Active Pixel Sensor Multielectrode Array for High Spatiotemporal Resolution

Berdondini, Luca; Bosca, Alessandro; Nieus, Thierry; Maccione, Alessandro

doi:10.1007/978-1-4899-8038-0_7

Cited by 8 publications

(7 citation statements)

References 151 publications

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“…Extracellular electrophysiological in vitro experiments were carried out by means of a recently developed HD-MEA. These devices, thoroughly reviewed in ref ( 46 ), provide a matrix of 4096 electrodes (sensing area 21 × 21 μm 2 , pitch 42 × 42 μm 2 ) arranged in a 64 × 64 layout and covering an area of 2.7 by 2.7 mm 2 . After seeding and growing dissociated neuronal cultures on top of the active area, extracellular recordings of the spontaneous network activity were performed simultaneously from all the 4096 electrodes at 7 kHz sampling rate.…”

Section: Methodsmentioning

confidence: 99%

Selective Targeting of Neurons with Inorganic Nanoparticles: Revealing the Crucial Role of Nanoparticle Surface Charge

et al. 2017

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Nanoparticles (NPs) are increasingly used in biomedical applications, but the factors that influence their interactions with living cells need to be elucidated. Here, we reveal the role of NP surface charge in determining their neuronal interactions and electrical responses. We discovered that negatively charged NPs administered at low concentration (10 nM) interact with the neuronal membrane and at the synaptic cleft, whereas positively and neutrally charged NPs never localize on neurons. This effect is shape and material independent. The presence of negatively charged NPs on neuronal cell membranes influences the excitability of neurons by causing an increase in the amplitude and frequency of spontaneous postsynaptic currents at the single cell level and an increase of both the spiking activity and synchronous firing at neural network level. The negatively charged NPs exclusively bind to excitable neuronal cells, and never to nonexcitable glial cells. This specific interaction was also confirmed by manipulating the electrophysiological activity of neuronal cells. Indeed, the interaction of negatively charged NPs with neurons is either promoted or hindered by pharmacological suppression or enhancement of the neuronal activity with tetrodotoxin or bicuculline, respectively. We further support our main experimental conclusions by using numerical simulations. This study demonstrates that negatively charged NPs modulate the excitability of neurons, revealing the potential use of NPs for controlling neuron activity.

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Section: Methodsmentioning

confidence: 99%

Selective Targeting of Neurons with Inorganic Nanoparticles: Revealing the Crucial Role of Nanoparticle Surface Charge

et al. 2017

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“…Protruding electrode microstructures were first described in the late 1990s [ 233 ], where the 3D structures were electroplated Pt hillocks ( Figure 11 A). Modern 3D MEAs typically consist of tip-shaped microelectrodes [ 195 , 235 , 239 ] ( Figure 11 B,C). The 3D tip-shaped electrodes can vary in base area and height and can penetrate 40–100 µm deep into the biological specimen.…”

Section: Brain-on-chip Electrophysiology: Fabrication Features Anmentioning

confidence: 99%

“…Left—MEA with EPON SU-8 clustering structures for investigating interconnected neuronal populations. Adapted with permission from [ 195 ]. Copyright © 2014, Springer Science Business Media New York.…”

Section: Figurementioning

confidence: 99%

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Electrophysiology Read-Out Tools for Brain-on-Chip Biotechnology

et al. 2021

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Brain-on-Chip (BoC) biotechnology is emerging as a promising tool for biomedical and pharmaceutical research applied to the neurosciences. At the convergence between lab-on-chip and cell biology, BoC couples in vitro three-dimensional brain-like systems to an engineered microfluidics platform designed to provide an in vivo-like extrinsic microenvironment with the aim of replicating tissue- or organ-level physiological functions. BoC therefore offers the advantage of an in vitro reproduction of brain structures that is more faithful to the native correlate than what is obtained with conventional cell culture techniques. As brain function ultimately results in the generation of electrical signals, electrophysiology techniques are paramount for studying brain activity in health and disease. However, as BoC is still in its infancy, the availability of combined BoC–electrophysiology platforms is still limited. Here, we summarize the available biological substrates for BoC, starting with a historical perspective. We then describe the available tools enabling BoC electrophysiology studies, detailing their fabrication process and technical features, along with their advantages and limitations. We discuss the current and future applications of BoC electrophysiology, also expanding to complementary approaches. We conclude with an evaluation of the potential translational applications and prospective technology developments.

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“…Extracellular recordings enable us to measure the activity of neurons as electrical potential deflections mainly due to ionic transmembrane currents. In recent years, many groups have been developing high-density multielectrode arrays (MEAs) for in vitro and in vivo applications, which allow measurements of neuronal activity with high spatiotemporal resolution (Berdondini et al 2014;Müller et al 2015;Neto et al 2016;Schröder et al 2015;Welkenhuysen et al 2016). These probes provide something close to a functional electrical imaging (Vassanelli 2014) of the neural activity, and this high information density can potentially be exploited to obtain a better understanding of the neural circuits under study.…”

Section: Introductionmentioning

confidence: 99%

Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification

Buccino

Kordovan

Ness

et al. 2018

Journal of Neurophysiology

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Neural circuits typically consist of many different types of neurons, and one faces a challenge in disentangling their individual contributions in measured neural activity. Classification of cells into inhibitory and excitatory neurons and localization of neurons on the basis of extracellular recordings are frequently employed procedures. Current approaches, however, need a lot of human intervention, which makes them slow, biased, and unreliable. In light of recent advances in deep learning techniques and exploiting the availability of neuron models with quasi-realistic three-dimensional morphology and physiological properties, we present a framework for automatized and objective classification and localization of cells based on the spatiotemporal profiles of the extracellular action potentials recorded by multielectrode arrays. We train convolutional neural networks on simulated signals from a large set of cell models and show that our framework can predict the position of neurons with high accuracy, more precisely than current state-of-the-art methods. Our method is also able to classify whether a neuron is excitatory or inhibitory with very high accuracy, substantially improving on commonly used clustering techniques. Furthermore, our new method seems to have the potential to separate certain subtypes of excitatory and inhibitory neurons. The possibility of automatically localizing and classifying all neurons recorded with large high-density extracellular electrodes contributes to a more accurate and more reliable mapping of neural circuits. NEW & NOTEWORTHY We propose a novel approach to localize and classify neurons from their extracellularly recorded action potentials with a combination of biophysically detailed neuron models and deep learning techniques. Applied to simulated data, this new combination of forward modeling and machine learning yields higher performance compared with state-of-the-art localization and classification methods.

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Active Pixel Sensor Multielectrode Array for High Spatiotemporal Resolution

Cited by 8 publications

References 151 publications

Selective Targeting of Neurons with Inorganic Nanoparticles: Revealing the Crucial Role of Nanoparticle Surface Charge

Selective Targeting of Neurons with Inorganic Nanoparticles: Revealing the Crucial Role of Nanoparticle Surface Charge

Electrophysiology Read-Out Tools for Brain-on-Chip Biotechnology

Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification

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