Clemens Boucsein

Astrocyte Ca2+ waves trigger responses in microglial cells in brain slices

Pathologic impacts in the brain lead to a widespread activation of microglial cells far beyond the site of injury. Here, we demonstrate that glial Ca2+ waves can trigger responses in microglial cells. We elicited Ca2+ waves in corpus callosum glial cells by electrical stimulation or local adenosine triphosphate (ATP) ejection in acute brain slices. Macroglial cells, but not microglia, were bulk‐loaded with Ca2+‐sensitive dyes. Using a transgenic animal in which astrocytes were labeled by the enhanced green fluorescence protein (EGFP) allowed us to identify the reacting cell populations: the wave activated a Ca2+ response in both astrocytes and non‐astrocytic glial cells and spread over hundreds of micrometers even into the adjacent cortical and ventricular cell layers. Regenerative ATP release and subsequent activation of metabotropic purinergic receptors caused the propagation of the glial Ca2+ wave: the wave was blocked by the purinergic receptor antagonist Reactive Blue 2 and was not affected by the gap junction blocker octanol, but enhanced in Ca2+ free saline. To test whether microglial cells respond to the wave, microglial cells were labeled with a dye‐coupled lectin and membrane currents were recorded with the patch‐clamp technique. When the wave passed by, a current with the characteristics of a purinergic response was activated. Thus, Ca2+ waves in situ are not restricted to astrocytic cells, but broadly activate different glial cell types.

Purinergic receptors on microglial cells: functional expression in acute brain slices and modulation of microglial activation in vitro

Microglial cells are the pathologic sensors in the brain. ATP released from damaged cells is a candidate for signalling neural injury to microglia. Moreover, ATP is an extracellular messenger for propagating astrocyte activity in the form of Ca2+ waves. To test for the functional expression of purinoreceptors in microglial cells we employed the patch‐clamp technique in acute slices of adult mouse brain. ATP triggered a nonselective cationic and a K+ current. Pharmacological screening with purinergic ligands indicated the presence of P2Y1 and P2Y2/4 receptors linked to the activation of a K+ current and P2X receptors, including P2X7, linked to the activation of a nonselective cationic current. These findings suggest that microglial cells in situ express different purinergic receptors with distinct sensitivity and functional coupling. To test for the involvement of purinoreceptors in microglial activation, we stimulated cultured microglial cells with lipopolysaccharide and measured the release of tumour necrosis factor α, interleukin‐6, interleukin‐12 and macrophage inflammatory protein 1α, induction of K+ outward currents and nitric oxide release. All these parameters were reduced in the presence of purinergic ligands, indicating that purinergic receptor activation attenuated indicators of microglial activation.

Measurement of variability dynamics in cortical spike trains

We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV(2)) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5-10 spikes on average. The practical difficulty of measuring the CV(2) in rate modulated data can be overcome by a simple procedure of spike train demodulation which was tested in numerical simulations and in real spike trains. We propose to test neuronal spike trains for deviations from the null-hypothesis FF=CV(2). We show that cortical pyramidal neurons, recorded under controlled stationary input conditions in vitro, comply with this assumption. Performing a time-resolved joint analysis of CV(2) and FF of a single unit recording from the motor cortex of a behaving monkey we demonstrate how the dynamic change of their quantitative relation can be interpreted with respect to neuron intrinsic and extrinsic factors that influence cortical variability in vivo. Finally, we discuss the effect of several additional factors such as serial interval correlation and refractory period on the empiric relation of FF and CV(2).

Electrophysiological properties of microglial cells in normal and pathologic rat brain slices

Microglial cells serve as pathologic sensors of the brain. They are highly abundant in all regions of the central nervous system (CNS) and are characterized by a ramified morphology within the normal tissue. In the present study, we have developed a procedure to study the membrane properties of identified, in situ microglia in acutely isolated brain slices from rat cortex, striatum and facial nucleus. Unlike the well characterized cultured microglial cells, ramified microglia of the slice are characterized by little, if any, voltage‐gated membrane currents and a very low membrane potential. They are thus distinct from neurons, other glial cells and nonbrain macrophages. To study the consequences of microglial activation on the membrane channel pattern, we compared cells in the normal facial nucleus and at defined times after facial nerve axotomy. Within 12 h of axotomy, microglial cells expressed a prominent inward rectifier current and thus acquired the physiological properties of cultured microglia. Within 24 h of the lesion, the cells expressed an additional outward current, which is typical for lipopolysaccharide (LPS)‐activated microglia in vitro. Seven days after the lesion, at a time of major regenerative processes in the facial nucleus, the physiological properties of microglial cells had reverted to those present prior to the pathological event. In conclusion: (i) ramified microglial cells represent a physiologically unique population of cells in the brain; (ii) are distinct from their cultured counterparts; and (iii), undergo a defined pattern of physiological states in the course of pathologic events.

An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.

Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround

Current concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume. Here, we discuss recent findings on the signal integration from horizontal inputs, showing that they could serve as a substrate for reliable and temporally precise signal propagation. Quantification of connection probabilities and parameters of synaptic physiology as a function of lateral distance indicates that horizontal projections constitute a considerable fraction, if not the majority, of inputs from within the cortical network. Taking these non-local horizontal inputs into account may dramatically change our current view on cortical information processing.

Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro

Stationary spiking of single neurons is often modelled by a renewal point process. Here, we tested the underlying model assumption that the inter-spike intervals are mutually independent by analyzing stationary spike train recordings from individual rat neocortical neurons in vivo and in vitro. All neurons exhibited moderate (in vivo) or weak (in vitro) negative first order serial correlation of neighboring intervals which was found to be significant in most cases. No significant higher order serial correlations were detected. The observed negative correlation lead to a strong reduction of the spike count variability by about 30% in vivo.

Dynamical Response Properties of Neocortical Neuron Ensembles: Multiplicative versus Additive Noise

To understand the mechanisms of fast information processing in the brain, it is necessary to determine how rapidly populations of neurons can respond to incoming stimuli in a noisy environment. Recently, it has been shown experimentally that an ensemble of neocortical neurons can track a time-varying input current in the presence of additive correlated noise very fast, up to frequencies of several hundred hertz. Modulations in the firing rate of presynaptic neuron populations affect, however, not only the mean but also the variance of the synaptic input to postsynaptic cells. It has been argued that such modulations of the noise intensity (multiplicative modulation) can be tracked much faster than modulations of the mean input current (additive modulation). Here, we compare the response characteristics of an ensemble of neocortical neurons for both modulation schemes. We injected sinusoidally modulated noisy currents (additive and multiplicative modulation) into layer V pyramidal neurons of the rat somatosensory cortex and measured the trial and ensemble-averaged spike responses for a wide range of stimulus frequencies. For both modulation paradigms, we observed low-pass behavior. The cutoff frequencies were markedly high, considerably higher than the average firing rates. We demonstrate that modulations in the variance can be tracked significantly faster than modulations in the mean input. Extremely fast stimuli (up to 1 kHz) can be reliably tracked, provided the stimulus amplitudes are sufficiently high.

Precisely timed signal transmission in neocortical networks with reliable intermediate-range projections

The mammalian neocortex has a remarkable ability to precisely reproduce behavioral sequences or to reliably retrieve stored information. In contrast, spiking activity in behaving animals shows a considerable trial-to-trial variability and temporal irregularity. The signal propagation and processing underlying these conflicting observations is based on fundamental neurophysiological processes like synaptic transmission, signal integration within single cells, and spike formation. Each of these steps in the neuronal signaling chain has been studied separately to a great extend, but it has been difficult to judge how they interact and sum up in active sub-networks of neocortical cells. In the present study, we experimentally assessed the precision and reliability of small neocortical networks consisting of trans-columnar, intermediate-range projections (200–1000 μm) on a millisecond time-scale. Employing photo-uncaging of glutamate in acute slices, we activated a number of distant presynaptic cells in a spatio-temporally precisely controlled manner, while monitoring the resulting membrane potential fluctuations of a postsynaptic cell. We found that signal integration in this part of the network is highly reliable and temporally precise. As numerical simulations showed, the residual membrane potential variability can be attributed to amplitude variability in synaptic transmission and may significantly contribute to trial-to-trial output variability of a rate signal. However, it does not impair the temporal accuracy of signal integration. We conclude that signals from intermediate-range projections onto neocortical neurons are propagated and integrated in a highly reliable and precise manner, and may serve as a substrate for temporally precise signal transmission in neocortical networks.

Challenges of understanding brain function by selective modulation of neuronal subpopulations

Neuronal networks confront researchers with an overwhelming complexity of interactions between their elements. A common approach to understanding neuronal processing is to reduce complexity by defining subunits and infer their functional role by selectively modulating them. However, this seemingly straightforward approach may lead to confusing results if the network exhibits parallel pathways leading to recurrent connectivity. We demonstrate limits of the selective modulation approach and argue that, even though highly successful in some instances, the approach fails in networks with complex connectivity. We argue to refine experimental techniques by carefully considering the structural features of the neuronal networks involved. Such methods could dramatically increase the effectiveness of selective modulation and may lead to a mechanistic understanding of principles underlying brain function.