PyMICE: APython library for analysis of IntelliCage data.

IntelliCage is an automated system for recording the behavior of a group of mice housed together. It produces rich, detailed behavioral data calling for new methods and software for their analysis. Here we present PyMICE, a free and open-source library for analysis of IntelliCage data in the Python programming language. We describe the design and demonstrate the use of the library through a series of examples. PyMICE provides easy and intuitive access to IntelliCage data, and thus facilitates the possibility of using numerous other Python scientific libraries to form a complete data analysis workflow.

Autophosphorylation of alpha isoform of calcium/calmodulin-dependent kinase II regulates alcohol addiction-related behaviors.

The development of addiction is associated with a dysregulation of glutamatergic transmission in the brain reward circuit. α isoform of calcium/calmodulin-dependent kinase II (αCaMKII) is one of the key proteins that regulates structural and functional plasticity of glutamatergic synapses. αCaMKII activity can be controlled by the autophosphorylation of threonine 286. The role of this autophosphorylation in the regulation of addiction-related behaviors has been proposed but is still poorly understood. Here, using αCaMKII autophosphorylation-deficient mutant mice (T286A), we show that, in comparison with wild-type animals, they are less resistant to high doses of alcohol and do not show psychostimulant response neither to alcohol injections nor during voluntary alcohol drinking. T286A mutants are also less prone to develop alcohol addiction-related behaviors including an increased motivation for alcohol, persistent alcohol seeking during withdrawal and alcohol consumption on relapse. Finally, we demonstrate that αCaMKII autophosphorylation regulates also alcohol-induced remodeling of glutamatergic synapses in the hippocampus and amygdala. In conclusion, our data suggest that αCaMKII autophosphorylation-dependent remodeling of glutamatergic synapses is a plausible mechanism for the regulation of the alcohol addiction-related behaviors.

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 ([Formula: see text]) value of about [Formula: see text]. Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency ([Formula: see text]) 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.

Reciprocal inhibition and slow calcium decay in perigeniculate interneurons explain changes of spontaneous firing of thalamic cells caused by cortical inactivation.

The role of cortical feedback in the thalamocortical processing loop has been extensively investigated over the last decades. With an exception of several cases, these searches focused on the cortical feedback exerted onto thalamo-cortical relay (TC) cells of the dorsal lateral geniculate nucleus (LGN). In a previous, physiological study, we showed in the cat visual system that cessation of cortical input, despite decrease of spontaneous activity of TC cells, increased spontaneous firing of their recurrent inhibitory interneurons located in the perigeniculate nucleus (PGN). To identify mechanisms underlying such functional changes we conducted a modeling study in NEURON on several networks of point neurons with varied model parameters, such as membrane properties, synaptic weights and axonal delays. We considered six network topologies of the retino-geniculo-cortical system. All models were robust against changes of axonal delays except for the delay between the LGN feed-forward interneuron and the TC cell. The best representation of physiological results was obtained with models containing reciprocally connected PGN cells driven by the cortex and with relatively slow decay of intracellular calcium. This strongly indicates that the thalamic reticular nucleus plays an essential role in the cortical influence over thalamo-cortical relay cells while the thalamic feed-forward interneurons are not essential in this process. Further, we suggest that the dependence of the activity of PGN cells on the rate of calcium removal can be one of the key factors determining individual cell response to elimination of cortical input.

Chronic fluoxetine treatment impairs motivation and reward learning by affecting neuronal plasticity in the central amygdala.

BACKGROUND AND PURPOSE: The therapeutic effects of fluoxetine are believed to be due to increasing neuronal plasticity and reversing some learning deficits. Nevertheless, a growing amount of evidence shows adverse effects of this drug on cognition and some forms of neuronal plasticity. EXPERIMENTAL APPROACH: To study the effects of chronic fluoxetine treatment, we combine an automated assessment of motivation and learning in mice with an investigation of neuronal plasticity in the central amygdala and basolateral amygdala. We use immunohistochemistry to visualize neuronal types and perineuronal nets, along with DI staining to assess dendritic spine morphology. Gel zymography is used to test fluoxetine's impact on matrix metalloproteinase-9, an enzyme involved in synaptic plasticity. KEY RESULTS: We show that chronic fluoxetine treatment in non-stressed mice increases perineuronal nets-dependent plasticity in the basolateral amygdala, while impairing MMP-9-dependent plasticity in the central amygdala. Further, we illustrate how the latter contributes to anhedonia and deficits of reward learning. Behavioural impairments are accompanied by alterations in morphology of dendritic spines in the central amygdala towards an immature state, most likely reflecting animals' inability to adapt. We strengthen the link between the adverse effects of fluoxetine and its influence on MMP-9 by showing that behaviour of MMP-9 knockout animals remains unaffected by the drug. CONCLUSION AND IMPLICATIONS: Chronic fluoxetine treatment differentially affects various forms of neuronal plasticity, possibly explaining its opposing effects on brain and behaviour. These findings are of immediate clinical relevance since reported side effects of fluoxetine pose a potential threat to patients.

Cross-trial correlation analysis of evoked potentials reveals arousal-related attenuation of thalamo-cortical coupling.

We describe a computational method for assessing functional connectivity in sensory neuronal networks. The method, which we term cross-trial correlation, can be applied to signals representing local field potentials (LFPs) evoked by sensory stimulations and utilizes their trial-to-trial variability. A set of single trial samples of a given post-stimulus latency from consecutive evoked potentials (EPs) recorded at a given site is correlated with such sets for all other latencies and recording sites. The results of this computation reveal how neuronal activities at various sites and latencies correspond to activation of other sites at other latencies. The method was used to investigate the functional connectivity of thalamo-cortical network of somatosensory system in behaving rats at two levels of alertness: habituated and aroused. We analyzed potentials evoked by vibrissal deflections recorded simultaneously from the ventrobasal thalamus and barrel cortex. The cross-trial correlation analysis applied to the early post-stimulus period (<25 ms) showed that the magnitude of the population spike recorded in the thalamus at 5 ms post-stimulus correlated with the cortical activation at 6-13 ms post-stimulus. This correlation value was reduced at 6-9 ms, i.e. at early postsynaptic cortical response, with increased level of the animals' arousal. Similarly, the aroused state diminished positive thalamo-cortical correlation for subsequent early EP waves, whereas the efficacy of an indirect cortico-fugal inhibition (over 15 ms) did not change significantly. Thus we were able to characterize the state related changes of functional connections within the thalamo-cortical network of behaving animals.

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.

Current source density reconstruction from incomplete data.

We propose two ways of estimating current source density (CSD) from measurements of voltage on a Cartesian grid with missing recording points using the inverse CSD method. The simplest approach is to substitute local averages (LA) in place of missing data. A more elaborate alternative is to estimate a smaller number of CSD parameters than the actual number of recordings and to take the least-squares fit (LS). We compare the two approaches in the three-dimensional case on several sets of surrogate and experimental data, for varying numbers of missing data points, and discuss their advantages and drawbacks. One can construct CSD distributions for which one or the other approach is better. However, in general, the LA method is to be recommended as being more stable and more robust to variations in the recorded fields.

Inferring coupling strength from event-related dynamics.

We propose an approach for inferring strength of coupling between two systems from their transient dynamics. This is of vital importance in cases where most information is carried by the transients, for instance, in evoked potentials measured commonly in electrophysiology. We show viability of our approach using nonlinear and linear measures of synchronization on a population model of thalamocortical loop and on a system of two coupled Rössler-type oscillators in nonchaotic regime.

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 4x5x7 positions.

Matrix Metalloproteinase-9 and Synaptic Plasticity in the Central Amygdala in Control of Alcohol-Seeking Behavior.

BACKGROUND: Dysfunction of the glutamatergic system has been implicated in alcohol addiction; however, the molecular underpinnings of this phenomenon are still poorly understood. In the current study we have investigated the possible function of matrix metalloproteinase-9 (MMP-9) in alcohol addiction because this protein has recently emerged as an important regulator of excitatory synaptic plasticity. METHODS: For long-term studies of alcohol drinking in mice we used IntelliCages. Dendritic spines were analyzed using Diolistic staining with DiI. Whole-cell patch clamp was used to assess silent synapses. Motivation for alcohol in human subjects was assessed on the basis of a Semi-Structured Assessment for the Genetics of Alcoholism interview. RESULTS: Mice devoid of MMP-9 (MMP-9 knockout) drank as much alcohol as wild-type animals; however, they were impaired in alcohol seeking during the motivation test and withdrawal. The deficit could be rescued by overexpression of exogenous MMP-9 in the central nucleus of the amygdala (CeA). Furthermore, the impaired alcohol seeking was associated with structural alterations of dendritic spines in the CeA and, moreover, whole-cell patch clamp analysis of the basal amygdala to CeA projections showed that alcohol consumption and withdrawal were associated with generation of silent synapses. These plastic changes were impaired in MMP-9 knockout mice. Finally, C/T polymorphism of MMP-9 gene at position -1562, which upregulates MMP-9 expression, correlated with increased motivation for alcohol in alcoholics. CONCLUSIONS: In aggregate, our results indicate a novel mechanism of alcohol craving that involves MMP-9-dependent synaptic plasticity in CeA.

GSK-3ß 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-3ß (GSK-3ß) has been implicated. Despite much evidence of the involvement of GSK-3ß in functional synaptic plasticity, it is unclear how GSK-3ß 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-3ß in neurons to investigate how GSK-3ß affects the structural plasticity of dendritic spines. Following visualization of dendritic spines with DiI dye, we found that increasing GSK-3ß activity increased the number of thin spines, whereas lacking GSK-3ß increased the number of stubby spines in the dentate gyrus. Under conditions of neuronal excitation, increasing GSK-3ß activity caused higher activity of extracellularly acting matrix metalloproteinase-9 (MMP-9), and MMP inhibition normalized thin spines in GSK-3ß overexpressing mice. Administration of the nonspecific GSK-3ß inhibitor lithium in animals with active MMP-9 and animals lacking MMP-9 revealed that GSK-3ß and MMP-9 act in concert to control dendritic spine morphology. Altogether, our data demonstrate that the dysregulation of GSK-3ß activity has dramatic consequences on dendritic spine morphology, implicating MMP-9 as a mediator of GSK-3ß-induced synaptic alterations.

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.

Eco-HAB as a fully automated and ecologically relevant assessment of social impairments in mouse models of autism.

Eco-HAB is an open source, RFID-based system for automated measurement and analysis of social preference and in-cohort sociability in mice. The system closely follows murine ethology. It requires no contact between a human experimenter and tested animals, overcoming the confounding factors that lead to irreproducible assessment of murine social behavior between laboratories. In Eco-HAB, group-housed animals live in a spacious, four-compartment apparatus with shadowed areas and narrow tunnels, resembling natural burrows. Eco-HAB allows for assessment of the tendency of mice to voluntarily spend time together in ethologically relevant mouse group sizes. Custom-made software for automated tracking, data extraction, and analysis enables quick evaluation of social impairments. The developed protocols and standardized behavioral measures demonstrate high replicability. Unlike classic three-chambered sociability tests, Eco-HAB provides measurements of spontaneous, ecologically relevant social behaviors in group-housed animals. Results are obtained faster, with less manpower, and without confounding factors.

Linking extracellular electric potential in the brain to neural activity - a review of source localization and component identification methods.

Due to technological advances in electrophysiology, there is renewed interest in the analysis of local field potentials recorded at many sites simultaneously. In this paper the main problems related to the analysis of LFP are presented, and recent developments in the data analysis methods are reviewed. The focus of the paper is on reconstruction of current source den sity from extracellular recordings and on decomposition of neural activity into meaningful components.

Emotional processing in Parkinson's disease and schizophrenia: evidence for response bias deficits in PD.

Deficits in facial emotion recognition in Parkinson's disease (PD) patients has been well documented. Nevertheless, it is still not clear whether facial emotion recognition deficits are secondary to other cognitive impairments. The aim of this study was to answer the question of whether deficits in facial emotion recognition in PD result from impaired sensory processes, or from impaired decision processes. To address this question, we tested the ability to recognize a mixture of basic and complex emotions in 38 non-demented PD patients and 38 healthy controls matched on demographic characteristics. By using a task with an increased level of ambiguity, in conjunction with the signal detection theory, we were able to differentiate between sensitivity and response bias in facial emotion recognition. Sensitivity and response bias for facial emotion recognition were calculated using a d-prime value and a c index respectively. Our study is the first to employ the EIS-F scale for assessing facial emotion recognition among PD patients; to test its validity as an assessment tool, a group comprising schizophrenia patients and healthy controls were also tested. Patients with PD recognized emotions with less accuracy than healthy individuals (d-prime) and used a more liberal response criterion (c index). By contrast, patients with schizophrenia merely showed diminished sensitivity (d-prime). Our results suggest that an impaired ability to recognize facial emotions in PD patients may result from both decreased sensitivity and a significantly more liberal response criteria, whereas facial emotion recognition in schizophrenia may stem from a generalized sensory impairment only.

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.

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.

LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons.

Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (≳500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.