Revealing the distribution of transmembrane currents along the dendritic tree of a neuron from extracellular recordings.

Revealing the current source distribution along the neuronal membrane is a key step on the way to understanding neural computations, however, the experimental and theoretical tools to achieve sufficient spatiotemporal resolution for the estimation remain to be established. Here we address this problem using extracellularly recorded potentials with arbitrarily distributed electrodes for a neuron of known morphology. We use simulations of models with varying complexity to validate the proposed method and to give recommendations for experimental applications. The method is applied to in vitro data from rat hippocampus

Estimation of neural network model parameters from local field potentials (LFPs)

publishedVersio

Blind source separation applied to the analysis of rat somatosensory evoked response

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, 2016.Na última década, o interesse por potenciais de campo local corticais foi renovado. Estes são potenciais elétricos adquiridos de dentro do córtex, geralmente com eletrodos linearmente espaçados inseridos perpendicularmente à superfície do córtex. A ferramenta padrão para a análise de potenciais de campo local de múltiplos eletrodos tem sido análise por densidade de fonte de corrente. Esta permite estimar a corrente por unidade de volume fluindo para dentro (fonte) ou para fora (sumidouro) do espaço extracelular ao redor de um eletrodo. A análise por densidade de fonte de corrente permite estudar conectividade de populações de diferentes camadas do córtex pelo posicionamento das fontes e dos sumidouros de corrente. Neste trabalho, várias técnicas de separação cega de fonte são aplicadas à potenciais evocados adquiridos do córtex somatosensorial de um rato para explicitar associações entre fontes e sumidouros de corrente. As técnicas utilizadas foram análise de componentes principais, análise de componentes independentes, análise de componentes independentes espaço-temporal, análise de componentes independentes triplo-N para misturas convolutivas, e análise de fatores paralelos. Para utilizar com sucesso análise de fatores paralelos com estes dados, uma solução alternativa teve de ser proposta que consiste em aproximar as fases das componentes tridimensionais do tensor de eletrodos por tempo por frequência a serem iguais às do tensor original. Isto permite reconstruir sinais no tempo a partir das componentes e ainda utilizar restrição de não negatividade.In the past decade, the interest on cortical Local Field Potentials (LFPs) has been renewed. These are electric potentials recorded from inside the cortex, generally with linearly spaced electrodes inserted perpendicularly to the cortical surface. The standard tool for the analysis of multielectrode Local Field Potentials has been the Current Source Density (CSD) analysis. This allows to estimate the current per unit volume flowing in (source) or out (sink) the extracellular space around an electrode. The CSD analysis allows to study connectivity of populations from different layers of the cortex by the positioning of the current sources and sinks. In this work several Blind Signal Separation techniques are applied to Evoked Potentials (EP) recorded from the somatosensory cortex of a rat to elicit associations between the current sources and sinks. The techniques used were Principal Component Analysis (PCA), Independent Component Analysis (ICA), Spatiotemporal ICA (stICA), Triple-N ICA for Convolutive Mixtures (TRINICON) and Parallel Factor Analysis (PARAFAC). To successfully use PARAFAC with this data, a workaround had to be proposed that consists of approximating the phases of the three-way components of the array of electrodes by time by frequency to be equal to the phases of the original array. This allows to reconstruct time signals from each components and still use nonnegativity constraints

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems

Discovering optimal features for neuron-type identification from extracellular recordings

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification

Biophysics-based modeling and data analysis of local field potential signal

Understanding the neurophysiological mechanisms of information processing within and across brain regions has always been a fundamental and challenging topic in neuroscience. Considerable works in the brain connectome and transcriptome have laid a profound foundation for understanding brain function by its structure. At the same time, the recent advance in recording techniques allows us to probe the nonstationary brain activity from various spatial and temporal scales. However, how to effectively build the dialogue between the anatomical structure and the dynamical brain signal still needs to be solved. To tackle the problem, we explore interpreting electrophysiology signals with mechanistic models. In chapter 2 we first segregate high-coherent brain signals into different pathways and then connect their dynamics to synaptic properties. Based on a state space model of LFP generation, we explore several preprocessing methods to bias the signal to the synaptic inputs and enhance the separatability of pathway-specific contributions. The separated sources are more reliable with the preprocessing methods, especially in highly coherent states, e.g., awake running. With reliably separated pathways, we further studied their synaptic properties and explored the local directional connections in the hippocampus. The estimated synaptic time constant and pathway connection agrees with well-established anatomical studies. In chapter 3 we explore establishing a simple model to capture the impulse response of passive neurons with detailed dendritic morphology. We validate Green’s function methods based on compartmentalized models by comparing them to numerical simulations and analytical solutions on continuous neuron membrane potentials. A parameterized model based on laminar Green’s function is further developed and helps to infer the anatomical properties, like the input current distribution and cell position, from their spatiotemporal response patterns. The effect of cell position and template are examed. Based on the model of chapter 3, we use the biophysical possible impulse response profile to regularize the source separation in the frequency domain in chapter 4. The components from different frequencies are clustered according to the same latent input distributions. The source separation in better-separated frequency bins from the same pathway helps separation in other highly contaminated frequencies. The optimization is formulated as a probabilistic model to maximize the negentropy as well as spatial likelihood. Similar to dipole approximation for EEG signals, Green’s function method provides an effective approximation to capture biologically possible spatiotemporal patterns and helps to guide the separation. We validated the method on real data with optogenetic stimulation. In chapter 5 we further separate the far-field signals from the local pathway activities according to their physiological properties. We propose a pipeline to reliably separate and automatically detect far-field signal components. Based on this, a toolbox is provided to remove the EMG artifacts and assess the cleaning performance. In the free-running animals, we show that EMG artifacts shadow the high-frequency oscillatory events detection, and EMG cleaning rescues this effect. Overall, this thesis explored multiple possibilities to incorporate neurophysiology knowledge to understand and model the electrical field potential signals.Das Verständnis der neurophysiologischen Mechanismen der Informationsverarbeitung innerhalb und zwischen Gehirnregionen war schon immer ein grundlegendes und herausforderndes Thema in den Neurowissenschaften. Weitreichende Arbeiten zum Konnektom und Transkriptom des Gehirns haben eine Grundlage für das Verständnis der Gehirnfunktion gelegt. Des Weiteren ermöglicht uns der derzeitige Fortschritt in der Aufnahmetechnik, die nicht stationäre Gehirnaktivität auf verschiedenen räumlichen und zeitlichen Skalen zu untersuchen. Wie jedoch die anatomischen Strukturen und die dynamischen Gehirnsignal effektiv zusammen wirken können, muss jedoch noch gelöst werden. Um dieses Problem anzugehen, untersuchen wir die Interpretation elektrophysiologischer Signale mit mechanistischen Modellen. In Kapitel 2 trennen wir zunächst die hochkohärenten Gehirnsignale in verschiedene Leitungsbahnen und verbinden dann die Dynamik mit synaptischen Eigenschaften. Basierend auf einem Zustandsraummodell zur Erzeugung lokaler Feldpotentiale (LFP) untersuchen wir verschiedene Vorverarbeitungsmethoden, die die Signale bestmöglich auf die synaptischen Eingangsströme ausrichten und die Trennbarkeit von leitungsbahnspezifischen Beiträgen verbessert. Die Trennung der Signalquellen ist durch das Vorverarbeitungsverfahren insbesondere während hochkohärenter Verhaltenszustände (z. B. laufen im Wachzustand) zuverlässiger. Mit zuverlässig getrennten Leitungsbahnen konnten wir die entsprechenden synaptischen Eigenschaften weiter untersuchen und die lokalen gerichteten Verbindungen im Hippocampus untersuchen. Die geschätzte synaptische Zeitkonstante und die Verbindungen der Leitungsbahnen stimmen mit etablierten anatomischen Studien überein. In Kapitel 3 untersuchen wir die Erstellung eines einfachen Modells zur Beschreibung der Impulsantwort passiver Neuronen mit detaillierter dendritischer Morphologie. Wir validieren Greensche Funktionsmethoden basierend auf kompartimentierten Modellen, indem wir sie mit numerischen Simulationen und analytischen Lösungen des kontinuierlichen Membranpotentials von Neuronen vergleichen. Ein parametrisiertes Modell, das auf der laminaren Greenschen Funktion basiert, wird weiterentwickelt. Es hilft dabei, die anatomischen Eigenschaften - die Verteilung des Eingangsstroms und die Zellposition - aus ihren raumzeitlichen Reaktionsmustern abzuleiten. Die Auswirkung der Zellposition und des Templates werden untersucht. Basierend auf dem Modell aus Kapitel 3 verwenden wir in Kapitel 4 das biophysikalisch mögliche Profil der Impulsantwort, um die Quellentrennung im Frequenzbereich festzulegen. Die Komponenten verschiedener Frequenzen werden nach derselben latenten Eingangsverteilungen geclustert. Die Quellentrennung in besser getrennten Frequenzbereichen derselben Leitungsbahn hilft bei der Quelltrennung in anderen stark kontaminierten Frequenzbereichen. Die Optimierung wird als probabilistisches Modell formuliert, um sowohl die Negentropie als auch die räumliche Wahrscheinlichkeit zu maximieren. Ähnlich wie die Dipolnäherungen für EEG-Signale bietet die Greensche Funktionsmethode eine effektive Annäherung, um biologisch mögliche raumzeitliche Muster zu erfassen, und hilft, die Quellen zu trennen. Wir haben die Methode an realen Daten mit optogenetischer Stimulation validiert. Im Kapitel 5 trennen wir weiter die Fernfeldsignale von den Signalen der lokalen Leitungsbahnen nach ihren physiologischen Eigenschaften. Wir schlagen eine Methode vor, die es erlaubt, Fernfeld-Signalkomponenten zuverlässig von lokaler Aktivitaet zu trennen und automatisch zu erkennen. Es wird eine Toolbox bereitgestellt, die EMG-Artefakte entfernt und die bereinigten Signale bewertet. In Ableitungen von freilaufenden Tieren zeigen wir, dass EMG-Artefakte die Erkennung von hochfrequenten Oszillationen beeintraechtigt, aber nach der Bereinigung des EMG-Signals erkannt werden kann. Insgesamt untersucht diese Dissertation mehrere Möglichkeiten die elektrischen Feldpotentiale neuronaler Aktivität unter Einbeziehung neurophysiologischen Wissens zu modellieren und zu verstehen

Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex.

Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from separate cortical populations. However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in the MUA (multi-unit activity; high-frequency part of the signal). In the presently developed generalized laminar population analysis (gLPA) the set of basis functions accounting for the LFP data is extended compared to the original LPA, thus allowing for a better fit of the model to experimental data. This enhances the risk for overfitting, however, and we therefore tested various versions of gLPA on virtual LFP data in which we knew the ground truth. These synthetic data were generated by biophysical forward-modeling of electrical signals from network activity in the comprehensive, and well-known, thalamocortical network model developed by Traub and coworkers. The results for the Traub model imply that while the laminar components extracted by the original LPA method overall are in fair agreement with the ground-truth laminar components, the results may be improved by use of gLPA method with two (gLPA-2) or even three (gLPA-3) postsynaptic LFP kernels per laminar population