Publications

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 adults.

Key points

• Novel pan-retinal recordings of mouse retinal waves were obtained at near cellular resolution using a large-scale, high-density array of 4096 electrodes to investigate changes in wave spatiotemporal properties from postnatal day 2 to eye opening.
• Early cholinergic waves are large, slow and random, with low cellular recruitment.
• A developmental shift in GABA_A signalling from depolarizing to hyperpolarizing influences the dynamics of cholinergic waves.
• Glutamatergic waves that occur just before eye opening are focused, faster, denser, non-random and repetitive.
• These results provide a new, deeper understanding of developmental changes in retinal spontaneous activity patterns, which will help researchers in the investigation of the role of early retinal activity during wiring of the visual system.

The immature retina generates spontaneous waves of spiking activity that sweep across the ganglion cell layer during a limited period of development before the onset of visual experience. The spatiotemporal patterns encoded in the waves are believed to be instructive for the wiring of functional connections throughout the visual system. However, the ontogeny of retinal waves is still poorly documented as a result of the relatively low resolution of conventional recording techniques. Here, we characterize the spatiotemporal features of mouse retinal waves from birth until eye opening in unprecedented detail using a large-scale, dense, 4096-channel multielectrode array that allowed us to record from the entire neonatal retina at near cellular resolution. We found that early cholinergic waves propagate with random trajectories over large areas with low ganglion cell recruitment. They become slower, smaller and denser when GABA_A signalling matures, as occurs beyond postnatal day (P) 7. Glutamatergic influences dominate from P10, coinciding with profound changes in activity dynamics. At this time, waves cease to be random and begin to show repetitive trajectories confined to a few localized hotspots. These hotspots gradually tile the retina with time, and disappear after eye opening. Our observations demonstrate that retinal waves undergo major spatiotemporal changes during ontogeny. Our results support the hypotheses that cholinergic waves guide the refinement of retinal targets and that glutamatergic waves may also support the wiring of retinal receptive fields.

Among the different methodologies used for electrophysiological measures in the brain, electrodes have played an undisputed role in high-quality intracellular signal recordings from a few neurons and in chronic extracellular measures with electrode-array probes implanted in the brain. Electrode arrays providing multisite extracellular measures have become a key methodology in neuroscience for studying coding and transmission of information by neuronal ensembles [1] and for the development of Brain–Machine Interfaces (BMIs) and neural prosthetics [2–8]. This is mainly because electrode arrays combine the unique features of bidirectionality (i.e., recording and stimulation), long-term stability (up to years), and of a large signal bandwidth that enables recordings of action potentials from multiple neurons as well as low-frequency field potentials (LFPs).

The central nervous system of mammals is among the most elaborate structures in nature. For example, the cerebral cortex, which is involved in perception, motor control, attention, and memory, is organized in horizontal layers, each of astonishing complexity (Jones and Peters 1990). One cubic millimeter of mammalian neocortex contains about 100,000 neurons (Meyer et al. 2010). Each neuron receives on the order of 20,000 synapses and communicates with tens to hundreds of other cells in an extraordinarily complex and highly interwoven cellular network. Moreover, neurons are remarkably diverse in terms of their morphology, electrical properties, connectivity, and neurotransmitter phenotype.

The brain is probably the least understood organ of our body, in both normal and pathological conditions. In consequence its study requires the development of novel neurotechnologies. Here we report on the development and experimental validation of novel on-chip neurotechnologies for investigating the properties of neural networks and brain tissues in-vitro.

Retinal ganglion cells can be categorized according to a range of physiological criteria, some of which correspond to distinct anatomical features such as dendritic stratification depth in the inner plexiform layer. So far, however, this classification largely relied on pooling data from multiple preparations due to limitations in the number of neurons that could be simultaneously recorded. Here, we present a novel approach for localizing ganglion cells recorded in the adult mouse retina during light stimulation with high density 4096 channel multielectrode arrays. Recording channels were arranged on a 64x64 lattice and separated by 42 um, enabling simultaneous sampling of activity at near cellular resolution of >500 units on a large patch of the retina. Typically, signals from the same cell were detectable on multiple neighboring channels. We exploited this to substantially improve the detection and isolation of spike events, to estimate the current source location, and to cluster spiking events according to spatial proximity, thus identifying the activity of single units. A comparison with standard spike sorting techniques showed this method could substantially improve spike detection and correctly cluster spikes that would otherwise be assigned to different sources. An analysis of responses to full field flashes (a sequence of dark/light stimuli) led to the following findings: response latencies were broadly distributed from 50-950 ms, with faster responses in OFF cells; distribution of the ratio of ON versus OFF responses were broad and bi-modal, without clear peak at equal ratios (ON-OFF cells) and higher abundance of OFF cells compared to ON cells; the spatial distribution of ON and OFF cells in the dorsal retina was random. In summary, our results are a first step towards a comprehensive and statistically sound characterization of the activity of a large population of neurons without variability caused by combining data from multiple preparations.

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Neuronal circuit disturbances that lead to hyperexcitability in the cortico-hippocampal network are one of the landmarks of temporal lobe epilepsy. The dentate gyrus (DG) network plays an important role in regulating the excitability of the entire hippocampus by filtering and integrating information received via the perforant path. Here, we investigated possible epileptogenic abnormalities in the function of the DG neuronal network in the Synapsin II (Syn II) knockout mouse (Syn II−/−), a genetic mouse model of epilepsy. Syn II is a presynaptic protein whose deletion in mice reproducibly leads to generalized seizures starting at the age of 2 months. We made use of a high-resolution microelectrode array (4096 electrodes) and patch-clamp recordings, and found that in acute hippocampal slices of young pre-symptomatic (3–6 week-old) Syn II−/− mice excitatory synaptic output of the mossy fibers is reduced. Moreover, we showed that the main excitatory neurons present in the polymorphic layer of the DG, hilar mossy cells, display a reduced excitability. We also provide evidence of a predominantly inhibitory regulatory output from mossy cells to granule cells, through feed-forward inhibition, and show that the excitatory-inhibitory ratio is increased in both pre-symptomatic and symptomatic Syn II−/− mice. These results support the key role of the hilar mossy neurons in maintaining the normal excitability of the hippocampal network and show that the late epileptic phenotype of the Syn II−/− mice is preceded by neuronal circuitry dysfunctions. Our data provide new insights into the mechanisms of epileptogenesis in the Syn II−/− mice and open the possibility for early diagnosis and therapeutic interventions.

High density microelectrode arrays (MEAs) provide extracellular recordings from thousand of electrodes (http://www.3brain.com) and offer novel capabilities to investigate electrophysiological signaling in cultured neuronal networks and in ex vivo brain tissues. In this study we report on our recent technological and data analysis advancements to investigate the propagation and the interplay of spontaneous and electrically evoked activities in cultured networks.

High-density microelectrode arrays (MEA) can measure neuronal activity in potentially thousands of units with a high spatial resolution [1]. However due to the small size of the preamplifers, noise artifacts can affect spike detection. Additionally, the MEA chip itself is not perfectly homogeneous and the electrical coupling between the electrodes and a neuron may be weak. Therefore, the characteristics of neuronal spikes and noise are inherently different in each recording channel, such that estimating an average performance of the spike detection would not be representative for individual recording channels. As we aim to observe slow changes in single neuron activity, it is crucial to know how much a change in electrical coupling could potentially affect the number of detected spikes.

Homeostatic activity in large networks of neurons is a relatively scantly explored area of neuroscience, both on experimental and computational level [1]. With recent advance in recording techniques, the lack of experimental data is gradually ceasing to be the limitation. New multielectrode arrays (MEA) allow for monitoring cultures of thousands of neurons over many days with high spatial resolution [2]. However, the interpretation of multi-neuron recordings is not straightforward and requires methods going beyond the simplest descriptive statistics.

Multielectrode arrays (MEAs) for electrophysiological studies in neuroscience can nowadays be realized with Complementary Metal Oxide Semiconductor (CMOS) technology to provide large active areas with several thousands of simultaneously recording microelectrodes and electrode densities with inter-electrode separations approaching cellular sizes. This provides spatial and temporal resolutions to literally image electrophysiological activity propagations. Here, we focus on the achieved sensing capabilities of a 64×64 electrode array as well as on the actuation performances of a novel CMOS-chip integrating 16 electrodes for electrical stimulation. We report and discuss our recent results obtained from neuronal cell cultures, brain slices and mouse retina preparations.

Ion Beam Induced Deposition (IBID) is employed to fabricate three-dimensional nanoprotrusions on top of the recording pads of an active pixel sensor array (APS-MEA) featuring 4096 microelectrodes. Modified APS-MEAs are envisioned as enhanced tools to achieve real-time “in-cell” recordings from thousands of sensing elements, thus aiming to large-scale in-vitro registrations with unprecedented signal quality. A generalized electric model is proposed to address the revealed complexity of the neuron/electrode interface, and simulations have been conducted revealing the most advantageous cell/electrode coupling conditions. Preliminary results on the recording of spontaneous activity in cultured neuronal networks by means of nanostructured microelectrodes demonstrate the compatibility of IBID technology and APS-MEA infrastructure. The interface between cultured mammalian neurons and modified microelectrodes is revealed by FIB/SEM analysis, fostering the employment of the proposed electrical model for interpretation of electrical recordings from nanostructured microelectrodes.

It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural connectivity of neuronal networks from multimodal datasets combining high-resolution Multi-Electrode Arrays (MEA) and fluorescence microscopy. Probabilistic directional features are used in a graph heat kernel framework to identify the structural connectivity of the neuronal network. Electrode connectivity maps are computed as weighted graphs in which the edge weights represent the strength of the structural connection.

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.

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High density MEAs, providing recording capability from thousand of electrodes, are nowadays a commercial tool (www.3brain.com) used to investigate spontaneous or chemically modulated activity in dissociated cultures as well as ex vivo brain tissues. In previous paper we already demonstrated that such as high spatial resolution (4’096 electrodes, 42 um pitch on a 2.7 mm by 2.7 mm active area) allows to infer the functional connectivity of low density cultures both at global network level and at the resolution of microcircuits of few cells, showing that functional connectivity qualitatively maps the topological and morphological spatial distribution of the network (Maccione et al., 2012). As a further step to better understand how information is processed in neuronal networks, here we present preliminary results on effective connectivity obtained by electrically stimulating dissociate cultures using a new generation of high density devices that integrate stimulating capabilities. These innovative MEAs provide 4’096 recording electrodes with a pitch of 81 um (active area of 8 mm by 8 mm) interlaced with stimulating electrodes every 8 recording sites. We applied on dissociated hippocampal networks at 18-21 DIVs a train of biphasic signals (3 Volt peak to peak, duty cycle 500 us) at low frequency (0.2 Hz) for each of the 16 stimulating sites, inducing repetitive and reliable responses.The analysis of the resulting Post Stimulus Time Histograms (PSTHs) show interesting features. First, the “early response” just after the deliver of the stimulus as observed on conventional MEAs, considered to be a non sinaptically propagated response (Wagenaar et al., 2005), is almost absent. Second, the PSTH center of mass changes according to the distance from the stimulation site. Interestingly, the induced propagating patterns are reliable and situ dependent, showing that the stimulation in different areas is able to elicit specific responses not belonging to the repertoire of spontaneous bursting activity.As a perspective, these results suggest that the stimulating capability of high density MEA combined with optical imaging might be a valuable tool to reconstruct the effective connectivity, opening thus new perspectives in understanding network signal processing.

Homeostatic plasticity is one of the key mechanisms ensuring the remarkable adaptive abilities of the brain. However, this is still a relatively scantly explored branch of both experimental and computational neuroscience - in particular on a large, multi-neuronal scale. With recent advance in recording techniques, the lack of experimental data can be easily overcome – novel multielectrode arrays allow for high-density recordings from in vitro cultures consisting of thousands of neurons. What is needed to complement this rich data is analysis techniques that would be able to shed some light on the mechanism of the underlying process – in contrast to most conventional analysis techniques, such as firing rates, correlations or inter-burst intervals, which provide little more than descriptive information. In search for measures able to capture more complex phenomena, over the last decade a new approach has been developed - pairwise maximum entropy modelling (MaxEnt). It is a statistical model that fits two sets of parameters to explain the probability of spiking patterns in the network: individual neuron parameters that could be interpreted as excitability; and pairwise interaction parameters that could be interpreted as the functional connection strength between neurons. Successful application of this model to a variety of recordings has helped reevaluate the importance of neuronal interactions in shaping network activity (Schneidman et al., 2006; Shlens et al., 2006). Additionally, the shortcomings of MaxEnt in certain cases can serve as an indicator of higher-order interactions between neurons (Ohiorhenuan et al., 2010). In present work we examine the extent to which the statistics of MaxEnd model fits and parameters can assist in understanding different modes of activity of a neuronal culture – specifically, along the duration of a homeostatic experiment. Neural activity from primary neuron cultures was recorded with the 4096 channel Active Pixel Sensor (APS) MEA, allowing for reliable isolation of single unit activity at near-cellular resolution (Berdondini et al., 2009). 20-minute datasets were obtained at different stages of homeostatic compensation during and after long-term CNQX application. For the data sets with a stationary activity state, large numbers of four-unit MaxEnt models were constructed for randomly chosen neurons on two spatial scales. Comparison of the statistics of the fits and parameters across the scales and across conditions indicates that different activity modes exhibit different profiles of local clustering and higher-order interactions.

High density MEAs provides improved capabilities in spatially and temporally resolving network activity patterns at the resolution of single cell, becoming more and more a standard technology in unravelling neuronal signal processing. In combination with fluorescence imaging, these devices open new perspective in finely identify structural and functional network properties. In this paper we present an automated analysis able to correlate the network topology extracted from fluorescence imaging of specific sub populations (e.g. inhibitory neurons), with high density electrophysiological recordings, paving the way to finely correlate functional activity with morphological spatial composition of dissociated neuronal cultures.

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We used electrophysiological signals recorded by CMOS Micro Electrode Arrays (MEAs) at high spatial resolution to estimate the functional-effective connectivity of sparse hippocampal neuronal networks in vitro by applying a cross-correlation (CC) based method and ad hoc developed spatio-temporal filtering. Low-density cultures were recorded by a recently introduced CMOS-MEA device providing simultaneous multi-site acquisition at high-spatial (21 μm inter-electrode separation) as well as high-temporal resolution (8 kHz per channel). The method is applied to estimate functional connections in different cultures and it is refined by applying spatio-temporal filters that allow pruning of those functional connections not compatible with signal propagation. This approach permits to discriminate between possible causal influence and spurious co-activation, and to obtain detailed maps down to cellular resolution. Further, a thorough analysis of the links strength and time delays (i.e., amplitude and peak position of the CC function) allows characterizing the inferred interconnected networks and supports a possible discrimination of fast mono-synaptic propagations, and slow poly-synaptic pathways. By focusing on specific regions of interest we could observe and analyze microcircuits involving connections among a few cells. Finally, the use of the high-density MEA with low density cultures analyzed with the proposed approach enables to compare the inferred effective links with the network structure obtained by staining procedures.

Finding the electrical conductivity of tissue is highly important for understanding the tissue’s structure and functioning. However, the inverse problem of inferring spatial conductivity from data is highly ill-posed and computationally intensive. In this paper, we propose a novel method to solve the inverse problem of inferring tissue conductivity from a set of transmembrane potential and stimuli measurements made by microelectrode arrays (MEA). We first formalize the discrete forward model of transmembrane potential propagation, based on a reaction–diffusion model with an anisotropic inhomogeneous electrical conductivity-tensor field. Then, we propose a novel parallel optimization algorithm for solving the complex inverse problem of estimating the electrical conductivity-tensor field. Specifically, we propose a single-step approximation with a parallel block-relaxation optimization routine that simplifies the joint tensor field estimation problem into a set of computationally tractable subproblems, allowing the use of efficient standard optimization tools. Finally, using numerical examples of several electrical conductivity field topologies and noise levels, we analyze the performance of our algorithm, and discuss its application to real measurements obtained from smooth-muscle cardiac tissue, using data collected with a high-resolution MEA system.

The fabrication of three-dimensional platinum nanoprotrusions by ion beam induced deposition (IBID) is here proposed to study their interaction with cultured rat primary neurons. The broad versatility of IBID allows to test the effects on cells network morphology of different protrusions shapes, from straight pillars to nail-headed or sphere-headed vertical structures. A preferential adhesion of cells on fabricated Pt nanostructures is clearly shown by fluorescence and scanning electron microscopy, with dense and suspended neuritic networks observed on arrays of large-headed pillars. This technique could be exploited to improve cells/electrodes adhesion and to increase the detected extracellular electric signal.

Microelectrode arrays (MEAs) are employed to study extracellular electrical activity in neuronal tissues. Nevertheless, commercially available MEAs provide a limited number of recording sites and do not allow a precise identification 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 (Berdondini 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 chemically 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.

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