms for neuronal voltage dynamics, Hebbian synaptic plasticity, and membrane polarization due to weak electric fields. The model is in good agreement with our experimental results, demonstrating their consistency with the proposed theory. The model is then used to predict effects of tDCS with new synaptic input patterns and propose future brain slice experiments. The remaining chapters, 6 through 8, discuss the advances made by this work and important limitations. The theory and accompanying model provide a principled method for predicting effects on synaptic plasticity when tDCS is applied during training. However, it does not account for several observed effects of tDCS, such as on plasticity that is induced after stimulation has ended. Integrating the present theory with other potential mechanisms is therefore an important area for future research. Nonetheless, this work establishes a mechanistic framework for interpreting the effects of tDCS on synaptic plasticity and should aid in the design of tDCS protocols to facilitate associative learning.



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