Deep 2-photon imaging and artifact-free optogenetics through transparent graphene microelectrode arrays

Thunemann, Martin; Lu, Yichen; Liu, Xin; Kılıç, Kıvılcım; Desjardins, Michèle; Vandenberghe, Matthieu; Sadegh, Sanaz; Saisan, Payam A.; Cheng, Qun; Weldy, Kimberly L.; Lyu, Hongming; Djurovic, Srdjan; Andreassen, Ole A.; Dale, Anders M.; Devor, Anna; Kuzum, Duygu

doi:10.1038/s41467-018-04457-5

Cited by 168 publications

(220 citation statements)

References 52 publications

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“…In addition, it has good scaling capabilities and can be further extended, optimized, and possibly integrated with already in-use techniques. Furthermore, it will be broadly applicable to experimental techniques for neural activation and recording, increasing its utility for the analyses of spontaneous neural activity patterns, as well as neuronal responses to pharmacological perturbations and electrical and optogenetic stimulations [68,69,70,71,72].…”

Section: Discussionmentioning

confidence: 99%

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Puppo

PrÐ

Bang

et al. 2020

Preprint

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Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in-vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective -direct and causal -connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then, reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect and apparent links. Our method can be generally applied to the functional characterization of any in-vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in-vitro iPSC-derived neuronal cultures by reconstructing their effective connectivity, thus facilitating future efforts to generate predictive models for neurological disorders, drug testing and neuronal network modeling.

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Section: Discussionmentioning

confidence: 99%

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Puppo

PrÐ

Bang

et al. 2020

Preprint

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“…Data acquisition, signal processing and increased dimensionality of the data present additional challenges when there is a need to perform recordings of two cell types with different Ca 2+ dynamics. As to data acquisition, although recent advances have pushed the boundaries of multiphoton imaging, with significant improvements that enable imaging in multiple brain areas, across laminae, and in nonhead-fixed configurations (Yang & Yuste, 2017) (Chung et al, 2019) and clear electrode arrays (Thunemann et al, 2018), to solve the current problem posed by the large equipment necessary to carry out single-neuron recordings, which precludes astrocyte imaging. Despite the advances in Ca 2+ imaging, single-neuron electrophysiological measurements are preferable, for Ca 2+ transients lack temporal resolution to reveal single-action potentials.…”

Section: Zooming Out To Astrocyte Populationsmentioning

confidence: 99%

A roadmap to integrate astrocytes into Systems Neuroscience

Kastanenka

Moreno-Bote

Pittà

et al. 2019

Glia

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Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well‐documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub‐serve coding and higher‐brain functions. First, we reviewed Systems‐like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte–neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy‐efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease.

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“…An array with 49 ITO electrodes (500 μm diameter) was tested in vivo (P Ledochowitsch et al, 2015). Thunemann et al have recently reported the use of graphene in ECoG electrodes (Thunemann et al, 2018). Arrays with 16 graphene electrodes (100×100 µm separated by 300 µm) enabled simultaneous 2-photon microscopy, optogenetic stimulation, and cortical recordings.…”

Section: How Can We Monitor Large-scale Neuronal Activity Dynamics Wimentioning

confidence: 99%

Transparent and flexible ECoG electrode arrays based on silver nanowire networks for neural recordings

Neto

Costa

Pinto

et al. 2020

Preprint

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This work explored hybrid films of silver nanowires (AgNWs) with indium-doped zinc oxide (IZO) for developing high-performance and low-cost electrocorticography (ECoG) electrodes.The hybrid films achieved a sheet resistance of 6 Ω/sq while maintaining a transparency of ≈60% at 550 nm. Electrodes with 500 μm diameter were fabricated with these films and reached an impedance of 20 kΩ at 1 kHz and a charge storage capacity of 3.2 mC/cm 2 , a 2× and 320× improvement over IZO electrodes, respectively. Characterization of light-induced artifacts was performed showing that small light intensities (<14 mW/mm 2 ) elicit electrical potential variation in the magnitude order of baseline noise. The validation of electrodes in vivo was achieved by recording electrical neural activity from the surface of rat cortex under anaesthesia. Moreover, the presence of the films did not cause obstruction of light during fluorescence microscopy.The presented film and electrode characterization studies highlighted the capabilities of this hybrid structure to fabricate transparent and flexible electrodes that are able to combine the superior temporal resolution of extracellular electrophysiology with the spatial resolution offered by optical imaging.

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Deep 2-photon imaging and artifact-free optogenetics through transparent graphene microelectrode arrays

Cited by 168 publications

References 52 publications

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

A roadmap to integrate astrocytes into Systems Neuroscience

Transparent and flexible ECoG electrode arrays based on silver nanowire networks for neural recordings

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