A Realistic Large-Scale Model of the Cerebellum Granular Layer Predicts Circuit Spatio-Temporal Filtering Properties

The way the cerebellar granular layer transforms incoming mossy fiber signals into new spike patterns to be related to Purkinje cells is not yet clear. Here, a realistic computational model of the granular layer was developed and used to address four main functional hypotheses: center-surround organization, time-windowing, high-pass filtering in responses to spike bursts and coherent oscillations in response to diffuse random activity. The model network was activated using patterns inspired by those recorded in vivo. Burst stimulation of a small mossy fiber bundle resulted in granule cell bursts delimited in time (time windowing) and space (center-surround) by network inhibition. This burst–burst transmission showed marked frequency-dependence configuring a high-pass filter with cut-off frequency around 100 Hz. The contrast between center and surround properties was regulated by the excitatory–inhibitory balance. The stronger excitation made the center more responsive to 10–50 Hz input frequencies and enhanced the granule cell output (with spikes occurring earlier and with higher frequency and number) compared to the surround. Finally, over a certain level of mossy fiber background activity, the circuit generated coherent oscillations in the theta-frequency band. All these processes were fine-tuned by NMDA and GABA-A receptor activation and neurotransmitter vesicle cycling in the cerebellar glomeruli. This model shows that available knowledge on cellular mechanisms is sufficient to unify the main functional hypotheses on the cerebellum granular layer and suggests that this network can behave as an adaptable spatio-temporal filter coordinated by theta-frequency oscillations

Kainate receptor activation shapes short-term synaptic plasticity by controlling receptor lateral Mobility at glutamatergic synapses

Kainate receptors (KARs) mediate postsynaptic currents with a key impact on neuronal excitability. However, the molecular determinants controlling KAR postsynaptic localization and stabilization are poorly understood. Here, we exploit optogenetic and single-particle tracking approaches to study the role of KAR conformational states induced by glutamate binding on KAR lateral mobility at synapses. We report that following glutamate binding, KARs are readily and reversibly trapped at glutamatergic synapses through increased interaction with the β-catenin/N-cadherin complex. We demonstrate that such activation-dependent synaptic immobilization of KARs is crucial for the modulation of short-term plasticity of glutamatergic synapses. Thus, the present study unveils the crosstalk between conformational states and lateral mobility of KARs, a mechanism regulating glutamatergic signaling, particularly in conditions of sustained synaptic activity

Experimental Investigation on Spontaneously Active Hippocampal Cultures Recorded by Means of High-Density MEAs: Analysis of the Spatial Resolution Effects

Based on experiments performed with high-resolution Active Pixel Sensor microelectrode arrays (APS-MEAs) coupled with spontaneously active hippocampal cultures, this work investigates the spatial resolution effects of the neuroelectronic interface on the analysis of the recorded electrophysiological signals. The adopted methodology consists, first, in recording the spontaneous activity at the highest spatial resolution (interelectrode separation of 21 μm) from the whole array of 4096 microelectrodes. Then, the full resolution dataset is spatially downsampled in order to evaluate the effects on raster plot representation, array-wide spike rate (AWSR), mean firing rate (MFR) and mean bursting rate (MBR). Furthermore, the effects of the array-to-network relative position are evaluated by shifting a subset of equally spaced electrodes on the entire recorded area. Results highlight that MFR and MBR are particularly influenced by the spatial resolution provided by the neuroelectronic interface. On high-resolution large MEAs, such analysis better represent the time-based parameterization of the network dynamics. Finally, this work suggest interesting capabilities of high-resolution MEAs for spatial-based analysis in dense and low-dense neuronal preparation for investigating signaling at both local and global neuronal circuitries

Electrophysiological imaging of epileptic brain slices reveals pharmacologically confined functional changes

Microelectrode arrays (MEAs) are employed to study extracellular electrical activity in neuronal tissues. Neverthe- less, commercially available MEAs provide a limited number of recording sites and do not allow a precise identifi- cation of the spatio-temporal characterization of the recorded signal. To overcome this limitation, high density MEAs, based on CMOS technology, were recently developed and validated on dissociated preparations (Ber- dondini et al. 2009). We show the platform capability to record extracellular electrophysiological signal from 4096 electrodes arranged in a squared area of 2.7 mm x 2.7 mm with inter-electrode distance of 21 μm at a sampling rate of 7.7 kHz/electrode. Here, we demonstrate the performances of these platforms for the acquisition chemi- cally evoked epileptiform activity from brain slices. Moreover the high spatial resolutions allow us to estimate the effect of drugs in spatially modulating Inter-Ictal ((I-IC) activity

Functional connectivity estimation over large networks at cellular resolution based on electrophysiological recordings and structural prior

Despite many structural and functional aspects of the brain organization have been extensively studied in neuroscience, we are still far from a clear understanding of the intricate structure-function interactions occurring in the multi-layered brain architecture, where billions of different neurons are involved. Although structure and function can individually convey a large amount of information, only a combined study of these two aspects can probably shade light on how brain circuits develop and operate at the cellular scale. Here, we propose a novel approach for refining functional connectivity estimates within neuronal networks using the structural connectivity as prior. This is done at the mesoscale, dealing with thousands of neurons while reaching, at the microscale, an unprecedented cellular resolution. The High-Density Micro Electrode Array (HD-MEA) technology, combined with fluorescence microscopy, offers the unique opportunity to acquire structural and functional data from large neuronal cultures approaching the granularity of the single cell. In this work, an advanced method based on probabilistic directional features and heat propagation is introduced to estimate the structural connectivity from the fluorescence image while functional connectivity graphs are obtained from the cross-correlation analysis of the spiking activity. Structural and functional information are then integrated by reweighting the functional connectivity graph based on the structural prior. Results show that the resulting functional connectivity estimates are more coherent with the network topology, as compared to standard measures purely based on cross-correlations and spatio-temporal filters. We finally use the obtained results to gain some insights on which features of the functional activity are more relevant to characterize actual neuronal interactions

Large-scale, high-resolution electrophysiological imaging of field potentials in brain slices with microelectronic multielectrode arrays

Multielectrode arrays (MEAs) are extensively used for electrophysiological studies on brain slices, but the spatial resolution and field of recording of conventional arrays are limited by the low number of electrodes available. Here, we present a large-scale array recording simultaneously from 4096 electrodes used to study propagating spontaneous and evoked network activity in acute murine cortico-hippocampal brain slices at unprecedented spatial and temporal resolution. We demonstrate that multiple chemically induced epileptiform episodes in the mouse cortex and hippocampus can be classified according to their spatio-temporal dynamics. Additionally, the large-scale and high-density features of our recording system enable the topological localization and quantification of the effects of antiepileptic drugs in local neuronal microcircuits, based on the distinct field potential propagation patterns. This novel high-resolution approach paves the way to detailed electrophysiological studies in brain circuits spanning spatial scales from single neurons up to the entire slice network

Nanoscale molecular reorganization of the inhibitory postsynaptic density is a determinant of gabaergic synaptic potentiation

Gephyrin is a key scaffold protein mediating the anchoring of GABAA receptors at inhibitory synapses. Here, we exploited superresolution techniques combined with proximity-based clustering analysis and model simulations to investigate the single-molecule gephyrin reorganization during plasticity of inhibitory synapses in mouse hippocampal cultured neurons. This approach revealed that, during the expression of inhibitory LTP, the increase of gephyrin density at postsynaptic sites is associated with the promoted formation of gephyrin nanodomains. We demonstrate that the gephyrin rearrangement in nanodomains stabilizes the amplitude of postsynaptic currents, indicating that, in addition to the number of synaptic GABAA receptors, the nanoscale distribution of GABAA receptors in the postsynaptic area is a crucial determinant for the expression of inhibitory synaptic plasticity. In addition, the methodology implemented here clears the way to the application of the graph-based theory to single-molecule data for the description and quantification of the spatial organization of the synapse at the single-molecule level

Dominant β-catenin mutations cause intellectual disability with recognizable syndromic features

The recent identification of multiple dominant mutations in the gene encoding β-catenin in both humans and mice has enabled exploration of the molecular and cellular basis of β-catenin function in cognitive impairment. In humans, β-catenin mutations that cause a spectrum of neurodevelopmental disorders have been identified. We identified de novo β-catenin mutations in patients with intellectual disability, carefully characterized their phenotypes, and were able to define a recognizable intellectual disability syndrome. In parallel, characterization of a chemically mutagenized mouse line that displays features similar to those of human patients with β-catenin mutations enabled us to investigate the consequences of β-catenin dysfunction through development and into adulthood. The mouse mutant, designated batface (Bfc), carries a Thr653Lys substitution in the C-terminal armadillo repeat of β-catenin and displayed a reduced affinity for membrane-associated cadherins. In association with this decreased cadherin interaction, we found that the mutation results in decreased intrahemispheric connections, with deficits in dendritic branching, long-term potentiation, and cognitive function. Our study provides in vivo evidence that dominant mutations in β-catenin underlie losses in its adhesion-related functions, which leads to severe consequences, including intellectual disability, childhood hypotonia, progressive spasticity of lower limbs, and abnormal craniofacial features in adult

Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings

Loss of Gnas Imprinting Differentially Affects REM/NREM Sleep and Cognition in Mice

It has been suggested that imprinted genes are important in the regulation of sleep. However, the fundamental question of whether genomic imprinting has a role in sleep has remained elusive up to now. In this work we show that REM and NREM sleep states are differentially modulated by the maternally expressed imprinted gene Gnas. In particular, in mice with loss of imprinting of Gnas, NREM and complex cognitive processes are enhanced while REM and REM–linked behaviors are inhibited. This is the first demonstration that a specific overexpression of an imprinted gene affects sleep states and related complex behavioral traits. Furthermore, in parallel to the Gnas overexpression, we have observed an overexpression of Ucp1 in interscapular brown adipose tissue (BAT) and a significant increase in thermoregulation that may account for the REM/NREM sleep phenotypes. We conclude that there must be significant evolutionary advantages in the monoallelic expression of Gnas for REM sleep and for the consolidation of REM–dependent memories. Conversely, biallelic expression of Gnas reinforces slow wave activity in NREM sleep, and this results in a reduction of uncertainty in temporal decision-making processes