Szymon Łęski

Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential

Despite its century-old use, the interpretation of local field potentials (LFPs), the low-frequency part of electrical signals recorded in the brain, is still debated. In cortex the LFP appears to mainly stem from transmembrane neuronal currents following synaptic input, and obvious questions regarding the ‘locality’ of the LFP are: What is the size of the signal-generating region, i.e., the spatial reach, around a recording contact? How far does the LFP signal extend outside a synaptically activated neuronal population? And how do the answers depend on the temporal frequency of the LFP signal? Experimental inquiries have given conflicting results, and we here pursue a modeling approach based on a well-established biophysical forward-modeling scheme incorporating detailed reconstructed neuronal morphologies in precise calculations of population LFPs including thousands of neurons. The two key factors determining the frequency dependence of LFP are the spatial decay of the single-neuron LFP contribution and the conversion of synaptic input correlations into correlations between single-neuron LFP contributions. Both factors are seen to give low-pass filtering of the LFP signal power. For uncorrelated input only the first factor is relevant, and here a modest reduction (<50%) in the spatial reach is observed for higher frequencies (>100 Hz) compared to the near-DC () value of about . Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency () LFP power can here be an order of magnitude or more larger than at 60 Hz. Moreover, the low-frequency LFP components have larger spatial reach and extend further outside the active population than high-frequency components. Further, the spatial LFP profiles for such populations typically span the full vertical extent of the dendrites of neurons in the population. Our numerical findings are backed up by an intuitive simplified model for the generation of population LFP.

Differential effects produced by ketamine on oscillatory activity recorded in the rat hippocampus, dorsal striatum and nucleus accumbens

Previously, we showed that NMDA antagonists enhance high-frequency oscillations (130–180 Hz) in the nucleus accumbens. However, whether NMDA antagonists can enhance high-frequency oscillations in other brain regions remains unclear. Here, we used monopolar, bipolar and inverse current source density techniques to examine oscillatory activity in the hippocampus, a region known to generate spontaneous ripples (∼200 Hz), its surrounding tissue, and the dorsal striatum, neuroanatomically related to the nucleus accumbens. In monopolar recordings, ketamine-induced increases in the power of high-frequency oscillations were detected in all structures, although the power was always substantially larger in the nucleus accumbens. In bipolar recordings, considered to remove common-mode input, high-frequency oscillations associated with ketamine injection were not present in the regions we investigated outside the nucleus accumbens. In line with this, inverse current source density showed the greatest changes in current to occur in the vicinity of the nucleus accumbens and a monopolar structure of the generator. We found little spatial localisation of ketamine high-frequency oscillations in other areas. In contrast, sharp-wave ripples, which were well localized to the hippocampus, occurred less frequently after ketamine. Notably, we also found ketamine produced small, but significant, changes in the power of 30–90 Hz gamma oscillations (an increase in the hippocampus and a decrease in the nucleus accumbens).

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.

Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs)

Microelectrode arrays (MEAs), substrate-integrated planar arrays of up to thousands of closely spaced metal electrode contacts, have long been used to record neuronal activity in in vitro brain slices with high spatial and temporal resolution. However, the analysis of the MEA potentials has generally been mainly qualitative. Here we use a biophysical forward-modelling formalism based on the finite element method (FEM) to establish quantitatively accurate links between neural activity in the slice and potentials recorded in the MEA set-up. Then we develop a simpler approach based on the method of images (MoI) from electrostatics, which allows for computation of MEA potentials by simple formulas similar to what is used for homogeneous volume conductors. As we find MoI to give accurate results in most situations of practical interest, including anisotropic slices covered with highly conductive saline and MEA-electrode contacts of sizable physical extensions, a Python software package (ViMEAPy) has been developed to facilitate forward-modelling of MEA potentials generated by biophysically detailed multicompartmental neurons. We apply our scheme to investigate the influence of the MEA set-up on single-neuron spikes as well as on potentials generated by a cortical network comprising more than 3000 model neurons. The generated MEA potentials are substantially affected by both the saline bath covering the brain slice and a (putative) inadvertent saline layer at the interface between the MEA chip and the brain slice. We further explore methods for estimation of current-source density (CSD) from MEA potentials, and find the results to be much less sensitive to the experimental set-up.

Inverse Current-Source Density Method in 3D: Reconstruction Fidelity, Boundary Effects, and Influence of Distant Sources

Estimation of the continuous current-source density in bulk tissue from a finite set of electrode measurements is a daunting task. Here we present a methodology which allows such a reconstruction by generalizing the one-dimensional inverse CSD method. The idea is to assume a particular plausible form of CSD within a class described by a number of parameters which can be estimated from available data, for example a set of cubic splines in 3D spanned on a fixed grid of the same size as the set of measurements. To avoid specificity of particular choice of reconstruction grid we add random jitter to the points positions and show that it leads to a correct reconstruction. We propose different ways of improving the quality of reconstruction which take into account the sources located outside the recording region through appropriate boundary treatment. The efficiency of the traditional CSD and variants of inverse CSD methods is compared using several fidelity measures on different test data to investigate when one of the methods is superior to the others. The methods are illustrated with reconstructions of CSD from potentials evoked by stimulation of a bunch of whiskers recorded in a slab of the rat forebrain on a grid of 4×5×7 positions.

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4×5×7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

miR-132 Regulates Dendritic Spine Structure by Direct Targeting of Matrix Metalloproteinase 9 mRNA

Mir-132 is a neuronal activity-regulated microRNA that controls the morphology of dendritic spines and neuronal transmission. Similar activities have recently been attributed to matrix metalloproteinase-9 (MMP-9), an extrasynaptic protease. In the present study, we provide evidence that miR-132 directly regulates MMP-9 mRNA in neurons to modulate synaptic plasticity. With the use of luciferase reporter system, we show that miR-132 binds to the 3’UTR of MMP-9 mRNA to regulate its expression in neurons. The overexpression of miR-132 in neurons reduces the level of endogenous MMP-9 protein secretion. In synaptoneurosomes, metabotropic glutamate receptor (mGluR)-induced signaling stimulates the dissociation of miR-132 from polyribosomal fractions and shifts it towards the messenger ribonucleoprotein (mRNP)-containing fraction. Furthermore, we demonstrate that the overexpression of miR-132 in the cultured hippocampal neurons from Fmr1 KO mice that have increased synaptic MMP-9 level provokes enlargement of the dendritic spine heads, a process previously implicated in enhanced synaptic plasticity. We propose that activity-dependent miR-132 regulates structural plasticity of dendritic spines through matrix metalloproteinase 9.

Independent Components of Neural Activity Carry Information on Individual Populations

Local field potential (LFP), the low-frequency part of the potential recorded extracellularly in the brain, reflects neural activity at the population level. The interpretation of LFP is complicated because it can mix activity from remote cells, on the order of millimeters from the electrode. To understand better the relation between the recordings and the local activity of cells we used a large-scale network thalamocortical model to compute simultaneous LFP, transmembrane currents, and spiking activity. We used this model to study the information contained in independent components obtained from the reconstructed Current Source Density (CSD), which smooths transmembrane currents, decomposed further with Independent Component Analysis (ICA). We found that the three most robust components matched well the activity of two dominating cell populations: superior pyramidal cells in layer 2/3 (rhythmic spiking) and tufted pyramids from layer 5 (intrinsically bursting). The pyramidal population from layer 2/3 could not be well described as a product of spatial profile and temporal activation, but by a sum of two such products which we recovered in two of the ICA components in our analysis, which correspond to the two first principal components of PCA decomposition of layer 2/3 population activity. At low noise one more cell population could be discerned but it is unlikely that it could be recovered in experiment given typical noise ranges.

A novel automated behavioral test battery assessing cognitive rigidity in two genetic mouse models of autism

Repetitive behaviors are a key feature of many pervasive developmental disorders, such as autism. As a heterogeneous group of symptoms, repetitive behaviors are conceptualized into two main subgroups: sensory/motor (lower-order) and cognitive rigidity (higher-order). Although lower-order repetitive behaviors are measured in mouse models in several paradigms, so far there have been no high-throughput tests directly measuring cognitive rigidity. We describe a novel approach for monitoring repetitive behaviors during reversal learning in mice in the automated IntelliCage system. During the reward-motivated place preference reversal learning, designed to assess cognitive abilities of mice, visits to the previously rewarded places were recorded to measure cognitive flexibility. Thereafter, emotional flexibility was assessed by measuring conditioned fear extinction. Additionally, to look for neuronal correlates of cognitive impairments, we measured CA3-CA1 hippocampal long term potentiation (LTP). To standardize the designed tests we used C57BL/6 and BALB/c mice, representing two genetic backgrounds, for induction of autism by prenatal exposure to the sodium valproate. We found impairments of place learning related to perseveration and no LTP impairments in C57BL/6 valproate-treated mice. In contrast, BALB/c valproate-treated mice displayed severe deficits of place learning not associated with perseverative behaviors and accompanied by hippocampal LTP impairments. Alterations of cognitive flexibility observed in C57BL/6 valproate-treated mice were related to neither restricted exploration pattern nor to emotional flexibility. Altogether, we showed that the designed tests of cognitive performance and perseverative behaviors are efficient and highly replicable. Moreover, the results suggest that genetic background is crucial for the behavioral effects of prenatal valproate treatment.

GSK-3β and MMP-9 Cooperate in the Control of Dendritic Spine Morphology

Changes in the morphology of dendritic spines are prominent during learning and in different neurological and neuropsychiatric diseases, including those in which glycogen synthase kinase-3β (GSK-3β) has been implicated. Despite much evidence of the involvement of GSK-3β in functional synaptic plasticity, it is unclear how GSK-3β controls structural synaptic plasticity (i.e., the number and shape of dendritic spines). In the present study, we used two mouse models overexpressing and lacking GSK-3β in neurons to investigate how GSK-3β affects the structural plasticity of dendritic spines. Following visualization of dendritic spines with DiI dye, we found that increasing GSK-3β activity increased the number of thin spines, whereas lacking GSK-3β increased the number of stubby spines in the dentate gyrus. Under conditions of neuronal excitation, increasing GSK-3β activity caused higher activity of extracellularly acting matrix metalloproteinase-9 (MMP-9), and MMP inhibition normalized thin spines in GSK-3β overexpressing mice. Administration of the nonspecific GSK-3β inhibitor lithium in animals with active MMP-9 and animals lacking MMP-9 revealed that GSK-3β and MMP-9 act in concert to control dendritic spine morphology. Altogether, our data demonstrate that the dysregulation of GSK-3β activity has dramatic consequences on dendritic spine morphology, implicating MMP-9 as a mediator of GSK-3β-induced synaptic alterations.