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      Multishank Thin‐Film Neural Probes and Implantation System for High‐Resolution Neural Recording Applications

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          Abstract

          Silicon probes have played a key role in studying the brain. However, the stark mechanical mismatch between these probes and the brain leads to chronic damage in the surrounding neural tissue, limiting their application in research and clinical translation. Mechanically flexible probes made of thin plastic shanks offer an attractive tissue‐compatible alternative but are difficult to implant into the brain. They also struggle to achieve the electrode density and layout necessary for the high‐resolution applications their silicon counterparts excel at. Here, a multishank high‐density flexible neural probe design is presented, which emulates the functionality of stiff silicon arrays for recording from neural population across multiple sites within a given region. The flexible probe is accompanied by a detachable 3D printed implanter, which delivers the probe by means of hydrophobic‐coated shuttles. The shuttles can then be retracted with minimal movement and the implanter houses the electronics necessary for in vivo recording applications. Validation of the probes through extracellular recordings from multiple brain regions and histological evidence of minimal foreign body response opens the path to long‐term chronic monitoring of neural ensembles.

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          Most cited references51

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          Fully integrated silicon probes for high-density recording of neural activity

          Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
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            Large-scale recording of neuronal ensembles.

            How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
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              NeuroGrid: recording action potentials from the surface of the brain

              Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultra-conformable, biocompatible and scalable neural interface array (the ‘NeuroGrid’) that can record both LFP and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding one week. We also recorded LFP-modulated spiking activity intra-operatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Advanced Electronic Materials
                Adv Elect Materials
                Wiley
                2199-160X
                2199-160X
                September 2023
                December 23 2022
                September 2023
                : 9
                : 9
                Affiliations
                [1 ] Department of Engineering Electrical Engineering Division University of Cambridge Cambridge CB3 0FA UK
                [2 ] Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge CB3 0AS UK
                [3 ] Bernstein Centre for Computational Neuroscience Munich Faculty of Medicine Ludwig‐Maximilians Universität München 82152 Planegg‐Martinsried Germany
                [4 ] Department of Clinical Neurosciences University of Cambridge Cambridge CB2 0QQ UK
                Article
                10.1002/aelm.202200883
                efea6336-d841-42ed-86bc-5e371d87c0eb
                © 2023

                http://creativecommons.org/licenses/by/4.0/

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