On the Estimation of Population-Specific Synaptic Currents from Laminar Multielectrode Recordings

Multielectrode array recordings of extracellular electrical field potentials along the depth axis of the cerebral cortex are gaining popularity as an approach for investigating the activity of cortical neuronal circuits. The low-frequency band of extracellular potential, i.e., the local field potential (LFP), is assumed to reflect synaptic activity and can be used to extract the laminar current source density (CSD) profile. However, physiological interpretation of the CSD profile is uncertain because it does not disambiguate synaptic inputs from passive return currents and does not identify population-specific contributions to the signal. These limitations prevent interpretation of the CSD in terms of synaptic functional connectivity in the columnar microcircuit. Here we present a novel anatomically informed model for decomposing the LFP signal into population-specific contributions and for estimating the corresponding activated synaptic projections. This involves a linear forward model, which predicts the population-specific laminar LFP in response to synaptic inputs applied at different positions along each population and a linear inverse model, which reconstructs laminar profiles of synaptic inputs from laminar LFP data based on the forward model. Assuming spatially smooth synaptic inputs within individual populations, the model decomposes the columnar LFP into population-specific contributions and estimates the corresponding laminar profiles of synaptic input as a function of time. It should be noted that constant synaptic currents at all positions along a neuronal population cannot be reconstructed, as this does not result in a change in extracellular potential. However, constraining the solution using a priori knowledge of the spatial distribution of synaptic connectivity provides the further advantage of estimating the strength of active synaptic projections from the columnar LFP profile thus fully specifying synaptic inputs

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

Frequency dependence of signal power and spatial reach of the local field potential

The first recording of electrical potential from brain activity was reported already in 1875, but still the interpretation of the signal is debated. To take full advantage of the new generation of microelectrodes with hundreds or even thousands of electrode contacts, an accurate quantitative link between what is measured and the underlying neural circuit activity is needed. Here we address the question of how the observed frequency dependence of recorded local field potentials (LFPs) should be interpreted. By use of a well-established biophysical modeling scheme, combined with detailed reconstructed neuronal morphologies, we find that correlations in the synaptic inputs onto a population of pyramidal cells may significantly boost the low-frequency components of the generated LFP. We further find that these low-frequency components may be less `local' than the high-frequency LFP components in the sense that (1) the size of signal-generation region of the LFP recorded at an electrode is larger and (2) that the LFP generated by a synaptically activated population spreads further outside the population edge due to volume conduction

Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings

The recent development of large multielectrode recording arrays has made it affordable for an increasing number of laboratories to record from multiple brain regions simultaneously. The development of analytical tools for array data, however, lags behind these technological advances in hardware. In this paper, we present a method based on forward modeling for estimating current source density from electrophysiological signals recorded on a two-dimensional grid using multi-electrode rectangular arrays. This new method, which we call two-dimensional inverse Current Source Density (iCSD 2D), is based upon and extends our previous one- and three-dimensional techniques. We test several variants of our method, both on surrogate data generated from a collection of Gaussian sources, and on model data from a population of layer 5 neocortical pyramidal neurons. We also apply the method to experimental data from the rat subiculum. The main advantages of the proposed method are the explicit specification of its assumptions, the possibility to include system-specific information as it becomes available, the ability to estimate CSD at the grid boundaries, and lower reconstruction errors when compared to the traditional approach. These features make iCSD 2D a substantial improvement over the approaches used so far and a powerful new tool for the analysis of multielectrode array data. We also provide a free GUI-based MATLAB toolbox to analyze and visualize our test data as well as user datasets

Laminar analysis of the slow wave activity in the somatosensory cortex of anesthetized rats.

Rhythmic slow waves characterize brain electrical activity during natural deep sleep and under anesthesia, reflecting the synchronous membrane potential fluctuations of neurons in the thalamocortical network. Strong evidence indicates that the neocortex plays an important role in the generation of slow wave activity (SWA), however, contributions of individual cortical layers to the SWA generation are still unclear. The anatomically correct laminar profiles of SWA were revealed under ketamine/xylazine anesthesia, with combined local field potential recordings, multiple-unit activity (MUA), current source density (CSD) and time-frequency analyses precisely co-registered with histology. The up-state related negative field potential wave showed the largest amplitude in layer IV, the CSD was largest in layers I and III, while MUA was maximal in layer V, suggesting spatially dissociated firing and synaptic/transmembrane processes in the rat somatosensory cortex. Up-state related firing could start in virtually any layers (III-VI) of the cortex, but were most frequently initiated in layer V. However, in a subset of experiments, layer IV was considerably active in initiating up-state related MUA even in the absence of somatosensory stimulation. Somatosensory stimulation further strengthened up-state initiation in layer IV. Our results confirm that cortical layer V firing may have a major contribution to the up-state generation of ketamine/xylazine-induced SWA, however, thalamic influence through the thalamorecipient layer IV can also play an initiating role, even in the absence of sensory stimulation. This article is protected by copyright. All rights reserved

Estimation of Thalamocortical and Intracortical Network Models from Joint Thalamic Single-Electrode and Cortical Laminar-Electrode Recordings in the Rat Barrel System

A new method is presented for extraction of population firing-rate models for both thalamocortical and intracortical signal transfer based on stimulus-evoked data from simultaneous thalamic single-electrode and cortical recordings using linear (laminar) multielectrodes in the rat barrel system. Time-dependent population firing rates for granular (layer 4), supragranular (layer 2/3), and infragranular (layer 5) populations in a barrel column and the thalamic population in the homologous barreloid are extracted from the high-frequency portion (multi-unit activity; MUA) of the recorded extracellular signals. These extracted firing rates are in turn used to identify population firing-rate models formulated as integral equations with exponentially decaying coupling kernels, allowing for straightforward transformation to the more common firing-rate formulation in terms of differential equations. Optimal model structures and model parameters are identified by minimizing the deviation between model firing rates and the experimentally extracted population firing rates. For the thalamocortical transfer, the experimental data favor a model with fast feedforward excitation from thalamus to the layer-4 laminar population combined with a slower inhibitory process due to feedforward and/or recurrent connections and mixed linear-parabolic activation functions. The extracted firing rates of the various cortical laminar populations are found to exhibit strong temporal correlations for the present experimental paradigm, and simple feedforward population firing-rate models combined with linear or mixed linear-parabolic activation function are found to provide excellent fits to the data. The identified thalamocortical and intracortical network models are thus found to be qualitatively very different. While the thalamocortical circuit is optimally stimulated by rapid changes in the thalamic firing rate, the intracortical circuits are low-pass and respond most strongly to slowly varying inputs from the cortical layer-4 population

The hippocampal CA3 region can generate two distinct types of sharp wave-ripple complexes, in vitro.

Hippocampal sharp wave-ripples (SPW-Rs) occur during slow wave sleep and behavioral immobility and are thought to play an important role in memory formation. We investigated the cellular and network properties of SPW-Rs with simultaneous laminar multielectrode and intracellular recordings in a rat hippocampal slice model, using physiological bathing medium. Spontaneous SPW-Rs were generated in the dentate gyrus (DG), CA3 and CA1 regions. These events were characterized by a local field potential gradient (LFPg) transient, increased fast oscillatory activity and increased multiple unit activity (MUA). Two types of SPW-Rs were distinguished in the CA3 region based on their different LFPg and current source density (CSD) pattern. Type 1 (T1) displayed negative LFPg transient in the pyramidal cell layer, and the associated CSD sink was confined to the proximal dendrites. Type 2 (T2) SPW-Rs were characterized by positive LFPg transient in the cell layer, and showed CSD sinks involving both the apical and basal dendrites. In both types, consistent with the somatic CSD source, only a small subset of CA3 pyramidal cells fired, most pyramidal cells were hyperpolarized, while most interneurons increased firing rate before the LFPg peak. Different neuronal populations, with different proportions of pyramidal cells and distinct subsets of interneurons were activated during T1 and T2 SPW-Rs. Activation of specific inhibitory cell subsets - with the possible leading role of perisomatic interneurons - seems to be crucial to synchronize distinct ensembles of CA3 pyramidal cells finally resulting in the expression of different SPW-R activities. This suggests that the hippocampus can generate dynamic changes in its activity stemming from the same excitatory and inhibitory circuits, and so, might provide the cellular and network basis for an input-specific and activity-dependent information transmission. (c) 2014 Wiley Periodicals, Inc

MAPPING LOW-FREQUENCY FIELD POTENTIALS IN BRAIN CIRCUITS WITH HIGH-RESOLUTION CMOS ELECTRODE ARRAY RECORDINGS

Neurotechnologies based on microelectronic active electrode array devices are on the way to provide the capability to record electrophysiological neural activity from a thousands of closely spaced microelectrodes. This generates increasing volumes of experimental data to be analyzed, but also offers the unprecedented opportunity to observe bioelectrical signals at high spatial and temporal resolutions in large portions of brain circuits. The overall aim of this PhD was to study the application of high-resolution CMOS-based electrode arrays (CMOS-MEAs) for electrophysiological experiments and to investigate computational methods adapted to the analysis of the electrophysiological data generated by these devices. A large part of my work was carried out on cortico-hippocampal brain slices by focusing on the hippocampal circuit. In the history of neuroscience, a major technological advance for hippocampal research, and also for the field of neurobiology, was the development of the in vitro hippocampal slice preparation. Neurobiological principles that have been discovered from work on in vitro hippocampal preparations include, for instance, the identification of excitatory and inhibitory synapses and their localization, the characterization of transmitters and receptors, the discovery of long-term potentiation (LTP) and long-term depression (LTD) and the study of oscillations in neuronal networks. In this context, an initial aim of my work was to optimize the preparation and maintenance of acute cortico-hippocampal brain slices on planar CMOS-MEAs. At first, I focused on experimental methods and computational data analysis tools for drug-screening applications based on LTP quantifications. Although the majority of standard protocols still use two electrodes platforms for quantifying LTP, in my PhD I investigate the potential advantages of recording the electrical activity from many electrodes to spatiotemporally characterized electrically induced responses. This work also involved the collaboration with 3Brain AG and a CRO involved in drug-testing, and led to a software tool that was licensed for developing its exploitation. In a second part of my work I focused on exploiting the recording resolution of planar CMOS-MEAs to study the generation of sharp wave ripples (SPW-Rs) in the hippocampal circuit. This research activity was carried out also by visiting the laboratory of Prof. A. Sirota (Ludwig Maximilians University, Munich). In addition to set-up the experimental conditions to record SPW-Rs from planar CMOS-MEAs integrating 4096 microelectrodes, I also explored the implementation of a data analysis pipeline to identify spatiotemporal features that might characterize different type of in-vitro generated SPW-R events. Finally, I also contributed to the initial implementation of high-density implantable CMOS-probes for in-vivo electrophysiology with the aim of evaluating in vivo the algorithms that I developed and investigated on brain slices. With this aim, in the last period of my PhD I worked on the development of a Graphical User Interface for controlling active dense CMOS probes (or SiNAPS probes) under development in our laboratory. I participated to preliminary experimental recordings using 4-shank CMOS-probes featuring 1024 simultaneously recording electrodes and I contributed to the development of a software interface for executing these experiments