A simulation study evaluating the performance of high-density electrode arrays on myocardial tissue

2000 ◽  
Vol 47 (7) ◽  
pp. 893-901 ◽  
Author(s):  
J.C. Eason ◽  
R.A. Malkin
2012 ◽  
Vol 207 (2) ◽  
pp. 161-171 ◽  
Author(s):  
Alessandro Maccione ◽  
Matteo Garofalo ◽  
Thierry Nieus ◽  
Mariateresa Tedesco ◽  
Luca Berdondini ◽  
...  

2019 ◽  
Author(s):  
Fabio Boi ◽  
Nikolas Perentos ◽  
Aziliz Lecomte ◽  
Gerrit Schwesig ◽  
Stefano Zordan ◽  
...  

AbstractThe advent of implantable active dense CMOS neural probes opened a new era for electrophysiology in neuroscience. These single shank electrode arrays, and the emerging tailored analysis tools, provide for the first time to neuroscientists the neurotechnology means to spatiotemporally resolve the activity of hundreds of different single-neurons in multiple vertically aligned brain structures. However, while these unprecedented experimental capabilities to study columnar brain properties are a big leap forward in neuroscience, there is the need to spatially distribute electrodes also horizontally. Closely spacing and consistently placing in well-defined geometrical arrangement multiple isolated single-shank probes is methodologically and economically impractical. Here, we present the first high-density CMOS neural probe with multiple shanks integrating thousand’s of closely spaced and simultaneously recording microelectrodes to map neural activity across 2D lattice. Taking advantage from the high-modularity of our electrode-pixels-based SiNAPS technology, we realized a four shanks active dense probe with 256 electrode-pixels/shank and a pitch of 28 µm, for a total of 1024 simultaneously recording channels. The achieved performances allow for full-band, whole-array read-outs at 25 kHz/channel, show a measured input referred noise in the action potential band (300-7000 Hz) of 6.5 ± 2.1µVRMS, and a power consumption <6 µW/electrode-pixel. Preliminary recordings in awake behaving mice demonstrated the capability of multi-shanks SiNAPS probes to simultaneously record neural activity (both LFPs and spikes) from a brain area >6 mm2, spanning cortical, hippocampal and thalamic regions. High-density 2D array enables combining large population unit recording across distributed networks with precise intra- and interlaminar/nuclear mapping of the oscillatory dynamics. These results pave the way to a new generation of high-density and extremely compact multi-shanks CMOS-probes with tunable layouts for electrophysiological mapping of brain activity at the single-neurons resolution.


Author(s):  
L. Romero ◽  
J.M. Ferrero ◽  
J. Saiz ◽  
B. Trenor ◽  
M. Monserrat ◽  
...  

Author(s):  
Davide Lonardoni ◽  
Hayder Amin ◽  
Stefano Zordan ◽  
Fabio Boi ◽  
Aziliz Lecomte ◽  
...  

Micromachines ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 944 ◽  
Author(s):  
Douglas Shire ◽  
Marcus Gingerich ◽  
Patricia Wong ◽  
Michael Skvarla ◽  
Stuart Cogan ◽  
...  

We present a retrospective of unique micro-fabrication problems and solutions that were encountered through over 10 years of retinal prosthesis product development, first for the Boston Retinal Implant Project initiated at the Massachusetts Institute of Technology and at Harvard Medical School’s teaching hospital, the Massachusetts Eye and Ear—and later at the startup company Bionic Eye Technologies, by some of the same personnel. These efforts culminated in the fabrication and assembly of 256+ channel visual prosthesis devices having flexible multi-electrode arrays that were successfully implanted sub-retinally in mini-pig animal models as part of our pre-clinical testing program. We report on the processing of the flexible multi-layered, planar and penetrating high-density electrode arrays, surgical tools for sub-retinal implantation, and other parts such as coil supports that facilitated the implantation of the peri-ocular device components. We begin with an overview of the implantable portion of our visual prosthesis system design, and describe in detail the micro-fabrication methods for creating the parts of our system that were assembled outside of our hermetically-sealed electronics package. We also note the unique surgical challenges that sub-retinal implantation of our micro-fabricated components presented, and how some of those issues were addressed through design, materials selection, and fabrication approaches.


2018 ◽  
Vol 13 (10) ◽  
pp. 972-972 ◽  
Author(s):  
Michele Dipalo ◽  
Giovanni Melle ◽  
Laura Lovato ◽  
Andrea Jacassi ◽  
Francesca Santoro ◽  
...  

2018 ◽  
Vol 17 (3) ◽  
pp. 1-19
Author(s):  
Yahya H. Yassin ◽  
Francky Catthoor ◽  
Fabian Kloosterman ◽  
Jyh-Jang Sun ◽  
JoãO Couto ◽  
...  

2002 ◽  
Author(s):  
Yutaka Kashihara ◽  
Naoki Morishita ◽  
Kazuo Watabe ◽  
Chosaku Noda ◽  
Katsuo Iwata ◽  
...  

2021 ◽  
Author(s):  
Alessio Paolo Buccino ◽  
Xinyue Yuan ◽  
Vishalini Emmenegger ◽  
Xiaohan Xue ◽  
Tobias Gaenswein ◽  
...  

Neurons communicate with each other by sending action potentials through their axons. The velocity of axonal signal propagation describes how fast electrical action potentials can travel, and can be affected in a human brain by several pathologies, including multiple sclerosis, traumatic brain injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural electrical activity. The high density of the recording electrodes enables to image the activity of individual neurons down to subcellular resolution, which includes the propagation of axonal signals. However, axon reconstruction, to date, mainly relies on a manual approach to select the electrodes and channels that seemingly record the signals along a specific axon, while an automated approach to track multiple axonal branches in extracellular action-potential recordings is still missing. In this article, we propose a fully automated approach to reconstruct axons from extracellular electrical-potential landscapes, so-called "electrical footprints" of neurons. After an initial electrode and channel selection, the proposed method first constructs a graph, based on the voltage signal amplitudes and latencies. Then, the graph is interrogated to extract possible axonal branches. Finally, the axonal branches are pruned and axonal action-potential propagation velocities are computed. We first validate our method using simulated data from detailed reconstructions of neurons, showing that our approach is capable of accurately reconstructing axonal branches. We then apply the reconstruction algorithm to experimental recordings of HD-MEAs and show that it can be used to determine axonal morphologies and signal-propagation velocities at high throughput. We introduce a fully automated method to reconstruct axonal branches and estimate axonal action-potential propagation velocities using HD-MEA recordings. Our method yields highly reliable and reproducible velocity estimations, which constitute an important electrophysiological feature of neuronal preparations.


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