A modeling framework to understand and optimize ultrasound neuromodulation

Over the last decade, Low Intensity Focused Ultrasound Stimulation (LIFUS) has emerged as an attractive technology to modulate the activity of deep neural targets without invasive procedures. However, the underlying mechanisms by which ultrasonic waves can excite neurons are still unclear, which prevents a reliable and targeted application of the LIFUS technology and hinders its translation into clinical settings. The Neuronal Intramembrane Cavitation Excitation (NICE) model hypothesizes that ultrasound excites neurons through the nucleation and resonance of nanoscale membrane structures (bilayer sonophores) that can alter membrane capacitance and induce depolarizing currents. The model predictions of LIFUS neuromodulatory effects match a wide range of empirical observations in the brain. However, because it neglects cellular morphology, the NICE model cannot address important questions on the interaction of ultrasonic waves with neural structures. In this thesis, I propose several strategies to address these limitations, and to bridge the gap towards an experimental validation of this mechanism. I begin with an introduction establishing the background, describing the state-of-the-art and issues plaguing the field of ultrasound neuromodulation, and motivating the global objectives that will be pursued throughout the thesis. In a second part, I provide a mathematical description of the two pillars of the NICE model, namely a cavitation model describing mechanical membrane oscillations, and a point-neuron model describing the membrane electrical response. In doing so, I also discuss the inherent model assumptions, the potential implications for its validity range, and its sensitivity to key parameters. In a third part, I present a novel multi-Scale Optimized Neuronal Intramembrane Cavitation (SONIC) model that alleviates the stiffness of the NICE model by numerically separating its two constituent time scales. I demonstrate how this approach drastically reduces computational costs and confers interpretability to LIFUS neuromodulatory effects in terms of effective membrane dynamics. In a fourth part, I present a morphological expansion of the SONIC model (termed morphoSONIC) allowing to simulate intramembrane cavitation in a wide variety of realistic neuron models.With this framework, I investigate LIFUS neuromodulatory effects in peripheral nerve fibers. I predict that myelinated and unmyelinated axons can be distinctively and selectively recruited by LIFUS, thereby opening exciting avenues for peripheral neuromodulation. In a fifth part, I present the results of a parallel collective effort to track down the mechanisms of ultrasound neuromodulation in sensory neurons extracted from the medicinal leech.We found that LIFUS can reliably induce spiking activity within an optimal intensity range, and identified common and differing response features between acoustically and electrically evoked spikes.This chapter ends with a discussion of the implications of our findings for the validity of the intramembrane cavitation hypothesis. The achievements presented in this thesis provide an increased understanding of the mechanisms by which ultrasound modulates neural activity, as well as computational tools for their investigation. Moreover, they pave the way towards the development of reliable modeling frameworks to simulate ultrasound neuromodulatory effects across spatial, temporal and functional scales, helping to propel LIFUS into the clinics.

Complexity and performance in simple neuron models

Wulfram Gerstner, Skander Mensi, Richard Naud

The ability of simple mathematical models to predict the activity of single neurons is important for computational neuroscience. In neurons, stimulated by a time-dependent current or conductance, we want to predict precisely the timing of spikes and the sub-threshold voltage. During the last years several models have been tested on this type of data but never compared with the same protocol. One of the major outcome is that, from a certain degree of complexity, all are very efficient and gave statistically indistinguishable results. We studied a class of integrate-and-fire models (IF), with each member of the class implementing a selection of possible improvements: exponential voltage non-linearity1, spike-triggered adaptation current2, spike-triggered change in conductance, moving threshold3, sub-threshold voltage-dependent currents4. Each refinement adds a new term to the equations of the IF model. This IF family is extendable and adaptable to different neuron types and is able to deal with complex neural activities (i.e. adaptation, facilitation, bursting, relative refractoriness, ...). To systematically explore the effects of a given term of the model a new fitting procedure based on linear regression of voltage change5 is used. This method is fast, robust and allows the extraction of all the models parameters from a few seconds recordings of fluctuating injected current and membrane potential with hundreds spikes without any prior knowledge. To investigate the effect of our modifications, we used as training data three different Hodgkin-Huxley-like models, and two experimental recordings of fast spiking and regular spiking cells and then evaluate each IF model on a given test set. We observe that it is possible to fit a model that can reproduce the activity of neurons with high reliability (i.e. almost 100 % of the spike time and less than 1 mV of sub-threshold voltage difference). Using this framework one can classify IF models in terms of complexity and performance and evaluate the importance of each term for different stimulation paradigms.

2010

Predictive Engineering the Membrane Composition of Neocortical Neurons

Georges Khazen

The brain is the most mysterious and intricate biological structure known to man. It dominates the way we live, think, reason and behave. It is built of billions of neurons that communicate with each other through trillions of connections using electrical signals. The neocortex is undoubtedly the most complex brain region, hosting all higher brain functions such as sensory perception and cognitive behavior. Scientists have been struggling for more than a century to identify the anatomical blueprint of its cellular organization and to understand its dynamic behavior. The Blue Brain Project aims to develop the first simulation-based research environment for modeling and studying the basic building blocks of the neocortex, the neocortical column. The model column is based on data obtained from the juvenile rat somatosensory cortex. The best way to build such a large-scale model is by following a data-constraint driven approach and integrating biological information at different levels. In this thesis, I present novel informatics approaches to systematically constrain the membrane protein composition of different neocortical neuron types. In the first study, I investigate the single-cell gene expression patterns of ion channels in different neuron types and present a combinatorial rule extractor model that reverse engineers the observed expression rules in ten different neocortical neuron types. In the second study, I present a physical constrain checker that mines different publicly available data resources regarding all known membrane proteins such as channels and receptors, and extracts available information relating to their specific location on different parts of the neuronal morphologies, their 3D morphological structures as well as their genetic and proteomic annotations. Combining the results from both studies allows us to build neuronal models that better reflect the biological reality of neocortical neurons, ensuring the correct localization and distribution of membrane proteins across neuronal morphologies and computing their maximum count number based on their molecular surface areas and the available neuronal membrane surface area. This novel data-constraint driven approach is the first step towards integrating existing biological knowledge and constraints from various data sources into the process of building neuron models, in this way improving the neuron models as they come closer to the biological reality of real neurons.

EPFL2011

Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity

Cristina Colangelo, Henry Markram, Srikanth Ramaswamy

Neuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network activity. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the physiology of cholinergic modulation of neocortical cells and synapses. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to investigate the effects of levels of ACh on diverse neuron types and synapses, and consequently on emergent network activity. Preliminary simulations from the framework, which was not tuned to reproduce any specific ACh-induced network effects, not only corroborate the long-standing notion that ACh desynchronizes spontaneous network activity, but also predict that a dose-dependent activation of ACh gives rise to a spectrum of neocortical network activity. We show that low levels of ACh, such as during non-rapid eye movement (nREM) sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as during wakefulness and REM sleep, govern fast oscillations and network asynchrony. In addition, spontaneous network activity modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the regulation of neurons and synapses caused by increasing levels of ACh, which enhances cellular excitability and decreases the efficacy of local synaptic transmission. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulators in neural information processing.

2018