Local Field Potentials: Myths and Misunderstandings

The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media. As such, similar levels of activity may result in potentials that differ in several orders of magnitude in different populations. The geometry of these sources has been experimentally inaccessible until intracerebral high density recordings enabled the co-activating sources to be revealed. Without this information, it has proven difficult to interpret a century's worth of recordings that used temporal cues alone, such as event or spike related potentials and frequency bands. Meanwhile, a collection of biophysically ill-founded concepts have been considered legitimate, which can now be corrected in the light of recent advances. The relationship of LFPs to their sources is often counterintuitive. For instance, most LFP activity is not local but remote, it may be larger further from rather than close to the source, the polarity does not define its excitatory or inhibitory nature, and the amplitude may increase when source's activity is reduced. As technological developments foster the use of LFPs, the time is now ripe to raise awareness of the need to take into account spatial aspects of these signals and of the errors derived from neglecting to do so.This work was supported by the Spanish Ministry of Economy and Competitiveness (BFU2013-41533R).Peer reviewedPeer Reviewe

Multi-Scale Mathematical Modelling of Brain Networks in Alzheimer's Disease

Perturbations to brain network dynamics on a range of spatial and temporal scales are believed to underpin neurological disorders such as Alzheimer’s disease (AD). This thesis combines quantitative data analysis with tools such as dynamical systems and graph theory to understand how the network dynamics of the brain are altered in AD and experimental models of related pathologies. Firstly, we use a biophysical neuron model to elucidate ionic mechanisms underpinning alterations to the dynamics of principal neurons in the brain’s spatial navigation systems in an animal model of tauopathy. To uncover how synaptic deficits result in alterations to brain dynamics, we subsequently study an animal model featuring local and long-range synaptic degeneration. Synchronous activity (functional connectivity; FC) between neurons within a region of the cortex is analysed using two-photon calcium imaging data. Long-range FC between regions of the brain is analysed using EEG data. Furthermore, a computational model is used to study relationships between networks on these different spatial scales. The latter half of this thesis studies EEG to characterize alterations to macro-scale brain dynamics in clinical AD. Spectral and FC measures are correlated with cognitive test scores to study the hypothesis that impaired integration of the brain’s processing systems underpin cognitive impairment in AD. Whole brain computational modelling is used to gain insight into the role of spectral slowing on FC, and elucidate potential synaptic mechanisms of FC differences in AD. On a finer temporal scale, microstate analyses are used to identify changes to the rapid transitioning behaviour of the brain’s resting state in AD. Finally, the electrophysiological signatures of AD identified throughout the thesis are combined into a predictive model which can accurately separate people with AD and healthy controls based on their EEG, results which are validated on an independent patient cohort. Furthermore, we demonstrate in a small preliminary cohort that this model is a promising tool for predicting future conversion to AD in patients with mild cognitive impairment

Realistic modeling of mesoscopic ephaptic coupling in the human brain

Altres ajuts: The National Institutes of Health (R01HD069776, R01NS073601, R21MH099196, R21 NS082870, R21 NS085491, R21HD07616)Several decades of research suggest that weak electric fields may influence neural processing, including those induced by neuronal activity and proposed as a substrate for a potential new cellular communication system, i.e., ephaptic transmission. Here we aim to model mesoscopic ephaptic activity in the human brain and explore its trajectory during aging by characterizing the electric field generated by cortical dipoles using realistic finite element modeling. Extrapolating from electrophysiological measurements, we first observe that modeled endogenous field magnitudes are comparable to those in measurements of weak but functionally relevant self-generated fields and to those produced by noninvasive transcranial brain stimulation, and therefore possibly able to modulate neuronal activity. Then, to evaluate the role of these fields in the human cortex in large MRI databases, we adapt an interaction approximation that considers the relative orientation of neuron and field to estimate the membrane potential perturbation in pyramidal cells. We use this approximation to define a simplified metric (EMOD1) that weights dipole coupling as a function of distance and relative orientation between emitter and receiver and evaluate it in a sample of 401 realistic human brain models from healthy subjects aged 16-83. Results reveal that ephaptic coupling, in the simplified mesoscopic modeling approach used here, significantly decreases with age, with higher involvement of sensorimotor regions and medial brain structures. This study suggests that by providing the means for fast and direct interaction between neurons, ephaptic modulation may contribute to the complexity of human function for cognition and behavior, and its modification across the lifespan and in response to pathology

Astrocytic modulation of neuronal network oscillations

The synchronization of the neuron’s membrane potential results in the emergence of neuronal oscillations at multiple frequencies that serve distinct physiological functions (e.g. facilitation of synaptic plasticity) and correlate with different behavioural states (e.g. sleep, wakefulness, attention). It has been postulated that at least ten distinct mechanisms are required to cover the large frequency range of neuronal oscillations in the cortex, including variations in the concentration of extracellular neurotransmitters and ions, as well as changes in cellular excitability. However, the mechanism that gears the transition between different oscillatory frequencies is still unknown. Over the past decade, astrocytes have been the focus of much research, mainly due to (1) their close association with synapses forming what is known today as the “tripartite synapse”, which allows them to bidirectionally interact with neurons and modulate synaptic transmission; (2) their syncytium-like activity, as they are electrically coupled via gap junctions and actively communicate through Ca2+ waves; and (3) their ability to regulate neuronal excitability via glutamate uptake and tight control of the extracellular K+ levels via a process termed K+ clearance. In this thesis we hypothesized that astrocytes, in addition to their role as modulators of neuronal excitability, also act as “network managers” that can modulate the overall network oscillatory activity within their spatial domain. To do so, it is proposed that astrocytes fine-tune their K+ clearance capabilities to affect neuronal intrinsic excitability properties and synchronization with other neurons, thus mediating the transitions between neuronal network oscillations at different frequencies. To validate or reject this hypothesis I have investigated the potential role of astrocytes in modulating cortical oscillations at both cellular and network levels, aiming at answering three main research questions: a) what is the impact of alterations in astrocytic K+ clearance mechanisms on cortical networks oscillatory dynamics? b) what specific neuronal properties underlying the generation of neuronal oscillations are affected as a result of impairments in the astrocytic K+ clearance process? and c) what are the bidirectional mechanisms between neurons and astrocytes (i.e. neuromodulators) that specifically affect the K+ clearance process to modulate the network activity output? In the first experimental chapter I used electrophysiological recordings and pharmacological manipulations to dissect the contribution of the different astrocytic K+ clearance mechanisms to the modulation of neuronal network oscillations at multiple frequencies. A key finding was that alterations in membrane properties of layer V pyramidal neurons strongly correlated with the network behaviour following impairments in astrocytic K+ clearance capabilities, depicted as enhanced excitability underlying the amplification of high-frequency oscillations, especially within the beta and gamma range. The second experimental chapter describes a combinatorial approach based on K+-selective microelectrode recordings and optical imaging of K+ ions used to quantitatively determine extracellular K+ changes and to follow the spatiotemporal distribution of K+ ions under both physiological and altered K+ clearance conditions, which affected the K+ clearance rate. The impact of different neuromodulators on astrocytic function is discussed in the third experimental chapter. Using extracellular K+ recordings and Ca2+ imaging I found that some neuromodulators act specifically on astrocytic receptors to affect both K+ clearance mechanisms and Ca2+ signalling, as evidenced by reduced K+ clearance rates and altered evoked Ca2+ signals. Overall, this thesis provides new insights regarding the impact of astrocytic K+ clearance mechanisms on modulating neuronal properties at both cellular and network levels, which in turn imposes alterations on neuronal oscillations that are associated with different behavioural states

Emergence of Spatio-Temporal Pattern Formation and Information Processing in the Brain.

The spatio-temporal patterns of neuronal activity are thought to underlie cognitive functions, such as our thoughts, perceptions, and emotions. Neurons and glial cells, specifically astrocytes, are interconnected in complex networks, where large-scale dynamical patterns emerge from local chemical and electrical signaling between individual network components. How these emergent patterns form and encode for information is the focus of this dissertation. I investigate how various mechanisms that can coordinate collections of neurons in their patterns of activity can potentially cause the interactions across spatial and temporal scales, which are necessary for emergent macroscopic phenomena to arise. My work explores the coordination of network dynamics through pattern formation and synchrony in both experiments and simulations. I concentrate on two potential mechanisms: astrocyte signaling and neuronal resonance properties. Due to their ability to modulate neurons, we investigate the role of astrocytic networks as a potential source for coordinating neuronal assemblies. In cultured networks, I image patterns of calcium signaling between astrocytes, and reproduce observed properties of the network calcium patterning and perturbations with a simple model that incorporates the mechanisms of astrocyte communication. Understanding the modes of communication in astrocyte networks and how they form spatial temporal patterns of their calcium dynamics is important to understanding their interaction with neuronal networks. We investigate this interaction between networks and how glial cells modulate neuronal dynamics through microelectrode array measurements of neuronal network dynamics. We quantify the spontaneous electrical activity patterns of neurons and show the effect of glia on the neuronal dynamics and synchrony. Through a computational approach I investigate an entirely different theoretical mechanism for coordinating ensembles of neurons. I show in a computational model how biophysical resonance shifts in individual neurons can interact with the network topology to influence pattern formation and separation. I show that sub-threshold neuronal depolarization, potentially from astrocytic modulation among other sources, can shift neurons into and out of resonance with specific bands of existing extracellular oscillations. This can act as a dynamic readout mechanism during information storage and retrieval. Exploring these mechanisms that facilitate emergence are necessary for understanding information processing in the brain.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111493/1/lshtrah_1.pd