Effects of weak electric fields on long-term synaptic plasticity

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

Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

International audienceEpileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology

Mecanismos biofísicos y fuentes de los potenciales extracelulares en el hipocampo

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física Aplicada III (Electricidad y Electrónica), leída el 20-11-2015Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEunpu

Effects of ionic concentration dynamics on neuronal activity

Neuronen sind bei der Informationsübertragung des zentralen Nervensystems von entscheidender Bedeutung. Ihre Aktivität liegt der Signalverarbeitung und höheren kognitiven Prozessen zugrunde. Neuronen sind in den extrazellulären Raum eingebettet, der mehrere Teilchen, darunter auch Ionen, enthält. Ionenkonzentrationen sind nicht statisch. Intensive neuronale Aktivität kann intrazelluläre und extrazelluläre Ionenkonzentrationen verändern. In dieser Arbeit untersuche ich das Wechselspiel zwischen neuronaler Aktivität und der Dynamik der Ionenkonzentrationen. Dabei konzentriere ich mich hauptsächlich auf extrazelluläre Kalium- und intrazelluläre Natriumkonzentrationen. Mit Hilfe der Theorie dynamischer Systeme zeige ich, wie moderate Änderungen dieser Ionenkonzentrationen die neuronale Aktivität qualitativ verändern können, wodurch sich möglicherweise die Signalverarbeitung verändert. Dann modelliere ich ein leitfähigkeitsbasiertes neuronales Netzwerk mit Spikes. Das Modell sagt voraus, dass eine moderate Änderung der Konzentrationen, die einen Mikroschaltkreis von Neuronen umgeben, die Leistungsspektraldichte der Populationsaktivität verändern könnte. Insgesamt unterstreicht diese Arbeit die Bedeutung der Dynamik der Ionenkonzentrationen für das Verständnis neuronaler Aktivität auf langen Zeitskalen und liefert technische Erkenntnisse darüber, wie das Zusammenspiel zwischen ihnen modelliert und analysiert werden kann.Neurons are essential in the information transfer mechanisms of the central nervous system. Their activity underlies both basic signal processing, and higher cognitive processes. Neurons are embedded in the extracellular space, which contains multiple particles, including ions which are vital to their functioning. Ionic concentrations are not static, intense neuronal activity alters the intracellular and extracellular ionic concentrations which in turn affect neuronal functioning. In this thesis, I study the interplay between neuronal activity and ionic concentration dynamics. I focus specifically on the extracellular potassium and intracellular sodium concentrations. Using dynamical systems theory, I illustrate how moderate changes in these ionic concentrations can qualitatively change neuronal activity, potentially altering signal processing. I then model a conductance-based spiking neural network. The model predicts that a moderate change in the concentrations surrounding a microcircuit of neurons could modify the power spectral density of the population activity. Altogether, this work highlights the need to consider ionic concentration dynamics to understand neuronal activity on long time scales and provides technical insights on how to model and analyze the interplay between them

A Mechanism of Co-Existence of Bursting and Silent Regimes of Activities of a Neuron

The co-existence of bursting activity and silence is a common property of various neuronal models. We describe a novel mechanism explaining the co-existence of and the transition between these two regimes. It is based on the specific homoclinic and Andronov-Hopf bifurcations of the hyper- and depolarized steady states that determine the co-existence domain in the parameter space of the leech heart interneuron models: canonical and simplified. We found that a sub-critical Andronov-Hopf bifurcation of the hyperpolarized steady state gives rise to small amplitude sub-threshold oscillations terminating through the secondary homoclinic bifurcation. Near the corresponding boundary the system can exhibit long transition from bursting oscillations into silence, as well as the bi-stability where the observed regime is determined by the initial state of the neuron. The mechanism found is shown to be generic for the simplified 4D and the original 14D leech heart interneuron models