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Wang F, Chen X, Roelfsema PR. Comparison of electrical microstimulation artifact removal methods for high-channel-count prostheses. J Neurosci Methods 2024; 408:110169. [PMID: 38782123 DOI: 10.1016/j.jneumeth.2024.110169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/15/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Neuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as blindness, paralysis, and other movement and psychiatric disorders. Recordings are often made simultaneously with stimulation, allowing the monitoring of neural signals and closed-loop control of devices. However, stimulation-evoked artifacts may obscure neural activity, particularly when stimulation and recording sites are nearby. Several methods have been developed to remove stimulation artifacts, but it remains challenging to validate and compare these methods because the 'ground-truth' of the neuronal signals may be contaminated by artifacts. NEW METHOD Here, we delivered stimulation to the visual cortex via a high-channel-count prosthesis while recording neuronal activity and stimulation artifacts. We quantified the waveforms and temporal properties of stimulation artifacts from the cortical visual prosthesis (CVP) and used them to build a dataset, in which we simulated the neuronal activity and the stimulation artifacts. We illustrate how to use the simulated data to evaluate the performance of six software-based artifact removal methods (Template subtraction, Linear interpolation, Polynomial fitting, Exponential fitting, SALPA and ERAASR) in a CVP application scenario. RESULTS We here focused on stimulation artifacts caused by electrical stimulation through a high-channel-count cortical prosthesis device. We find that the Polynomial fitting and Exponential fitting methods outperform the other methods in recovering spikes and multi-unit activity. Linear interpolation and Template subtraction recovered the local-field potentials. CONCLUSION Polynomial fitting and Exponential fitting provided a good trade-off between the quality of the recovery of spikes and multi-unit activity (MUA) and the computational complexity for a cortical prosthesis.
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Affiliation(s)
- Feng Wang
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), Amsterdam 1105 BA, the Netherlands.
| | - Xing Chen
- Department of Ophthalmology, University of Pittsburgh School of Medicine, 203 Lothrop St, Pittsburgh, PA 15213, US.
| | - Pieter R Roelfsema
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), Amsterdam 1105 BA, the Netherlands; Department of Ophthalmology, University of Pittsburgh School of Medicine, 203 Lothrop St, Pittsburgh, PA 15213, US; Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, Amsterdam 1081 HV, the Netherlands; Department of Neurosurgery, Academic Medical Centre, Postbus 22660, Amsterdam 1100 DD, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris F-75012, France.
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2
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Antin B, Sadahiro M, Gajowa M, Triplett MA, Adesnik H, Paninski L. Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization. PLoS Comput Biol 2024; 20:e1012053. [PMID: 38709828 PMCID: PMC11098512 DOI: 10.1371/journal.pcbi.1012053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 05/16/2024] [Accepted: 04/03/2024] [Indexed: 05/08/2024] Open
Abstract
Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical excitation of nearby cells can induce direct photocurrents in the postsynaptic cell. These photocurrent artifacts contaminate synaptic currents, making it difficult or impossible to probe connectivity for nearby cells. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are less variable than those of synaptic currents. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available as open source software.
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Affiliation(s)
- Benjamin Antin
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Masato Sadahiro
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Marta Gajowa
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Marcus A. Triplett
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
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3
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Shokri M, Gogliettino AR, Hottowy P, Sher A, Litke AM, Chichilnisky EJ, Pequito S, Muratore D. Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach. J Neural Eng 2024; 21:016022. [PMID: 38271715 PMCID: PMC10853761 DOI: 10.1088/1741-2552/ad228f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/08/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts.Approach. Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts.Main results. We applied our method to high-density multi-electrode recordings from the primate retina in anex vivosetup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R2=0.951for human 1 andR2=0.944for human 2).Significance. Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.
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Affiliation(s)
- Mohammad Shokri
- Delft Center for Systems and Control, Delft University of Technology, Delft 2628 CN, The Netherlands
| | - Alex R Gogliettino
- Neurosciences PhD Program, Stanford University, Stanford, CA 94305, United States of America
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, United States of America
| | - Paweł Hottowy
- Faculty of Physics and Applied Computer Science, AGH University of Krakow, Krakow, Poland
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - E J Chichilnisky
- Departments of Neurosurgery and Ophthalmology, Stanford University, Stanford, CA 94305, United States of America
| | - Sérgio Pequito
- Division of Systems and Control, Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden
| | - Dante Muratore
- Microelectronics Department, Delft University of Technology, Delft 2628 CN, The Netherlands
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4
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Xie T, Foutz TJ, Adamek M, Swift JR, Inman CS, Manns JR, Leuthardt EC, Willie JT, Brunner P. Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM). J Neural Eng 2023; 20:066036. [PMID: 38063368 PMCID: PMC10751949 DOI: 10.1088/1741-2552/ad1385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/02/2023] [Accepted: 12/07/2023] [Indexed: 12/28/2023]
Abstract
Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.
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Affiliation(s)
- Tao Xie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
| | - Thomas J Foutz
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Markus Adamek
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States of America
| | - James R Swift
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
| | - Cory S Inman
- Department of Psychology, University of Utah, Salt Lake City, UT, United States of America
| | - Joseph R Manns
- Department of Psychology, Emory University, Atlanta, GA, United States of America
| | - Eric C Leuthardt
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
| | - Peter Brunner
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
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5
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Wu Y, Li BZ, Wang L, Fan S, Chen C, Li A, Lin Q, Wang P. An unsupervised real-time spike sorting system based on optimized OSort. J Neural Eng 2023; 20:066015. [PMID: 37972395 DOI: 10.1088/1741-2552/ad0d15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. TheCCmethod not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5-80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.
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Affiliation(s)
- Yingjiang Wu
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
| | - Ben-Zheng Li
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, United States of America
| | - Liyang Wang
- State Key Laboratory of Analog and Mixed Signal VLSI, University of Macau, Macau, People's Republic of China
- Department of Electrical and Computer Engineering, University of Macau, Macau, People's Republic of China
| | - Shaocan Fan
- School of Electronics and Communication Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen, People's Republic of China
| | - Changhao Chen
- Zhuhai Hokai Medical Instruments Co., Ltd, Zhuhai, People's Republic of China
| | - Anan Li
- Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, People's Republic of China
| | - Qin Lin
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
| | - Panke Wang
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
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6
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Meyer LM, Samann F, Schanze T. DualSort: online spike sorting with a running neural network. J Neural Eng 2023; 20:056031. [PMID: 37795548 DOI: 10.1088/1741-2552/acfb3a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Abstract
Objective.Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction.Approach.Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results.With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline.Significance.This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.
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Affiliation(s)
- L M Meyer
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - F Samann
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
- Department of Biomedical Engineering, University of Duhok, Kurdistan Region, Iraq
| | - T Schanze
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
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7
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Madugula SS, Vilkhu R, Shah NP, Grosberg LE, Kling A, Gogliettino AR, Nguyen H, Hottowy P, Sher A, Litke AM, Chichilnisky EJ. Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells. J Neurosci 2023; 43:4808-4820. [PMID: 37268418 PMCID: PMC10312054 DOI: 10.1523/jneurosci.1023-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical sensitivity of a large population of target neurons, to precisely control their activity, can be difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale multielectrode stimulation and recording from retinal ganglion cells (RGCs) of male and female macaque monkeys ex vivo Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds across cell types, retinas, and eccentricities, with systematic and distinct trends for somas and axons. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by their recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These trends were quantitatively reproduced with biophysical simulations. Results from human RGCs were broadly similar. The inference of stimulation sensitivity from recorded electrical features was tested in a data-driven simulation of visual reconstruction, revealing that the approach could significantly improve the function of future high-fidelity retinal implants.SIGNIFICANCE STATEMENT This study demonstrates that individual in situ primate retinal ganglion cells of different types respond to artificially generated, external electrical fields in a systematic manner, in accordance with theoretical predictions, that allows for prediction of electrical stimulus sensitivity from recorded spontaneous activity. It also provides evidence that such an approach could be immensely helpful in the calibration of clinical retinal implants.
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Affiliation(s)
- Sasidhar S Madugula
- Neurosciences PhD Program, Stanford University, Stanford, California 94305
- School of Medicine, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Ramandeep Vilkhu
- Department of Electrical Engineering, Stanford University, Stanford, California 94305
| | - Nishal P Shah
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Department of Electrical Engineering, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Lauren E Grosberg
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
- Facebook Reality Labs, Facebook, Mountain View, California 94040
| | - Alexandra Kling
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Alex R Gogliettino
- Neurosciences PhD Program, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Huy Nguyen
- Department of Neurosurgery, Stanford University, Stanford, California 94305
| | - Paweł Hottowy
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland 30-059
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
| | - E J Chichilnisky
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Department of Ophthalmology, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
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8
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Bayat FK, Alp Mİ, Bostan S, Gülçür HÖ, Öztürk G, Güveniş A. An improved platform for cultured neuronal network electrophysiology: multichannel optogenetics integrated with MEAs. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2022; 51:503-514. [PMID: 35930029 DOI: 10.1007/s00249-022-01613-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/11/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
Cultured neuronal networks (CNNs) are powerful tools for studying how neuronal representation and adaptation emerge in networks of controlled populations of neurons. To ensure the interaction of a CNN and an artificial setting, reliable operation in both open and closed loops should be provided. In this study, we integrated optogenetic stimulation with microelectrode array (MEA) recordings using a digital micromirror device and developed an improved research tool with a 64-channel interface for neuronal network control and data acquisition. We determined the ideal stimulation parameters including light intensity, frequency, and duty cycle for our configuration. This resulted in robust and reproducible neuronal responses. We also demonstrated both open and closed loop configurations in the new platform involving multiple bidirectional channels. Unlike previous approaches that combined optogenetic stimulation and MEA recordings, we did not use binary grid patterns, but assigned an adjustable-size, non-binary optical spot to each electrode. This approach allowed simultaneous use of multiple input-output channels and facilitated adaptation of the stimulation parameters. Hence, we advanced a 64-channel interface in that each channel can be controlled individually in both directions simultaneously without any interference or interrupts. The presented setup meets the requirements of research in neuronal plasticity, network encoding and representation, closed-loop control of firing rate and synchronization. Researchers who develop closed-loop control techniques and adaptive stimulation strategies for network activity will benefit much from this novel setup.
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Affiliation(s)
- F Kemal Bayat
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey.
| | - M İkbal Alp
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Sevginur Bostan
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - H Özcan Gülçür
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Gürkan Öztürk
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Albert Güveniş
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
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9
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Bod RB, Rokai J, Meszéna D, Fiáth R, Ulbert I, Márton G. From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Front Neuroinform 2022; 16:851024. [PMID: 35769832 PMCID: PMC9236662 DOI: 10.3389/fninf.2022.851024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/06/2022] [Indexed: 11/15/2022] Open
Abstract
The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.
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Affiliation(s)
- Réka Barbara Bod
- Laboratory of Experimental Neurophysiology, Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, Romania
| | - János Rokai
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Domokos Meszéna
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Richárd Fiáth
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Márton
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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Schmieder F, Habibey R, Striebel J, Büttner L, Czarske J, Busskamp V. Tracking connectivity maps in human stem cell-derived neuronal networks by holographic optogenetics. Life Sci Alliance 2022; 5:5/7/e202101268. [PMID: 35418473 PMCID: PMC9008225 DOI: 10.26508/lsa.202101268] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 11/24/2022] Open
Abstract
Holographic optogenetic stimulation of human iPSC–derived neuronal networks was exploited to map precise functional connectivity motifs and their long-term dynamics during network development. Neuronal networks derived from human induced pluripotent stem cells have been exploited widely for modeling neuronal circuits, neurological diseases, and drug screening. As these networks require extended culturing periods to functionally mature in vitro, most studies are based on immature networks. To obtain insights on long-term functional features, we improved a glia–neuron co-culture protocol within multi-electrode arrays, facilitating continuous assessment of electrical features in weekly intervals. By full-field optogenetic stimulation, we detected an earlier onset of neuronal firing and burst activity compared with spontaneous activity. Full-field stimulation enhanced the number of active neurons and their firing rates. Compared with full-field stimulation, which evoked synchronized activity across all neurons, holographic stimulation of individual neurons resulted in local activity. Single-cell holographic stimulation facilitated to trace propagating evoked activities of 400 individually stimulated neurons per multi-electrode array. Thereby, we revealed precise functional neuronal connectivity motifs. Holographic stimulation data over time showed increasing connection numbers and strength with culture age. This holographic stimulation setup has the potential to establish a profound functional testbed for in-depth analysis of human-induced pluripotent stem cell-derived neuronal networks.
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Affiliation(s)
- Felix Schmieder
- Laboratory of Measurement and Sensor System Technique, Faculty of Electrical and Computer Engineering, TU Dresden, Dresden, Germany
| | - Rouhollah Habibey
- Department of Ophthalmology, Universitäts-Augenklinik Bonn, University of Bonn, Bonn, Germany
| | - Johannes Striebel
- Department of Ophthalmology, Universitäts-Augenklinik Bonn, University of Bonn, Bonn, Germany
| | - Lars Büttner
- Laboratory of Measurement and Sensor System Technique, Faculty of Electrical and Computer Engineering, TU Dresden, Dresden, Germany
| | - Jürgen Czarske
- Laboratory of Measurement and Sensor System Technique, Faculty of Electrical and Computer Engineering, TU Dresden, Dresden, Germany .,Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Dresden, Germany.,Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany.,Institute of Applied Physics, School of Science, TU Dresden, Dresden, Germany
| | - Volker Busskamp
- Department of Ophthalmology, Universitäts-Augenklinik Bonn, University of Bonn, Bonn, Germany
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11
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Schelles M, Wouters J, Asamoah B, Mc Laughlin M, Bertrand A. Objective evaluation of stimulation artefact removal techniques in the context of neural spike sorting. J Neural Eng 2022; 19. [DOI: 10.1088/1741-2552/ac4ecf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 01/25/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective - We present a framework to objectively test and compare stimulation artefact removal techniques in the context of neural spike sorting. Approach - To this end, we used realistic hybrid ground-truth spiking data, with superimposed artefacts from in vivo recordings. We used the framework to evaluate and compare several techniques: blanking, template subtraction by averaging, linear regression, and a multi-channel Wiener filter (MWF). Main results - Our study demonstrates that blanking and template subtraction result in a poorer spike sorting performance than linear regression and MWF, while the latter two perform similarly. Finally, to validate the conclusions found from the hybrid evaluation framework, we also performed a qualitative analysis on in vivo recordings without artificial manipulations. Significance - Our framework allows direct quantification of the impact of the residual artefact on the spike sorting accuracy, thereby allowing for a more objective and more relevant comparison compared to indirect signal quality metrics that are estimated from the signal statistics. Furthermore, the availability of a ground truth in the form of single-unit spiking activity also facilitates a better estimation of such signal quality metrics.
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12
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Tandon P, Bhaskhar N, Shah N, Madugula S, Grosberg L, Fan VH, Hottowy P, Sher A, Litke AM, Chichilnisky EJ, Mitra S. Automatic Identification of Axon Bundle Activation for Epiretinal Prosthesis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2496-2502. [PMID: 34784278 PMCID: PMC8860174 DOI: 10.1109/tnsre.2021.3128486] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Objective: Retinal prostheses must be able to activate cells in a selective way in order to restore high-fidelity vision. However, inadvertent activation of far-away retinal ganglion cells (RGCs) through electrical stimulation of axon bundles can produce irregular and poorly controlled percepts, limiting artificial vision. In this work, we aim to provide an algorithmic solution to the problem of detecting axon bundle activation with a bi-directional epiretinal prostheses. Methods: The algorithm utilizes electrical recordings to determine the stimulation current amplitudes above which axon bundle activation occurs. Bundle activation is defined as the axonal stimulation of RGCs with unknown soma and receptive field locations, typically beyond the electrode array. The method exploits spatiotemporal characteristics of electrically-evoked spikes to overcome the challenge of detecting small axonal spikes. Results: The algorithm was validated using large-scale, single-electrode and short pulse, ex vivo stimulation and recording experiments in macaque retina, by comparing algorithmically and manually identified bundle activation thresholds. For 88% of the electrodes analyzed, the threshold identified by the algorithm was within ±10% of the manually identified threshold, with a correlation coefficient of 0.95. Conclusion: This works presents a simple, accurate and efficient algorithm to detect axon bundle activation in epiretinal prostheses. Significance: The algorithm could be used in a closed-loop manner by a future epiretinal prosthesis to reduce poorly controlled visual percepts associated with bundle activation. Activation of distant cells via axonal stimulation will likely occur in other types of retinal implants and cortical implants, and the method may therefore be broadly applicable.
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13
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Vilkhu RS, Madugula SS, Grosberg LE, Gogliettino AR, Hottowy P, Dabrowski W, Sher A, Litke AM, Mitra S, Chichilnisky EJ. Spatially patterned bi-electrode epiretinal stimulation for axon avoidance at cellular resolution. J Neural Eng 2021; 18. [PMID: 34710857 DOI: 10.1088/1741-2552/ac3450] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/28/2021] [Indexed: 11/12/2022]
Abstract
Objective.Epiretinal prostheses are designed to restore vision to people blinded by photoreceptor degenerative diseases by stimulating surviving retinal ganglion cells (RGCs), which carry visual signals to the brain. However, inadvertent stimulation of RGCs at their axons can result in non-focal visual percepts, limiting the quality of artificial vision. Theoretical work has suggested that axon activation can be avoided with current stimulation designed to minimize the second spatial derivative of the induced extracellular voltage along the axon. However, this approach has not been verified experimentally at the resolution of single cells.Approach.In this work, a custom multi-electrode array (512 electrodes, 10μm diameter, 60μm pitch) was used to stimulate and record RGCs in macaque retinaex vivoat single-cell, single-spike resolution. RGC activation thresholds resulting from bi-electrode stimulation, which consisted of bipolar currents simultaneously delivered through two electrodes straddling an axon, were compared to activation thresholds from traditional single-electrode stimulation.Main results.On average, across three retinal preparations, the bi-electrode stimulation strategy reduced somatic activation thresholds (∼21%) while increasing axonal activation thresholds (∼14%), thus favoring selective somatic activation. Furthermore, individual examples revealed rescued selective activation of somas that was not possible with any individual electrode.Significance.This work suggests that a bi-electrode epiretinal stimulation strategy can reduce inadvertent axonal activation at cellular resolution, for high-fidelity artificial vision.
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Affiliation(s)
- Ramandeep S Vilkhu
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - Sasidhar S Madugula
- Departments of Neurosurgery, Ophthalmology, and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America
| | - Lauren E Grosberg
- Departments of Neurosurgery, Ophthalmology, and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America
| | - Alex R Gogliettino
- Departments of Neurosurgery, Ophthalmology, and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America
| | - Pawel Hottowy
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow 30-059, Poland
| | - Wladyslaw Dabrowski
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow 30-059, Poland
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - Subhasish Mitra
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - E J Chichilnisky
- Departments of Neurosurgery, Ophthalmology, and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America
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14
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Perez-Prieto N, Rodriguez-Vazquez A, Alvarez-Dolado M, Delgado-Restituto M. A 32-Channel Time-Multiplexed Artifact-Aware Neural Recording System. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:960-977. [PMID: 34460384 DOI: 10.1109/tbcas.2021.3108725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This paper presents a low-power, low-noise microsystem for the recording of neural local field potentials or intracranial electroencephalographic signals. It features 32 time-multiplexed channels at the electrode interface and offers the possibility to spatially delta encode data to take advantage of the large correlation of signals captured from nearby channels. The circuit also implements a mixed-signal voltage-triggered auto-ranging algorithm which allows to attenuate large interferers in digital domain while preserving neural information. This effectively increases the system dynamic range and avoids the onset of saturation. A prototype, fabricated in a standard 180 nm CMOS process, has been experimentally verified in-vitro with cellular cultures of primary cortical neurons from mice. The system shows an integrated input-referred noise in the 0.5-200 Hz band of 1.4 μVrms for a spot noise of about 85 nV /√{Hz}. The system draws 1.5 μW per channel from 1.2 V supply and obtains 71 dB + 26 dB dynamic range when the artifact-aware auto-ranging mechanism is enabled, without penalising other critical specifications such as crosstalk between channels or common-mode and power supply rejection ratios.
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15
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Dastin-van Rijn EM, Provenza NR, Calvert JS, Gilron R, Allawala AB, Darie R, Syed S, Matteson E, Vogt GS, Avendano-Ortega M, Vasquez AC, Ramakrishnan N, Oswalt DN, Bijanki KR, Wilt R, Starr PA, Sheth SA, Goodman WK, Harrison MT, Borton DA. Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method. CELL REPORTS METHODS 2021; 1:100010. [PMID: 34532716 PMCID: PMC8443190 DOI: 10.1016/j.crmeth.2021.100010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/08/2021] [Accepted: 04/21/2021] [Indexed: 10/26/2022]
Abstract
Advances in therapeutic neuromodulation devices have enabled concurrent stimulation and electrophysiology in the central nervous system. However, stimulation artifacts often obscure the sensed underlying neural activity. Here, we develop a method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker are used for validation. Performance is found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: (1) it is superior in signal recovery; (2) it is easily adaptable to several neurostimulation paradigms; and (3) it has low complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.
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Affiliation(s)
| | - Nicole R. Provenza
- Brown University School of Engineering, Providence, RI, USA
- Charles Stark Draper Laboratory, Cambridge, MA, USA
| | | | - Ro'ee Gilron
- Deparment of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | | | - Radu Darie
- Brown University School of Engineering, Providence, RI, USA
| | - Sohail Syed
- Department of Neurosurgery, Warren Alpert School of Medicine of Brown University, Providence, RI, USA
| | - Evan Matteson
- Brown University School of Engineering, Providence, RI, USA
| | - Gregory S. Vogt
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michelle Avendano-Ortega
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ana C. Vasquez
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nithya Ramakrishnan
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Denise N. Oswalt
- Department of Neurosurgery, Perelman School of Medicine, Philadelphia, PA, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Robert Wilt
- Deparment of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Philip A. Starr
- Deparment of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Wayne K. Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - David A. Borton
- Brown University School of Engineering, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs, Providence, RI, USA
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16
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Shah NP, Chichilnisky EJ. Computational challenges and opportunities for a bi-directional artificial retina. J Neural Eng 2020; 17:055002. [PMID: 33089827 DOI: 10.1088/1741-2552/aba8b1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve more acute visual perception will also require substantial computational advances. Using high-density large-scale recording and stimulation in the primate retina with an ex vivo multi-electrode array lab prototype, we frame several of the major computational problems, and describe current progress and future opportunities in solving them. First, we identify cell types and locations from spontaneous activity in the blind retina, and then efficiently estimate their visual response properties by using a low-dimensional manifold of inter-retina variability learned from a large experimental dataset. Second, we estimate retinal responses to a large collection of relevant electrical stimuli by passing current patterns through an electrode array, spike sorting the resulting recordings and using the results to develop a model of evoked responses. Third, we reproduce the desired responses for a given visual target by temporally dithering a diverse collection of electrical stimuli within the integration time of the visual system. Together, these novel approaches may substantially enhance artificial vision in a next-generation device.
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Affiliation(s)
- Nishal P Shah
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America. Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America. Department of Neurosurgery, Stanford University, Stanford, CA, United States of America. Author to whom any correspondence should be addressed
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17
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Sadeghi Najafabadi M, Chen L, Dutta K, Norris A, Feng B, Schnupp JWH, Rosskothen-Kuhl N, Read HL, Escabí MA. Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces. Front Neurosci 2020; 14:709. [PMID: 32765212 PMCID: PMC7379342 DOI: 10.3389/fnins.2020.00709] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 06/11/2020] [Indexed: 11/13/2022] Open
Abstract
Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.
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Affiliation(s)
- Mina Sadeghi Najafabadi
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
| | - Longtu Chen
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Kelsey Dutta
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
| | - Ashley Norris
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Bin Feng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Jan W. H. Schnupp
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
| | - Nicole Rosskothen-Kuhl
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Neurobiological Research Laboratory, Section for Clinical and Experimental Otology, University Medical Center Freiburg, Freiburg im Breisgau, Germany
| | - Heather L. Read
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
- Department of Psychology, University of Connecticut, Storrs, CT, United States
- The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, United States
| | - Monty A. Escabí
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
- Department of Psychology, University of Connecticut, Storrs, CT, United States
- The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, United States
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18
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Caldwell DJ, Cronin JA, Rao RPN, Collins KL, Weaver KE, Ko AL, Ojemann JG, Kutz JN, Brunton BW. Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning. J Neural Eng 2020; 17:026023. [PMID: 32103828 PMCID: PMC7333778 DOI: 10.1088/1741-2552/ab7a4f] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available. APPROACH We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings. MAIN RESULTS We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation. SIGNIFICANCE Our work will enable future advances in neural engineering with simultaneous stimulation and recording.
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Affiliation(s)
- D J Caldwell
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America. Medical Scientist Training Program, University of Washington, Seattle, WA, United States of America. Center for Neurotechnology, Seattle, WA, United States of America. University of Washington Institute for Neuroengineering, Seattle, WA, United States of America. Author to whom any correspondence should be addressed
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19
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Hughes C, Herrera A, Gaunt R, Collinger J. Bidirectional brain-computer interfaces. BRAIN-COMPUTER INTERFACES 2020; 168:163-181. [DOI: 10.1016/b978-0-444-63934-9.00013-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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20
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Drebitz E, Rausch LP, Kreiter AK. A novel approach for removing micro-stimulation artifacts and reconstruction of broad-band neuronal signals. J Neurosci Methods 2019; 332:108549. [PMID: 31837345 DOI: 10.1016/j.jneumeth.2019.108549] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 12/03/2019] [Accepted: 12/10/2019] [Indexed: 01/14/2023]
Abstract
BACKGROUND Electrical stimulation is a widely used method in the neurosciences with a variety of application fields. However, stimulation frequently induces large and long-lasting artifacts, which superimpose on the actual neuronal signal. Existing methods were developed for analyzing fast events such as spikes, but are not well suited for the restoration of LFP signals. NEW METHOD We developed a method that extracts artifact components while also leaving the LFP components of the neuronal signal intact. We based it on an exponential fit of the average artifact shape, which is subsequently adapted to the individual artifacts amplitude and then subtracted. Importantly, we used for fitting of the individual artifact only a short initial time window, in which the artifact is dominating the superimposition with the neuronal signal. Using this short period ensures that LFP components are not part of the fit, which leaves them unaffected by the subsequent artifact removal. RESULTS By using the method presented here, we could diminish the substantial distortions of neuronal signals caused by electrical stimulation to levels that were statistically indistinguishable from the original data. Furthermore, the effect of stimulation on the phases of γ- and β- oscillations was reduced by 85 and 75 %, respectively. COMPARISON WITH EXISTING METHODS This approach avoids signal loss as caused by methods cutting out artifacts and minimizes the distortion of the signal's temporal structure as compared to other approaches. CONCLUSION The method presented here allows for a successful reconstruction of broad-band signals.
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Affiliation(s)
- Eric Drebitz
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Germany.
| | - Lukas-Paul Rausch
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Germany
| | - Andreas K Kreiter
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Germany
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21
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Shadmani A, Viswam V, Chen Y, Bounik R, Dragas J, Radivojevic M, Geissler S, Sitnikov S, Muller J, Hierlemann A. Stimulation and Artifact-Suppression Techniques for In Vitro High-Density Microelectrode Array Systems. IEEE Trans Biomed Eng 2019; 66:2481-2490. [PMID: 30605090 PMCID: PMC6711758 DOI: 10.1109/tbme.2018.2890530] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
We present novel voltage stimulation buffers with controlled output current, along with recording circuits featuring adjustable high-pass cut-off filtering to perform efficient stimulation while actively suppressing stimulation artifacts in high-density microelectrode arrays. Owing to the dense packing and close proximity of the electrodes in such systems, a stimulation through one electrode can cause large electrical artifacts on neighboring electrodes that easily saturate the corresponding recording amplifiers. To suppress such artifacts, the high-pass corner frequencies of all available 2048 recording channels can be raised from several Hz to several kHz by applying a "soft-reset" or pole-shifting technique. With the implemented artifact suppression technique, the saturation time of the recording circuits, connected to electrodes in immediate vicinity to the stimulation site, could be reduced to less than 150 μs. For the stimulation buffer, we developed a circuit, which can operate in two modes: either control of only the stimulation voltage or control of current and voltage during stimulation. The voltage-only controlled mode employs a local common-mode feedback operational transconductance amplifier with a near rail-to-rail input/output range, suitable for driving high-capacitive loads. The current/voltage controlled mode is based on a positive current conveyor generating adjustable output currents, whereas its upper and lower output voltages are limited by two feedback loops. The current/voltage controlled circuit can generate stimulation pulses up to 30 μA with less than ±0.1% linearity error in the low-current mode and up to 300 μA with less than ±0.2% linearity error in the high-current mode.
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22
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Towards neural co-processors for the brain: combining decoding and encoding in brain-computer interfaces. Curr Opin Neurobiol 2019; 55:142-151. [PMID: 30954862 DOI: 10.1016/j.conb.2019.03.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 03/13/2019] [Accepted: 03/14/2019] [Indexed: 12/18/2022]
Abstract
The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device. Such a device acts as a 'co-processor' for the brain, with applications ranging from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. We review recent progress in simultaneous decoding and encoding for closed-loop control and plasticity induction. To address the challenge of multi-channel decoding and encoding, we introduce a unifying framework for developing brain co-processors based on artificial neural networks and deep learning. These 'neural co-processors' can be used to jointly optimize cost functions with the nervous system to achieve desired behaviors ranging from targeted neuro-rehabilitation to augmentation of brain function.
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23
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Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Curr Opin Neurobiol 2019; 50:232-241. [PMID: 29738986 DOI: 10.1016/j.conb.2018.04.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 03/12/2018] [Accepted: 04/06/2018] [Indexed: 01/01/2023]
Abstract
Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.
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Affiliation(s)
- L Paninski
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States; Department of Neuroscience, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States.
| | - J P Cunningham
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States
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Progress towards restoring upper limb movement and sensation through intracortical brain-computer interfaces. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2018. [DOI: 10.1016/j.cobme.2018.11.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Weiss JM, Flesher SN, Franklin R, Collinger JL, Gaunt RA. Artifact-free recordings in human bidirectional brain–computer interfaces. J Neural Eng 2018; 16:016002. [DOI: 10.1088/1741-2552/aae748] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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26
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Fan VH, Grosberg LE, Madugula SS, Hottowy P, Dabrowski W, Sher A, Litke AM, Chichilnisky EJ. Epiretinal stimulation with local returns enhances selectivity at cellular resolution. J Neural Eng 2018; 16:025001. [PMID: 30523958 DOI: 10.1088/1741-2552/aaeef1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Epiretinal prostheses are designed to restore vision in people blinded by photoreceptor degenerative diseases, by directly activating retinal ganglion cells (RGCs) using an electrode array implanted on the retina. In present-day clinical devices, current spread from the stimulating electrode to a distant return electrode often results in the activation of many cells, potentially limiting the quality of artificial vision. In the laboratory, epiretinal activation of RGCs with cellular resolution has been demonstrated with small electrodes, but distant returns may still cause undesirable current spread. Here, the ability of local return stimulation to improve the selective activation of RGCs at cellular resolution was evaluated. APPROACH A custom multi-electrode array (512 electrodes, 10 μm diameter, 60 μm pitch) was used to simultaneously stimulate and record from RGCs in isolated primate retina. Stimulation near the RGC soma with a single electrode and a distant return was compared to stimulation in which the return was provided by six neighboring electrodes. MAIN RESULTS Local return stimulation enhanced the capability to activate cells near the central electrode (<30 μm) while avoiding cells farther away (>30 μm). This resulted in an improved ability to selectively activate ON and OFF cells, including cells encoding immediately adjacent regions in the visual field. SIGNIFICANCE These results suggest that a device that restricts the electric field through local returns could optimize activation of neurons at cellular resolution, improving the quality of artificial vision.
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Affiliation(s)
- Victoria H Fan
- Departments of Neurosurgery, Ophthalmology, and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America
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Jung H, Kim J, Nam Y. Recovery of early neural spikes from stimulation electrodes using a DC-coupled low gain high resolution data acquisition system. J Neurosci Methods 2018; 304:118-125. [PMID: 29709657 DOI: 10.1016/j.jneumeth.2018.04.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 04/16/2018] [Accepted: 04/18/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND Neural responses to electrical stimulation provide valuable information to probe and study the network function. Especially, recording neural responses from the stimulated site provides improved neural interfacing method. However, it is difficult to measure short-delayed responses at the stimulated electrode due to the saturation of the amplifier after stimulation which is called "stimulus artifact". Despite the advances in handling stimulation artifacts, it is still very challenging to deal with the artifacts if one tries to stimulate and record from the same electrode. NEW METHOD In this paper, we developed a system consisting of 24 bit ADC and low gain DC-amplifier which allows us to record the entire responses including saturation-free stimulus artifact and neural responses with excellent resolution. RESULTS Our approach showed saturation-free recording after stimulation, which makes it possible to recover neural spike as early as in 2 ms at the stimulating electrode with digital elimination methods. COMPARISON WITH EXISTING METHODS With our system we could record neural signals after stimulation that was difficult with high gain and high pass filtered recording system due to amplifier saturation. CONCLUSIONS Our new system can enhance interface performance with its higher robustness and with simple system configuration.
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Affiliation(s)
- Hyunjun Jung
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jintae Kim
- Department of Electronics Engineering, Konkuk University, Seoul, Republic of Korea
| | - Yoonkey Nam
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
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O'Shea DJ, Shenoy KV. ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings. J Neural Eng 2018; 15:026020. [PMID: 29265009 PMCID: PMC5833982 DOI: 10.1088/1741-2552/aaa365] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation prevent the detection of spiking activity on nearby recording electrodes, which obscures the neural population response evoked by stimulation. We sought to develop a method to clean artifact-corrupted electrode signals recorded on multielectrode arrays in order to recover the underlying neural spiking activity. APPROACH We created an algorithm, which performs estimation and removal of array artifacts via sequential principal components regression (ERAASR). This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The ERAASR algorithm requires no special hardware, imposes no requirements on the shape of the artifact or the multielectrode array geometry, and comprises sequential application of straightforward linear methods with intuitive parameters. The approach should be readily applicable to most datasets where stimulation does not saturate the recording amplifier. MAIN RESULTS The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. SIGNIFICANCE We hope that enabling simultaneous electrical stimulation and multielectrode array recording will help elucidate the causal links between neural activity and cognition and facilitate naturalistic sensory protheses.
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Affiliation(s)
- Daniel J O'Shea
- Neurosciences Program, Stanford University, Stanford, CA 94305, United States of America. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States of America
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Yger P, Spampinato GLB, Esposito E, Lefebvre B, Deny S, Gardella C, Stimberg M, Jetter F, Zeck G, Picaud S, Duebel J, Marre O. A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. eLife 2018; 7:e34518. [PMID: 29557782 PMCID: PMC5897014 DOI: 10.7554/elife.34518] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 03/19/2018] [Indexed: 01/03/2023] Open
Abstract
In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain 'ground truth' data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes.
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Affiliation(s)
- Pierre Yger
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
| | | | - Elric Esposito
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
| | | | - Stéphane Deny
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
| | - Christophe Gardella
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
- Laboratoire de Physique Statistique, CNRS, ENS, UPMC, 75005ParisFrance
| | - Marcel Stimberg
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
| | | | | | - Serge Picaud
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
| | - Jens Duebel
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
| | - Olivier Marre
- Institut de la Vision, INSERM UMRS 968, UPMC UM 80ParisFrance
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