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Wu S, Zhang X, Wang Y. Neural Manifold Constraint for Spike Prediction Models Under Behavioral Reinforcement. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2772-2781. [PMID: 39074025 DOI: 10.1109/tnsre.2024.3435568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Spike prediction models effectively predict downstream spike trains from upstream neural activity for neural prostheses. Such prostheses could potentially restore damaged neural communication pathways using predicted patterns to guide electrical stimulations on downstream. Since the ground truth of downstream neural activity is unavailable for subjects with the damage, reinforcement learning (RL) with behavior-level rewards becomes necessary for model training. However, existing models do not involve any constraint on the generated firing patterns and neglect the correlations among neural activities. Thus, the model outputs can greatly deviate from the natural range of neural activities, causing concerns for clinical usage. This study proposes the neural manifold constraint to solve this problem, shaping RL-generated spike trains in the feature space. The constraint terms describe the first and second order statistics of the neural manifold estimated from neural recordings during subjects' freely moving period. Then, the models can be optimized within the neural manifold by behavioral reinforcement. We test the method to predict primary motor cortex (M1) spikes from medial prefrontal (mPFC) spikes when rats perform the two-lever discrimination task. Results show that the neural activity generated by constrained models resembles the real M1 recordings. Compared with models without constraints, our approach achieves similar behavioral success rates, but reduces the mean squared error of neural firing by 61%. The constraints also increase the model's robustness across data segments and induce realistic neural correlations. Our method provides a promising tool to restore transregional communication with high behavioral performance and more realistic microscopic patterns.
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2
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Roeder BM, She X, Dakos AS, Moore B, Wicks RT, Witcher MR, Couture DE, Laxton AW, Clary HM, Popli G, Liu C, Lee B, Heck C, Nune G, Gong H, Shaw S, Marmarelis VZ, Berger TW, Deadwyler SA, Song D, Hampson RE. Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories. Front Comput Neurosci 2024; 18:1263311. [PMID: 38390007 PMCID: PMC10881797 DOI: 10.3389/fncom.2024.1263311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/19/2024] [Indexed: 02/24/2024] Open
Abstract
Objective Here, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject's own hippocampal spatiotemporal neural codes for memory. Approach We constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. Main results MDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation. Significance These results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.
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Affiliation(s)
- Brent M Roeder
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Xiwei She
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Alexander S Dakos
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Bryan Moore
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Robert T Wicks
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
- Johns Hopkins Children's Center, Baltimore, MD, United States
| | - Mark R Witcher
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
- Virginia Tech Carilion School of Medicine and Research Institute, Roanoke, VA, United States
| | - Daniel E Couture
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Adrian W Laxton
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | | | - Gautam Popli
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Charles Liu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - Brian Lee
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - Christianne Heck
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - George Nune
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - Hui Gong
- Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Susan Shaw
- Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Sam A Deadwyler
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Robert E Hampson
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
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Ezzyat Y, Kragel JE, Solomon EA, Lega BC, Aronson JP, Jobst BC, Gross RE, Sperling MR, Worrell GA, Sheth SA, Wanda PA, Rizzuto DS, Kahana MJ. Functional and anatomical connectivity predict brain stimulation's mnemonic effects. Cereb Cortex 2024; 34:bhad427. [PMID: 38041253 PMCID: PMC10793570 DOI: 10.1093/cercor/bhad427] [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: 08/27/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 12/03/2023] Open
Abstract
Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.
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Affiliation(s)
- Youssef Ezzyat
- Dept. of Psychology, Wesleyan University, Middletown, CT 06459, USA
| | - James E Kragel
- Dept. of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Ethan A Solomon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bradley C Lega
- Dept. of Neurosurgery, University of Texas Southwestern, Dallas, TX 75390, USA
| | - Joshua P Aronson
- Dept. of Neurosurgery, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Barbara C Jobst
- Dept. of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Robert E Gross
- Dept. of Neurosurgery, Emory University Hospital, Atlanta, GA 30322, USA
| | - Michael R Sperling
- Dept. of Neurology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA 19107, USA
| | | | - Sameer A Sheth
- Dept. of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Paul A Wanda
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel S Rizzuto
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Kahana
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
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4
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Emery BA, Hu X, Khanzada S, Kempermann G, Amin H. High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity. Biosens Bioelectron 2023; 237:115471. [PMID: 37379793 DOI: 10.1016/j.bios.2023.115471] [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: 01/05/2023] [Revised: 05/17/2023] [Accepted: 06/10/2023] [Indexed: 06/30/2023]
Abstract
Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different scales, the precise impact of experience on network-wide computational dynamics remains inaccessible due to the lack of applicable large-scale recording methodology. We here demonstrate a large-scale multi-site biohybrid brain circuity on-CMOS-based biosensor with an unprecedented spatiotemporal resolution of 4096 microelectrodes, which allows simultaneous electrophysiological assessment across the entire hippocampal-cortical subnetworks from mice living in an enriched environment (ENR) and standard-housed (SD) conditions. Our platform, empowered with various computational analyses, reveals environmental enrichment's impacts on local and global spatiotemporal neural dynamics, firing synchrony, topological network complexity, and large-scale connectome. Our results delineate the distinct role of prior experience in enhancing multiplexed dimensional coding formed by neuronal ensembles and error tolerance and resilience to random failures compared to standard conditions. The scope and depth of these effects highlight the critical role of high-density, large-scale biosensors to provide a new understanding of the computational dynamics and information processing in multimodal physiological and experience-dependent plasticity conditions and their role in higher brain functions. Knowledge of these large-scale dynamics can inspire the development of biologically plausible computational models and computational artificial intelligence networks and expand the reach of neuromorphic brain-inspired computing into new applications.
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Affiliation(s)
- Brett Addison Emery
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Xin Hu
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Shahrukh Khanzada
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Gerd Kempermann
- Research Group "Adult Neurogenesis", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany; Center for Regenerative Therapies TU Dresden (CRTD), Fetscherstraße 105, 01307, Dresden, Germany
| | - Hayder Amin
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany; TU Dresden, Faculty of Medicine Carl Gustav Carus, Bergstraße 53, 01069, Dresden, Germany.
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5
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B C Girard C, Song D. Adaptive octree meshes for simulation of extracellular electrophysiology. J Neural Eng 2023; 20:056028. [PMID: 37722378 DOI: 10.1088/1741-2552/acfabf] [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: 06/22/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Objective.The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations.Approach.This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly.Main results.In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes achieve accuracy comparable to high-resolution static meshes in an order of magnitude less time.Significance.The proposed simulation pipeline improves model scalability, allowing greater detail with fewer computational resources. The implementation is available as an open-source Python module, providing flexibility and ease of reuse for the broader research community.
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Affiliation(s)
- Christopher B C Girard
- Fowler School of Engineering, Chapman University, Orange, CA, United States of America
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Dong Song
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
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6
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Ezzyat Y, Kragel JE, Solomon EA, Lega BC, Aronson JP, Jobst BC, Gross RE, Sperling MR, Worrell GA, Sheth SA, Wanda PA, Rizzuto DS, Kahana MJ. Functional and anatomical connectivity predict brain stimulation's mnemonic effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550851. [PMID: 37609181 PMCID: PMC10441352 DOI: 10.1101/2023.07.27.550851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.
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Affiliation(s)
- Youssef Ezzyat
- Dept. of Psychology, Wesleyan University, Middletown CT 06459
| | | | - Ethan A. Solomon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia PA 19104
| | - Bradley C. Lega
- Dept. of Neurosurgery, University of Texas Southwestern, Dallas TX 75390
| | - Joshua P. Aronson
- Dept. of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon NH 03756
| | - Barbara C. Jobst
- Dept. of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon NH 03756
| | - Robert E. Gross
- Dept. of Neurosurgery, Emory University Hospital, Atlanta GA 30322
| | - Michael R. Sperling
- Dept. of Neurology, Thomas Jefferson University Hospital, Philadelphia PA 19107
| | | | - Sameer A. Sheth
- Dept. of Neurosurgery, Columbia University Medical Center, New York, NY 10032
| | - Paul A. Wanda
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
| | - Daniel S. Rizzuto
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
| | - Michael J. Kahana
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
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7
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Hwang S, Hwang Y, Kim D, Lee J, Choe HK, Lee J, Kang H, Kung J. ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network. Front Neurosci 2023; 17:1161592. [PMID: 37638314 PMCID: PMC10448768 DOI: 10.3389/fnins.2023.1161592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/28/2023] [Indexed: 08/29/2023] Open
Abstract
Recent developments in artificial neural networks and their learning algorithms have enabled new research directions in computer vision, language modeling, and neuroscience. Among various neural network algorithms, spiking neural networks (SNNs) are well-suited for understanding the behavior of biological neural circuits. In this work, we propose to guide the training of a sparse SNN in order to replace a sub-region of a cultured hippocampal network with limited hardware resources. To verify our approach with a realistic experimental setup, we record spikes of cultured hippocampal neurons with a microelectrode array (in vitro). The main focus of this work is to dynamically cut unimportant synapses during SNN training on the fly so that the model can be realized on resource-constrained hardware, e.g., implantable devices. To do so, we adopt a simple STDP learning rule to easily select important synapses that impact the quality of spike timing learning. By combining the STDP rule with online supervised learning, we can precisely predict the spike pattern of the cultured network in real-time. The reduction in the model complexity, i.e., the reduced number of connections, significantly reduces the required hardware resources, which is crucial in developing an implantable chip for the treatment of neurological disorders. In addition to the new learning algorithm, we prototype a sparse SNN hardware on a small FPGA with pipelined execution and parallel computing to verify the possibility of real-time replacement. As a result, we can replace a sub-region of the biological neural circuit within 22 μs using 2.5 × fewer hardware resources, i.e., by allowing 80% sparsity in the SNN model, compared to the fully-connected SNN model. With energy-efficient algorithms and hardware, this work presents an essential step toward real-time neuroprosthetic computation.
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Affiliation(s)
- Sangwoo Hwang
- Department of Electrical Engineering and Computer Science, DGIST, Daegu, Republic of Korea
| | - Yujin Hwang
- Department of Electrical Engineering and Computer Science, DGIST, Daegu, Republic of Korea
| | - Duhee Kim
- Department of Electrical Engineering and Computer Science, DGIST, Daegu, Republic of Korea
| | - Junhee Lee
- Department of Electrical Engineering and Computer Science, DGIST, Daegu, Republic of Korea
| | - Han Kyoung Choe
- Department of Brain Sciences, DGIST, Daegu, Republic of Korea
| | - Junghyup Lee
- Department of Electrical Engineering and Computer Science, DGIST, Daegu, Republic of Korea
| | - Hongki Kang
- Department of Electrical Engineering and Computer Science, DGIST, Daegu, Republic of Korea
| | - Jaeha Kung
- School of Electrical Engineering, Korea University, Seoul, Republic of Korea
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8
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Wu S, Wang Y. Applying Neural Manifold Constraint on Point Process Model for Neural Spike Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083695 DOI: 10.1109/embc40787.2023.10340489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Neural prostheses can compensate for functional losses caused by blocked neural pathways by modeling neural activities among cortical areas. Existing methods generally utilize point process models to predict neural spikes from one area to another, and optimize the model by maximizing the log-likelihood between model predictions and recorded activities of individual neurons. However, single-neuron recordings can be distorted, while neuron population activity tends to reside within a stable subspace called the neural manifold, which reflects the connectivity and correlation among output neurons. This paper proposes a neural manifold constraint to modify the loss function for model training. The constraint term minimizes the distance from model predictions to the empirical manifold to amend the model predictions from distorted recordings. We test our methods on synthetic data with distortion on output spike trains and evaluate the similarity between model predictions and original output spike trains by the Kolmogorov-Smirnov test. The results show that the models trained with constraint have higher goodness-of-fit than those trained without constraint, which indicates the potential better approach for neural prostheses in noisy environments.
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9
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Gupta A, Vardalakis N, Wagner FB. Neuroprosthetics: from sensorimotor to cognitive disorders. Commun Biol 2023; 6:14. [PMID: 36609559 PMCID: PMC9823108 DOI: 10.1038/s42003-022-04390-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/19/2022] [Indexed: 01/07/2023] Open
Abstract
Neuroprosthetics is a multidisciplinary field at the interface between neurosciences and biomedical engineering, which aims at replacing or modulating parts of the nervous system that get disrupted in neurological disorders or after injury. Although neuroprostheses have steadily evolved over the past 60 years in the field of sensory and motor disorders, their application to higher-order cognitive functions is still at a relatively preliminary stage. Nevertheless, a recent series of proof-of-concept studies suggest that electrical neuromodulation strategies might also be useful in alleviating some cognitive and memory deficits, in particular in the context of dementia. Here, we review the evolution of neuroprosthetics from sensorimotor to cognitive disorders, highlighting important common principles such as the need for neuroprosthetic systems that enable multisite bidirectional interactions with the nervous system.
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Affiliation(s)
- Ankur Gupta
- grid.462010.1Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France
| | | | - Fabien B. Wagner
- grid.462010.1Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France
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10
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Webler RD, Oathes DJ, van Rooij SJH, Gewirtz JC, Nahas Z, Lissek SM, Widge AS. Causally mapping human threat extinction relevant circuits with depolarizing brain stimulation methods. Neurosci Biobehav Rev 2023; 144:105005. [PMID: 36549377 DOI: 10.1016/j.neubiorev.2022.105005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/17/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
Laboratory threat extinction paradigms and exposure-based therapy both involve repeated, safe confrontation with stimuli previously experienced as threatening. This fundamental procedural overlap supports laboratory threat extinction as a compelling analogue of exposure-based therapy. Threat extinction impairments have been detected in clinical anxiety and may contribute to exposure-based therapy non-response and relapse. However, efforts to improve exposure outcomes using techniques that boost extinction - primarily rodent extinction - have largely failed to date, potentially due to fundamental differences between rodent and human neurobiology. In this review, we articulate a comprehensive pre-clinical human research agenda designed to overcome these failures. We describe how connectivity guided depolarizing brain stimulation methods (i.e., TMS and DBS) can be applied concurrently with threat extinction and dual threat reconsolidation-extinction paradigms to causally map human extinction relevant circuits and inform the optimal integration of these methods with exposure-based therapy. We highlight candidate targets including the amygdala, hippocampus, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, and mesolimbic structures, and propose hypotheses about how stimulation delivered at specific learning phases could strengthen threat extinction.
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Affiliation(s)
- Ryan D Webler
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
| | - Desmond J Oathes
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Jonathan C Gewirtz
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA; Department of Psychology, Arizona State University, AZ, USA
| | - Ziad Nahas
- Department of Psychology, Arizona State University, AZ, USA
| | - Shmuel M Lissek
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Alik S Widge
- Department of Psychiatry and Medical Discovery Team on Addictions, University of Minnesota Medical School, MN, USA
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11
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Liu X, Wang F, Ramakrishna S. Hippocampus-guided engineering of memory prosthesis. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2022. [DOI: 10.1016/j.cobme.2022.100415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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12
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Chiappalone M, Cota VR, Carè M, Di Florio M, Beaubois R, Buccelli S, Barban F, Brofiga M, Averna A, Bonacini F, Guggenmos DJ, Bornat Y, Massobrio P, Bonifazi P, Levi T. Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering. Brain Sci 2022; 12:1578. [PMID: 36421904 PMCID: PMC9688667 DOI: 10.3390/brainsci12111578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/09/2022] [Accepted: 11/17/2022] [Indexed: 08/27/2023] Open
Abstract
Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.
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Affiliation(s)
- Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Vinicius R. Cota
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marta Carè
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Mattia Di Florio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Romain Beaubois
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Federico Barban
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Francesco Bonacini
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - David J. Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City, KS 66103, USA
- Landon Center on Aging, University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Yannick Bornat
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- National Institute for Nuclear Physics (INFN), 16146 Genova, Italy
| | - Paolo Bonifazi
- IKERBASQUE, The Basque Fundation, 48009 Bilbao, Spain
- Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Timothée Levi
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
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13
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Umbach G, Tan R, Jacobs J, Pfeiffer BE, Lega B. Flexibility of functional neuronal assemblies supports human memory. Nat Commun 2022; 13:6162. [PMID: 36257934 PMCID: PMC9579146 DOI: 10.1038/s41467-022-33587-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/22/2022] [Indexed: 12/24/2022] Open
Abstract
Episodic memories, or consciously accessible memories of unique events, represent a key aspect of human cognition. Evidence from rodent models suggests that the neural representation of these complex memories requires cooperative firing of groups of neurons on short time scales, organized by gamma oscillations. These co-firing groups, termed "neuronal assemblies," represent a fundamental neurophysiological unit supporting memory. Using microelectrode data from neurosurgical patients, we identify neuronal assemblies in the human MTL and show that they exhibit consistent organization in their firing pattern based on gamma phase information. We connect these properties to memory performance across recording sessions. Finally, we describe how human neuronal assemblies flexibly adjust over longer time scales. Our findings provide key evidence linking assemblies to human episodic memory for the first time.
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Affiliation(s)
- Gray Umbach
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Ryan Tan
- Department of Neurological Surgery, University of Texas Southwestern, Dallas, TX, 75235, USA
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Brad E Pfeiffer
- Department of Neuroscience, University of Texas Southwestern, Dallas, TX, 75235, USA
| | - Bradley Lega
- Department of Neurological Surgery, University of Texas Southwestern, Dallas, TX, 75235, USA.
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14
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Koverola M, Kunnari A, Drosinou M, Palomäki J, Hannikainen IR, Jirout Košová M, Kopecký R, Sundvall J, Laakasuo M. Treatments approved, boosts eschewed: Moral limits of neurotechnological enhancement. JOURNAL OF EXPERIMENTAL SOCIAL PSYCHOLOGY 2022. [DOI: 10.1016/j.jesp.2022.104351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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15
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Chen BW, Yang SH, Kuo CH, Chen JW, Lo YC, Kuo YT, Lin YC, Chang HC, Lin SH, Yu X, Qu B, Ro SCV, Lai HY, Chen YY. Neuro-Inspired Reinforcement Learning To Improve Trajectory Prediction In Reward-Guided Behavior. Int J Neural Syst 2022; 32:2250038. [DOI: 10.1142/s0129065722500381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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16
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Fang H, Yang Y. Designing and Validating a Robust Adaptive Neuromodulation Algorithm for Closed-Loop Control of Brain States. J Neural Eng 2022; 19. [PMID: 35576912 DOI: 10.1088/1741-2552/ac7005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/16/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications. APPROACH We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity. MAIN RESULTS The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms. SIGNIFICANCE Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.
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Affiliation(s)
- Hao Fang
- University of Central Florida, Research 1 Room 334, 313/316, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
| | - Yuxiao Yang
- Department of Electrical and Computer Engineering, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
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17
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Rahiminejad E, Azad F, Parvizi-Fard A, Amiri M, Linares-Barranco B. A Neuromorphic CMOS Circuit With Self-Repairing Capability. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:2246-2258. [PMID: 33417568 DOI: 10.1109/tnnls.2020.3045019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Neurophysiological observations confirm that the brain not only is able to detect the impaired synapses (in brain damage) but also it is relatively capable of repairing faulty synapses. It has been shown that retrograde signaling by astrocytes leads to the modulation of synaptic transmission and thus bidirectional collaboration of astrocyte with nearby neurons is an important aspect of self-repairing mechanism. Specifically, the retrograde signaling via astrocyte can increase the transmission probability of the healthy synapses linked to the neuron. Motivated by these findings, in the present research, a CMOS neuromorphic circuit with self-repairing capabilities is proposed based on astrocyte signaling. In this way, the computational model of self-repairing process is hired as a basis for designing a novel analog integrated circuit in the 180-nm CMOS technology. It is illustrated that the proposed analog circuit is able to successfully recompense the damaged synapses by appropriately modifying the voltage signals of the remaining healthy synapses in the wide range of frequency. The proposed circuit occupies 7500- [Formula: see text] silicon area and its power consumption is about [Formula: see text]. This neuromorphic fault-tolerant circuit can be considered as a key candidate for future silicon neuronal systems and implementation of neurorobotic and neuro-inspired circuits.
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18
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Accelerating Input-Output Model Estimation with Parallel Computing for Testing Hippocampal Memory Prostheses in Human. J Neurosci Methods 2022; 370:109492. [DOI: 10.1016/j.jneumeth.2022.109492] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/21/2022] [Accepted: 01/27/2022] [Indexed: 11/16/2022]
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19
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She X, Berger TW, Song D. A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size. Neural Comput 2021; 34:219-254. [PMID: 34758485 DOI: 10.1162/neco_a_01459] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 08/19/2021] [Indexed: 11/04/2022]
Abstract
We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.
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Affiliation(s)
- Xiwei She
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
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20
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Gaudry KS, Ayaz H, Bedows A, Celnik P, Eagleman D, Grover P, Illes J, Rao RPN, Robinson JT, Thyagarajan K. Projections and the Potential Societal Impact of the Future of Neurotechnologies. Front Neurosci 2021; 15:658930. [PMID: 34867139 PMCID: PMC8634831 DOI: 10.3389/fnins.2021.658930] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 10/04/2021] [Indexed: 12/17/2022] Open
Abstract
Traditionally, recording from and stimulating the brain with high spatial and temporal resolution required invasive means. However, recently, the technical capabilities of less invasive and non-invasive neuro-interfacing technology have been dramatically improving, and laboratories and funders aim to further improve these capabilities. These technologies can facilitate functions such as multi-person communication, mood regulation and memory recall. We consider a potential future where the less invasive technology is in high demand. Will this demand match that the current-day demand for a smartphone? Here, we draw upon existing research to project which particular neuroethics issues may arise in this potential future and what preparatory steps may be taken to address these issues.
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Affiliation(s)
- Kate S. Gaudry
- Kilpatrick Townsend & Stockton LLP, Washington, DC, United States
| | - Hasan Ayaz
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
- Department of Psychology, College of Arts and Sciences, Drexel University, Philadelphia, PA, United States
- Drexel Solutions Institute, Drexel University, Philadelphia, PA, United States
- Department of Family and Community Health, University of Pennsylvania, Philadelphia, PA, United States
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | | | - Pablo Celnik
- Department of Physical Medicine and Rehabilitation, Johns Hopkins, School of Medicine, Baltimore, MD, United States
| | - David Eagleman
- Department of Psychiatry, Stanford University School of Medicine, Stanford, CA, United States
| | - Pulkit Grover
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, United States
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Judy Illes
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
- Neuroethics Canada, University of British Columbia, Vancouver, BC, Canada
| | - Rajesh P. N. Rao
- Center for Neurotechnology, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, DC, United States
| | - Jacob T. Robinson
- Department of Bioengineering, Rice University, Houston, TX, United States
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
- Applied Physics Program, Rice University, Houston, TX, United States
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States
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21
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Joseph K, Kirsch M, Johnston M, Münkel C, Stieglitz T, Haas CA, Hofmann UG. Transcriptional characterization of the glial response due to chronic neural implantation of flexible microprobes. Biomaterials 2021; 279:121230. [PMID: 34736153 DOI: 10.1016/j.biomaterials.2021.121230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/20/2021] [Accepted: 10/24/2021] [Indexed: 01/13/2023]
Abstract
Long term implantation of (micro-)probes into neural tissue causes unique and disruptive responses. In this study, we investigate the transcriptional trajectory of glial cells responding to chronic implantation of 380 μm flexible micro-probes for up to 18 weeks. Transcriptomic analysis shows a rapid activation of microglial cells and a strong reactive astrocytic polarization, both of which are lost over the chronic of the implant duration. Animals that were implanted for 18 weeks show a transcriptional profile similar to non-implanted controls, with increased expression of genes associated with wound healing and angiogenesis, which raises hope of a normalization of the neuropil to the pre-injury state when using flexible probes. Nevertheless, our data shows that a subset of genes upregulated after 18 weeks belong to the family of immediate early genes, which indicates that structural and functional remodeling is not complete at this time point. Our results confirm and extend previous work on the molecular changes resulting from the presence of neural probes and provide a rational basis for developing interventional strategies to control them.
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Affiliation(s)
- Kevin Joseph
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Germany; Department of Neurosurgery, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; BrainLinks-BrainTools, University of Freiburg, Germany.
| | - Matthias Kirsch
- BrainLinks-BrainTools, University of Freiburg, Germany; Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Midori Johnston
- Faculty of Medicine, University of Freiburg, Germany; BrainLinks-BrainTools, University of Freiburg, Germany; Experimental Epilepsy Research, Dept. of Neurosurgery, Medical Center- University of Freiburg, Germany
| | - Christian Münkel
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Germany; Department of Neurosurgery, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany
| | - Thomas Stieglitz
- BrainLinks-BrainTools, University of Freiburg, Germany; Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Faculty of Engineering, University of Freiburg, Germany
| | - Carola A Haas
- Faculty of Medicine, University of Freiburg, Germany; Experimental Epilepsy Research, Dept. of Neurosurgery, Medical Center- University of Freiburg, Germany
| | - Ulrich G Hofmann
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Germany; Department of Neurosurgery, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; BrainLinks-BrainTools, University of Freiburg, Germany
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22
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Faillot M, Chaillet A, Palfi S, Senova S. Rodent models used in preclinical studies of deep brain stimulation to rescue memory deficits. Neurosci Biobehav Rev 2021; 130:410-432. [PMID: 34437937 DOI: 10.1016/j.neubiorev.2021.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 08/10/2021] [Accepted: 08/13/2021] [Indexed: 11/28/2022]
Abstract
Deep brain stimulation paradigms might be used to treat memory disorders in patients with stroke or traumatic brain injury. However, proof of concept studies in animal models are needed before clinical translation. We propose here a comprehensive review of rodent models for Traumatic Brain Injury and Stroke. We systematically review the histological, behavioral and electrophysiological features of each model and identify those that are the most relevant for translational research.
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Affiliation(s)
- Matthieu Faillot
- Neurosurgery department, Henri Mondor University Hospital, APHP, DMU CARE, Université Paris Est Créteil, Mondor Institute for Biomedical Research, INSERM U955, Team 15, Translational Neuropsychiatry, France
| | - Antoine Chaillet
- Laboratoire des Signaux et Systèmes (L2S-UMR8506) - CentraleSupélec, Université Paris Saclay, Institut Universitaire de France, France
| | - Stéphane Palfi
- Neurosurgery department, Henri Mondor University Hospital, APHP, DMU CARE, Université Paris Est Créteil, Mondor Institute for Biomedical Research, INSERM U955, Team 15, Translational Neuropsychiatry, France
| | - Suhan Senova
- Neurosurgery department, Henri Mondor University Hospital, APHP, DMU CARE, Université Paris Est Créteil, Mondor Institute for Biomedical Research, INSERM U955, Team 15, Translational Neuropsychiatry, France.
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23
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Schmied A, Varma S, Dubinsky JM. Acceptability of Neuroscientific Interventions in Education. SCIENCE AND ENGINEERING ETHICS 2021; 27:52. [PMID: 34351520 DOI: 10.1007/s11948-021-00328-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
Researchers are increasingly applying neuroscience technologies that probe or manipulate the brain to improve educational outcomes. However, their use remains fraught with ethical controversies. Here, we investigate the acceptability of neuroscience applications to educational practice in two groups of young adults: those studying bioscience who will be driving future basic neuroscience research and technology transfer, and those studying education who will be choosing among neuroscience-derived applications for their students. Respondents rated the acceptability of six scenarios describing neuroscience applications to education spanning multiple methodologies, from neuroimaging to neuroactive drugs to brain stimulation. They did so from two perspectives (student, teacher) and for three recipient populations (low-achieving, high-achieving students, students with learning disabilities). Overall, the biosciences students were more favorable to all neuroscience applications than the education students. Scenarios that measured brain activity (i.e., EEG or fMRI) to assess or predict intellectual abilities were deemed more acceptable than manipulations of mental activity by drug use or stimulation techniques, which may violate body integrity. Enhancement up to the norm for low-achieving students and especially students with learning disabilities was more favorably viewed than enhancement beyond the norm for high-achieving students. Finally, respondents rated neuroscientific applications to be less acceptable when adopting the perspective of a teacher than that of a student. Future studies should go beyond the acceptability ratings collected here to delineate the role that concepts of access, equity, authenticity, agency and personal choice play in guiding respondents' reasoning.
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Affiliation(s)
- A Schmied
- Department of Educational Psychology, University of Minnesota, Minneapolis, MN, USA
| | - S Varma
- School of Interactive Computing, College of Computing & School of Psychology, College of Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - J M Dubinsky
- Department of Neuroscience, University of Minnesota, 6-145 Jackson Hall, 321 Church St SE, Minneapolis, MN, 55455, USA.
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24
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Sangodoyin S, Ugurlu E, Dey M, Prvulovic M, Zajic A. Leveraging On-Chip Transistor Switching for Communication and Sensing in Neural Implants and Gastrointestinal Devices. IEEE Trans Biomed Eng 2021; 69:377-389. [PMID: 34236960 DOI: 10.1109/tbme.2021.3094543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This paper presents a Proof-of-Concept (POC) design and implementation of a biosensing and communication system that can be used for biotelemetry in neural and Gastrointestinal (GI) applications. METHODS Our proposed system is based on backscattering from a semi-passive Radio-Frequency-Identification-Device (RFID) implemented using an Application Specific Integrated Circuit (ASIC) in which electronic switching between transistor gates in high and low states create an impedance difference, thereby effectively changing the ASIC's Radar Cross Section (RCS) and thus modulating its backscat-tered field. The ASIC is used in conjunction with a biosensor to measure and transmit vital signs from within the body. With this system, we conducted backscatter propagation experiments through different biological and phantom tissues (in ex-vivo and in-vitro) in the GI and neural environments. RESULTS Our results show that the backscattered waveforms can penetrate tissues of various compositions and thicknesses with power received at distances of up to 55 cm away from the RFID ASIC. Furthermore, results from single-and multi-bit biotelemetry measurements showed a high signal fidelity with low Bit-Error-Rate (BER) while being able to resolve varying tissue temperatures measured by the biosensor in our system. CONCLUSION We realized a POC system in which on-chip transistor switching in an ASIC can be used to achieve backscatter communication and biosensing. This system is deployable in neural and GI applications. SIGNIFICANCE Our findings in this work will provide an important practical basis for the design and development of RFID ASIC for biosensing and biotelemetry in medical applications.
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25
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Samiei A, Hashemi H. A Bidirectional Neural Interface SoC With Adaptive IIR Stimulation Artifact Cancelers. IEEE JOURNAL OF SOLID-STATE CIRCUITS 2021; 56:2142-2157. [PMID: 34483356 PMCID: PMC8409175 DOI: 10.1109/jssc.2021.3056040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We present a 180-nm CMOS bidirectional neural interface system-on-chip that enables simultaneous recording and stimulation with on-chip stimulus artifact cancelers. The front-end cancellation scheme incorporates a least-mean-square engine that adapts the coefficients of a 2-tap infinite-impulse-response filter to replicate the stimulation artifact waveform and subtract it at the front-end. Measurements demonstrate the efficacy of the canceler in mitigating artifacts up to 700 mVpp and reducing the front-end amplifier saturation recovery time in response to a 2.5 Vpp artifact. Each recording channel houses a pair of adaptive infinite-impulse-response filters, which enable cancellation of the artifacts generated by the simultaneous operation of the 2 on-chip stimulators. The analog front-end consumes 2.5 μW of power per channel, has a maximum gain of 50 dB and a bandwidth of 9.0 kHz with 6.2 μVrms integrated input-referred noise.
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Affiliation(s)
- Aria Samiei
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Hossein Hashemi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089 USA
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26
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Germani F, Kellmeyer P, Wäscher S, Biller-Andorno N. Engineering Minds? Ethical Considerations on Biotechnological Approaches to Mental Health, Well-Being, and Human Flourishing. Trends Biotechnol 2021; 39:1111-1113. [PMID: 33958228 DOI: 10.1016/j.tibtech.2021.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/09/2021] [Accepted: 04/09/2021] [Indexed: 11/15/2022]
Abstract
Our bodies can be designed and modified in accordance with our ideals of health and well-being. These increasingly targeted and personalized interventions will be more effective than current therapies. Here we review technologies to alter mood, and explore the ethics of bioengineering approaches to mental health.
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Affiliation(s)
- Federico Germani
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | - Philipp Kellmeyer
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland; Neuroethics and AI Ethics Lab, Department of Neurosurgery, University Medical Center Freiburg, Freiburg, Germany; Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Freiburg, Germany
| | - Sebastian Wäscher
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | - Nikola Biller-Andorno
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland.
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27
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Elyahoodayan S, Jiang W, Lee CD, Shao X, Weiland G, Whalen JJ, Petrossians A, Song D. Stimulation and Recording of the Hippocampus Using the Same Pt-Ir Coated Microelectrodes. Front Neurosci 2021; 15:616063. [PMID: 33716647 PMCID: PMC7943859 DOI: 10.3389/fnins.2021.616063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 01/28/2021] [Indexed: 01/11/2023] Open
Abstract
Same-electrode stimulation and recording with high spatial resolution, signal quality, and power efficiency is highly desirable in neuroscience and neural engineering. High spatial resolution and signal-to-noise ratio is necessary for obtaining unitary activities and delivering focal stimulations. Power efficiency is critical for battery-operated implantable neural interfaces. This study demonstrates the capability of recording single units as well as evoked potentials in response to a wide range of electrochemically safe stimulation pulses through high-resolution microelectrodes coated with co-deposition of Pt-Ir. It also compares signal-to-noise ratio, single unit activity, and power efficiencies between Pt-Ir coated and uncoated microelectrodes. To enable stimulation and recording with the same microelectrodes, microelectrode arrays were treated with electrodeposited platinum-iridium coating (EPIC) and tested in the CA1 cell body layer of rat hippocampi. The electrodes' ability to (1) inject a large range of electrochemically reversable stimulation pulses to the tissue, and (2) record evoked potentials and single unit activities were quantitively assessed over an acute time period. Compared to uncoated electrodes, EPIC electrodes recorded signals with higher signal-to-noise ratios (coated: 9.77 ± 1.95 dB; uncoated: 1.95 ± 0.40 dB) and generated lower voltages (coated: 100 mV; uncoated: 650 mV) for a given stimulus (5 μA). The improved performance corresponded to lower energy consumptions and electrochemically safe stimulation above 5 μA (>0.38 mC/cm2), which enabled elicitation of field excitatory post synaptic potentials and population spikes. Spontaneous single unit activities were also modulated by varying stimulation intensities and monitored through the same electrodes. This work represents an example of stimulation and recording single unit activities from the same microelectrode, which provides a powerful tool for monitoring and manipulating neural circuits at the single neuron level.
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Affiliation(s)
- Sahar Elyahoodayan
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Wenxuan Jiang
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | | | - Xiecheng Shao
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | | | | | | | - Dong Song
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
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28
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Sullivan CRP, Olsen S, Widge AS. Deep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks. Neuroimage 2021; 225:117515. [PMID: 33137473 PMCID: PMC7802517 DOI: 10.1016/j.neuroimage.2020.117515] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/19/2020] [Accepted: 10/24/2020] [Indexed: 01/16/2023] Open
Abstract
Deep brain stimulation (DBS) is a promising intervention for treatment-resistant psychiatric disorders, particularly major depressive disorder (MDD) and obsessive-compulsive disorder (OCD). Up to 90% of patients who have not recovered with therapy or medication have reported benefit from DBS in open-label studies. Response rates in randomized controlled trials (RCTs), however, have been much lower. This has been argued to arise from surgical variability between sites, and recent psychiatric DBS research has focused on refining targeting through personalized imaging. Much less attention has been given to the fact that psychiatric disorders arise from dysfunction in distributed brain networks, and that DBS likely acts by altering communication within those networks. This is in part because psychiatric DBS research relies on subjective rating scales that make it difficult to identify network biomarkers. Here, we overview recent DBS RCT results in OCD and MDD, as well as the follow-on imaging studies. We present evidence for a new approach to studying DBS' mechanisms of action, focused on measuring objective cognitive/emotional deficits that underpin these and many other mental disorders. Further, we suggest that a focus on cognition could lead to reliable network biomarkers at an electrophysiologic level, especially those related to inter-regional synchrony of the local field potential (LFP). Developing the network neuroscience of DBS has the potential to finally unlock the potential of this highly specific therapy.
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Affiliation(s)
- Christi R P Sullivan
- University of Minnesota Medical School Department of Psychiatry and Behavioral Sciences, 2001 6th Street SE, Minneapolis, MN 55454, USA.
| | - Sarah Olsen
- University of Minnesota Medical School Department of Psychiatry and Behavioral Sciences, 2001 6th Street SE, Minneapolis, MN 55454, USA.
| | - Alik S Widge
- University of Minnesota Medical School Department of Psychiatry and Behavioral Sciences, 2001 6th Street SE, Minneapolis, MN 55454, USA.
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Mankin EA, Aghajan ZM, Schuette P, Tran ME, Tchemodanov N, Titiz A, Kalender G, Eliashiv D, Stern J, Weiss SA, Kirsch D, Knowlton B, Fried I, Suthana N. Stimulation of the right entorhinal white matter enhances visual memory encoding in humans. Brain Stimul 2021; 14:131-140. [PMID: 33279717 PMCID: PMC7855810 DOI: 10.1016/j.brs.2020.11.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/15/2020] [Accepted: 11/16/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND While deep brain stimulation has been successful in treating movement disorders, such as in Parkinson's disease, its potential application in alleviating memory disorders is inconclusive. OBJECTIVE/HYPOTHESIS We investigated the role of the location of the stimulating electrode on memory improvement and hypothesized that entorhinal white versus gray matter stimulation would have differential effects on memory. METHODS Intracranial electrical stimulation was applied to the entorhinal area of twenty-two participants with already implanted electrodes as they completed visual memory tasks. RESULTS We found that stimulation of right entorhinal white matter during learning had a beneficial effect on subsequent memory, while stimulation of adjacent gray matter or left-sided stimulation was ineffective. This finding was consistent across three different visually guided memory tasks. CONCLUSIONS Our results highlight the importance of precise stimulation site on modulation of human hippocampal-dependent memory and suggest that stimulation of afferent input into the right hippocampus may be an especially promising target for enhancement of visual memory.
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Affiliation(s)
- Emily A Mankin
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Los Angeles, CA, 90095, USA
| | - Zahra M Aghajan
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior at UCLA, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Peter Schuette
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior at UCLA, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA, 90095, USA; Department of Bioengineering, University of California, Los Angeles, 410 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Michelle E Tran
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Los Angeles, CA, 90095, USA
| | - Natalia Tchemodanov
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Los Angeles, CA, 90095, USA
| | - Ali Titiz
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Los Angeles, CA, 90095, USA
| | - Güldamla Kalender
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Los Angeles, CA, 90095, USA
| | - Dawn Eliashiv
- Department of Neurology, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - John Stern
- Department of Neurology, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Shennan A Weiss
- Department of Neurology, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Dylan Kirsch
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior at UCLA, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Barbara Knowlton
- Department of Psychology, University of California, Los Angeles, 502 Portola Plaza, Los Angeles, CA, 90095, USA
| | - Itzhak Fried
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Los Angeles, CA, 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior at UCLA, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA, 90095, USA; Functional Neurosurgery Unit, Tel-Aviv Medical Center and Sackler School of Medicine, Tel-Aviv University, P.O.B 39040 Ramat Aviv, Tel-Aviv, 69978, Israel
| | - Nanthia Suthana
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Los Angeles, CA, 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior at UCLA, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA, 90095, USA; Department of Psychology, University of California, Los Angeles, 502 Portola Plaza, Los Angeles, CA, 90095, USA; Department of Bioengineering, University of California, Los Angeles, 410 Westwood Plaza, Los Angeles, CA, 90095, USA.
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30
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Cui X, Zhang F, Zhang H, Huang X, Wang K, Huang T, Yang X, Zou L. Neuroprotective Effect of Optogenetics Varies With Distance From Channelrhodopsin-2 Expression in an Amyloid-β-Injected Mouse Model of Alzheimer's Disease. Front Neurosci 2020; 14:583628. [PMID: 33162881 PMCID: PMC7584457 DOI: 10.3389/fnins.2020.583628] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/14/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is the most common cause of dementia. Optogenetics uses a combination of genetic engineering and light to activate or inhibit specific neurons in the brain. Objective: The objective of the study was to examine the effect of activation of glutamatergic neurons in the hippocampus of mice injected with Aβ1-42 on memory function and biomarkers of neuroinflammation and neuroprotection in the brain to elucidate the clinical utility of optogenetic neuromodulation in AD. Methods: AAV5–CaMKII–channelrhodopsin-2 (CHR2)–mCherry (Aβ-CHR2 mice) or AAV5—CaMKII–mCherry (Aβ-non-CHR2 mice) was injected into the dentate gyrus (DG) of the bilateral hippocampus of an Aβ1-42-injected mouse model of AD. The novel object recognition test was used to investigate working memory (M1), short-term memory (M2), and long-term memory (M3) after Aβ1-42 injection. Hippocampus tissues were collected for immunohistochemical analysis. Results: Compared to controls, M1 and M2 were significantly higher in Aβ-CHR2 mice, but there was no significant difference in M3; NeuN and synapsin expression were significantly increased in the DG of Aβ-CHR2 mice, but not in CA1, CA3, the subventricular zone (SVZ), or the entorhinal cortex (ENT); GluR2 and IL-10 expressions were significantly increased, and GFAP expression was significantly decreased, in CA1, CA3, the DG, and the SVZ of Aβ-CHR2 mice, but not in the ENT. Conclusion: Activation of glutamatergic neurons by optogenetics in the bilateral DG of an Aβ-injected mouse model of AD improved M1 and M2, but not M3. A single-target optogenetics strategy has spatial limitations; therefore, a multiple targeted optogenetics approach to AD therapy should be explored.
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Affiliation(s)
- Xiaorui Cui
- Department of Neurology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China.,Department of Neurology, Affiliated Hospital of Xiangnan University, Chenzhou, China
| | - Feng Zhang
- Intensive Care Unit, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Hui Zhang
- Department of Neurology, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Second Clinical College, Jinan University, Shenzhen, China
| | - Xi Huang
- Department of Neurology, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Second Clinical College, Jinan University, Shenzhen, China
| | - Kewei Wang
- Department of Neurology, Longgang District People's Hospital of Shenzhen, Shenzhen, China
| | - Ting Huang
- Department of Cerebrovascular Disease, People's Hospital of Yuxi, The Sixth Affiliated Hospital of Kunming Medical University, Yuxi, China
| | - Xifei Yang
- Key Laboratory of Modern Toxicology of Shenzhen, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Liangyu Zou
- Department of Neurology, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Second Clinical College, Jinan University, Shenzhen, China
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George R, Chiappalone M, Giugliano M, Levi T, Vassanelli S, Partzsch J, Mayr C. Plasticity and Adaptation in Neuromorphic Biohybrid Systems. iScience 2020; 23:101589. [PMID: 33083749 PMCID: PMC7554028 DOI: 10.1016/j.isci.2020.101589] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.
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Affiliation(s)
- Richard George
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | | | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies, Trieste, Italy
| | - Timothée Levi
- Laboratoire de l’Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Stefano Vassanelli
- Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | - Christian Mayr
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
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32
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Scholten K, Larson CE, Xu H, Song D, Meng E. A 512-Channel Multi-Layer Polymer-Based Neural Probe Array. JOURNAL OF MICROELECTROMECHANICAL SYSTEMS : A JOINT IEEE AND ASME PUBLICATION ON MICROSTRUCTURES, MICROACTUATORS, MICROSENSORS, AND MICROSYSTEMS 2020; 29:1054-1058. [PMID: 33746477 PMCID: PMC7978043 DOI: 10.1109/jmems.2020.2999550] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
We present for the first time the design, fabrication, and preliminary bench-top characterization of a high-density, polymer-based penetrating microelectrode array, developed for chronic, large-scale recording in the cortices and hippocampi of behaving rats. We present two architectures for these targeted brain regions, both featuring 512 Pt recording electrodes patterned front-and-back on micromachined eight-shank arrays of thin-film Parylene C. These devices represent an order of magnitude improvement in both number and density of recording electrodes compared with prior work on polymer-based microelectrode arrays. We present enabling advances in polymer micro-machining related to lithographic resolution and a new method for back-side patterning of electrodes. In vitro electrochemical data verifies suitable electrode function and surface properties. Finally, we describe next steps toward the implementation of these arrays in chronic, large-scale recording studies in free-moving animal models.
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Affiliation(s)
- Kee Scholten
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Christopher E Larson
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Huijing Xu
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Dong Song
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Ellis Meng
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089 USA
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33
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Mankin EA, Fried I. Modulation of Human Memory by Deep Brain Stimulation of the Entorhinal-Hippocampal Circuitry. Neuron 2020; 106:218-235. [PMID: 32325058 DOI: 10.1016/j.neuron.2020.02.024] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 01/13/2020] [Accepted: 01/27/2020] [Indexed: 01/02/2023]
Abstract
Neurological disorders affecting human memory present a major scientific, medical, and societal challenge. Direct or indirect deep brain stimulation (DBS) of the entorhinal-hippocampal system, the brain's major memory hub, has been studied in people with epilepsy or Alzheimer's disease, intending to enhance memory performance or slow memory decline. Variability in the spatiotemporal parameters of stimulation employed to date notwithstanding, it is likely that future DBS for memory will employ closed-loop, nuanced approaches that are synergistic with native physiological processes. The potential for editing human memory-decoding, enhancing, incepting, or deleting specific memories-suggests exciting therapeutic possibilities but also raises considerable ethical concerns.
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Affiliation(s)
- Emily A Mankin
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Itzhak Fried
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA; Tel Aviv Medical Center and Tel Aviv University, Tel Aviv, Israel.
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34
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35
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Mazurek KA, Schieber MH. Injecting Information into the Mammalian Cortex: Progress, Challenges, and Promise. Neuroscientist 2020; 27:129-142. [PMID: 32648527 DOI: 10.1177/1073858420936253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
For 150 years artificial stimulation has been used to study the function of the nervous system. Such stimulation-whether electrical or optogenetic-eventually may be used in neuroprosthetic devices to replace lost sensory inputs and to otherwise introduce information into the nervous system. Efforts toward this goal can be classified broadly as either biomimetic or arbitrary. Biomimetic stimulation aims to mimic patterns of natural neural activity, so that the subject immediately experiences the artificial stimulation as if it were natural sensation. Arbitrary stimulation, in contrast, makes no attempt to mimic natural patterns of neural activity. Instead, different stimuli-at different locations and/or in different patterns-are assigned different meanings randomly. The subject's time and effort then are required to learn to interpret different stimuli, a process that engages the brain's inherent plasticity. Here we will examine progress in using artificial stimulation to inject information into the cerebral cortex and discuss the challenges for and the promise of future development.
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Affiliation(s)
- Kevin A Mazurek
- Department of Neuroscience, University of Rochester, Rochester, NY, USA.,Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA
| | - Marc H Schieber
- Department of Neuroscience, University of Rochester, Rochester, NY, USA.,Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA.,Department of Neurology, University of Rochester, Rochester, NY, USA.,Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA
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36
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Wicks RT, Witcher MR, Couture DE, Laxton AW, Popli G, Whitlow CT, Fetterhoff D, Dakos AS, Roeder BM, Deadwyler SA, Hampson RE. Hippocampal CA1 and CA3 neural recording in the human brain: validation of depth electrode placement through high-resolution imaging and electrophysiology. Neurosurg Focus 2020; 49:E5. [PMID: 32610296 DOI: 10.3171/2020.4.focus20164] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 04/16/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Intracranial human brain recordings typically utilize recording systems that do not distinguish individual neuron action potentials. In such cases, individual neurons are not identified by location within functional circuits. In this paper, verified localization of singly recorded hippocampal neurons within the CA3 and CA1 cell fields is demonstrated. METHODS Macro-micro depth electrodes were implanted in 23 human patients undergoing invasive monitoring for identification of epileptic seizure foci. Individual neurons were isolated and identified via extracellular action potential waveforms recorded via macro-micro depth electrodes localized within the hippocampus. A morphometric survey was performed using 3T MRI scans of hippocampi from the 23 implanted patients, as well as 46 normal (i.e., nonepileptic) patients and 26 patients with a history of epilepsy but no history of depth electrode placement, which provided average dimensions of the hippocampus along typical implantation tracks. Localization within CA3 and CA1 cell fields was tentatively assigned on the basis of recording electrode site, stereotactic positioning of the depth electrode in comparison with the morphometric survey, and postsurgical MRI. Cells were selected as candidate CA3 and CA1 principal neurons on the basis of waveform and firing rate characteristics and confirmed within the CA3-to-CA1 neural projection pathways via measures of functional connectivity. RESULTS Cross-correlation analysis confirmed that nearly 80% of putative CA3-to-CA1 cell pairs exhibited positive correlations compatible with feed-forward connection between the cells, while only 2.6% exhibited feedback (inverse) connectivity. Even though synchronous and long-latency correlations were excluded, feed-forward correlation between CA3-CA1 pairs was identified in 1071 (26%) of 4070 total pairs, which favorably compares to reports of 20%-25% feed-forward CA3-CA1 correlation noted in published animal studies. CONCLUSIONS This study demonstrates the ability to record neurons in vivo from specified regions and subfields of the human brain. As brain-machine interface and neural prosthetic research continues to expand, it is necessary to be able to identify recording and stimulation sites within neural circuits of interest.
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Affiliation(s)
| | | | | | | | | | | | - Dustin Fetterhoff
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Alexander S Dakos
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Brent M Roeder
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Sam A Deadwyler
- 5Physiology and Pharmacology, and.,6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Robert E Hampson
- 2Neurology.,5Physiology and Pharmacology, and.,6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
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37
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She X, Robinson BS, Berger TW, Song D. Accelerating Estimation of a Multi-Input Multi-Output Model of the Hippocampus with a Parallel Computing Strategy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2479-2482. [PMID: 33018509 DOI: 10.1109/embc44109.2020.9175490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
To build hippocampal memory prosthesis for restoring memory functions, we previously developed and implemented a multi-input multi-output (MIMO) nonlinear dynamic model of the hippocampus. This model can successfully predict hippocampal output spike activities based on input spike activities, and thus be used to drive microstimulation to bypass the damaged hippocampal region. Building such a MIMO model involves estimations of a large number of model coefficients, which typically takes hundreds of hours using a single personal computer. In practice, however, due to the requirement of medical care and clinical trials, the modeling processes must be completed within 72 hours after the recording, so that models can be used to drive stimulations. To solve this problem, we utilized a parallelization strategy to divide the whole MIMO model computation involving iterative estimation and optimization into independent computing tasks that can be performed simultaneously in multiple computer nodes. Such a strategy was implemented on the high-performance computing cluster at the University of Southern California. It reduced the model estimation time to tens of hours and thus allowed us to complete the modeling process within the required time frame to further test model-driven electrical stimulation for the hippocampal memory prosthesis.
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38
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Welle EJ, Patel PR, Woods JE, Petrossians A, della Valle E, Vega-Medina A, Richie JM, Cai D, Weiland JD, Chestek CA. Ultra-small carbon fiber electrode recording site optimization and improved in vivo chronic recording yield. J Neural Eng 2020; 17:026037. [PMID: 32209743 PMCID: PMC10771280 DOI: 10.1088/1741-2552/ab8343] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE Carbon fiber electrodes may enable better long-term brain implants, minimizing the tissue response commonly seen with silicon-based electrodes. The small diameter fiber may enable high-channel count brain-machine interfaces capable of reproducing dexterous movements. Past carbon fiber electrodes exhibited both high fidelity single unit recordings and a healthy neuronal population immediately adjacent to the recording site. However, the recording yield of our carbon fiber arrays chronically implanted in the brain typically hovered around 30%, for previously unknown reasons. In this paper we investigated fabrication process modifications aimed at increasing recording yield and longevity. APPROACH We tested a new cutting method using a 532nm laser against traditional scissor methods for the creation of the electrode recording site. We verified the efficacy of improved recording sites with impedance measurements and in vivo array recording yield. Additionally, we tested potentially longer-lasting coating alternatives to PEDOT:pTS, including PtIr and oxygen plasma etching. New coatings were evaluated with accelerated soak testing and acute recording. MAIN RESULTS We found that the laser created a consistent, sustainable 257 ± 13.8 µm2 electrode with low 1 kHz impedance (19 ± 4 kΩ with PEDOT:pTS) and low fiber-to-fiber variability. The PEDOT:pTS coated laser cut fibers were found to have high recording yield in acute (97% > 100 µV pp , N = 34 fibers) and chronic (84% > 100 µV pp , day 7; 71% > 100 µV pp , day 63, N = 45 fibers) settings. The laser cut recording sites were good platforms for the PtIr coating and oxygen plasma etching, slowing the increase in 1 kHz impedance compared to PEDOT:pTS in an accelerated soak test. SIGNIFICANCE We have found that laser cut carbon fibers have a high recording yield that can be maintained for over two months in vivo and that alternative coatings perform better than PEDOT:pTS in accelerated aging tests. This work provides evidence to support carbon fiber arrays as a viable approach to high-density, clinically-feasible brain-machine interfaces.
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Affiliation(s)
- Elissa J Welle
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Paras R Patel
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Joshua E Woods
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America
| | | | - Elena della Valle
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Alexis Vega-Medina
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, United States of America
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States of America
| | - Julianna M Richie
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Dawen Cai
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, United States of America
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States of America
- Biophysics, University of Michigan, Ann Arbor, MI, United States of America
| | - James D Weiland
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- Platinum Group Coatings, Pasadena, CA, United States of America
- Robotics Graduate Program, University of Michigan, Ann Arbor, MI, United States of America
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States of America
- Robotics Graduate Program, University of Michigan, Ann Arbor, MI, United States of America
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Mikhaylov A, Pimashkin A, Pigareva Y, Gerasimova S, Gryaznov E, Shchanikov S, Zuev A, Talanov M, Lavrov I, Demin V, Erokhin V, Lobov S, Mukhina I, Kazantsev V, Wu H, Spagnolo B. Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics. Front Neurosci 2020; 14:358. [PMID: 32410943 PMCID: PMC7199501 DOI: 10.3389/fnins.2020.00358] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 03/24/2020] [Indexed: 11/18/2022] Open
Abstract
Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale cross-bar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.
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Affiliation(s)
- Alexey Mikhaylov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey Pimashkin
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Yana Pigareva
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | | | - Evgeny Gryaznov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Sergey Shchanikov
- Department of Information Technologies, Vladimir State University, Murom, Russia
| | - Anton Zuev
- Department of Information Technologies, Vladimir State University, Murom, Russia
| | - Max Talanov
- Neuroscience Laboratory, Kazan Federal University, Kazan, Russia
| | - Igor Lavrov
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
- Laboratory of Motor Neurorehabilitation, Kazan Federal University, Kazan, Russia
| | | | - Victor Erokhin
- Neuroscience Laboratory, Kazan Federal University, Kazan, Russia
- Kurchatov Institute, Moscow, Russia
- CNR-Institute of Materials for Electronics and Magnetism, Italian National Research Council, Parma, Italy
| | - Sergey Lobov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
| | - Irina Mukhina
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Cell Technology Group, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Victor Kazantsev
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
| | - Huaqiang Wu
- Institute of Microelectronics, Tsinghua University, Beijing, China
| | - Bernardo Spagnolo
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Dipartimento di Fisica e Chimica-Emilio Segrè, Group of Interdisciplinary Theoretical Physics, Università di Palermo and CNISM, Unità di Palermo, Palermo, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Catania, Italy
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40
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McAndrews MP, Cohn M, Gold DA. Infusing cognitive neuroscience into the clinical neuropsychology of memory. Curr Opin Behav Sci 2020. [DOI: 10.1016/j.cobeha.2020.01.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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41
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Herff C, Krusienski DJ, Kubben P. The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions. Front Neurosci 2020; 14:123. [PMID: 32174810 PMCID: PMC7056827 DOI: 10.3389/fnins.2020.00123] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 01/30/2020] [Indexed: 12/17/2022] Open
Abstract
Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.
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Affiliation(s)
- Christian Herff
- Department of Neurosurgery, School of Mental Health and Neurosciences, Maastricht University, Maastricht, Netherlands
| | - Dean J Krusienski
- ASPEN Lab, Biomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, United States
| | - Pieter Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands
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Serb A, Corna A, George R, Khiat A, Rocchi F, Reato M, Maschietto M, Mayr C, Indiveri G, Vassanelli S, Prodromakis T. Memristive synapses connect brain and silicon spiking neurons. Sci Rep 2020; 10:2590. [PMID: 32098971 PMCID: PMC7042282 DOI: 10.1038/s41598-020-58831-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/21/2020] [Indexed: 11/09/2022] Open
Abstract
Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics has made important advances to emulate neurons and synapses and brain-computer interfacing concepts that interlink brain and brain-inspired devices are beginning to materialise. We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus. Memristive plasticity accounts for modulation of connection strength, while transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.
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Affiliation(s)
- Alexantrou Serb
- Centre for Electronics Frontiers, University of Southampton, Southampton, SO17 1BJ, UK
| | - Andrea Corna
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Richard George
- Institute of Circuits and Systems, TU Dresden, Dresden, 01062, Germany
| | - Ali Khiat
- Centre for Electronics Frontiers, University of Southampton, Southampton, SO17 1BJ, UK
| | - Federico Rocchi
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Marco Reato
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Marta Maschietto
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Christian Mayr
- Institute of Circuits and Systems, TU Dresden, Dresden, 01062, Germany
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - Stefano Vassanelli
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy.
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43
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Bingham CS, Mergenthal A, Bouteiller JMC, Song D, Lazzi G, Berger TW. ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons and an Application to Electroceutical Modeling. Front Comput Neurosci 2020; 14:13. [PMID: 32153379 PMCID: PMC7047217 DOI: 10.3389/fncom.2020.00013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 01/31/2020] [Indexed: 11/13/2022] Open
Abstract
Advances in computation and neuronal modeling have enabled the study of entire neural tissue systems with an impressive degree of biological realism. These efforts have focused largely on modeling dendrites and somas while largely neglecting axons. The need for biologically realistic explicit axonal models is particularly clear for applications involving clinical and therapeutic electrical stimulation because axons are generally more excitable than other neuroanatomical subunits. While many modeling efforts can rely on existing repositories of reconstructed dendritic/somatic morphologies to study real cells or to estimate parameters for a generative model, such datasets for axons are scarce and incomplete. Those that do exist may still be insufficient to build accurate models because the increased geometric variability of axons demands a proportional increase in data. To address this need, a Ruled-Optimum Ordered Tree System (ROOTS) was developed that extends the capability of neuronal morphology generative methods to include highly branched cortical axon terminal arbors. Further, this study presents and explores a clear use-case for such models in the prediction of cortical tissue response to externally applied electric fields. The results presented herein comprise (i) a quantitative and qualitative analysis of the generative algorithm proposed, (ii) a comparison of generated fibers with those observed in histological studies, (iii) a study of the requisite spatial and morphological complexity of axonal arbors for accurate prediction of neuronal response to extracellular electrical stimulation, and (iv) an extracellular electrical stimulation strength-duration analysis to explore probable thresholds of excitation of the dentate perforant path under controlled conditions. ROOTS demonstrates a superior ability to capture biological realism in model fibers, allowing improved accuracy in predicting the impact that microscale structures and branching patterns have on spatiotemporal patterns of activity in the presence of extracellular electric fields.
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Affiliation(s)
- Clayton S. Bingham
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Adam Mergenthal
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Jean-Marie C. Bouteiller
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Gianluca Lazzi
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
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Schneider F, Horowitz A, Lesch KP, Dandekar T. Delaying memory decline: different options and emerging solutions. Transl Psychiatry 2020; 10:13. [PMID: 32066684 PMCID: PMC7026464 DOI: 10.1038/s41398-020-0697-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 11/28/2019] [Accepted: 12/08/2019] [Indexed: 12/13/2022] Open
Abstract
Memory decline can be a devastating disease and increases in aging Western populations. Memory enhancement technologies hold promise for this and other conditions. Approaches include stem cell transplantation, which improved memory in several animal studies as well as vaccination against Alzheimer´s disease (AD) by β-amyloid antibodies. For a positive clinical effect, the vaccine should probably be administered over a long period of time and before amyloid pathologies manifest in the brain. Different drugs, such as erythropoietin or antiplatelet therapy, improve memory in neuropsychiatric diseases or AD or at least in animal studies. Omega-3 polyunsaturated fatty acid-rich diets improve memory through the gut-brain axis by altering the gut flora through probiotics. Sports, dancing, and memory techniques (e.g., Method of Loci) utilize behavioral approaches for memory enhancement, and were effective in several studies. Augmented reality (AR) is an auspicious way for enhancing memory in real time. Future approaches may include memory prosthesis for head-injured patients and light therapy for restoring memory in AD. Memory enhancement in humans in health and disease holds big promises for the future. Memory training helps only in mild or no impairment. Clinical application requires further investigation.
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Affiliation(s)
- Felicitas Schneider
- grid.8379.50000 0001 1958 8658Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Alan Horowitz
- grid.8379.50000 0001 1958 8658Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Klaus-Peter Lesch
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany ,grid.448878.f0000 0001 2288 8774Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia ,grid.5012.60000 0001 0481 6099Department of Psychiatry and Psychology, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074, Würzburg, Germany. .,EMBL, Computational Biology and Structures Program, 69117, Heidelberg, Germany.
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45
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Pilly PK, Skorheim SW, Hubbard RJ, Ketz NA, Roach SM, Lerner I, Jones AP, Robert B, Bryant NB, Hartholt A, Mullins TS, Choe J, Clark VP, Howard MD. One-Shot Tagging During Wake and Cueing During Sleep With Spatiotemporal Patterns of Transcranial Electrical Stimulation Can Boost Long-Term Metamemory of Individual Episodes in Humans. Front Neurosci 2020; 13:1416. [PMID: 31998067 PMCID: PMC6967741 DOI: 10.3389/fnins.2019.01416] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 12/16/2019] [Indexed: 12/01/2022] Open
Abstract
Targeted memory reactivation (TMR) during slow-wave oscillations (SWOs) in sleep has been demonstrated with sensory cues to achieve about 5-12% improvement in post-nap memory performance on simple laboratory tasks. But prior work has not yet addressed the one-shot aspect of episodic memory acquisition, or dealt with the presence of interference from ambient environmental cues in real-world settings. Further, TMR with sensory cues may not be scalable to the multitude of experiences over one's lifetime. We designed a novel non-invasive non-sensory paradigm that tags one-shot experiences of minute-long naturalistic episodes in immersive virtual reality (VR) with unique spatiotemporal amplitude-modulated patterns (STAMPs) of transcranial electrical stimulation (tES). In particular, we demonstrated that these STAMPs can be re-applied as brief pulses during SWOs in sleep to achieve about 10-20% improvement in the metamemory of targeted episodes compared to the control episodes at 48 hours after initial viewing. We found that STAMPs can not only facilitate but also impair metamemory for the targeted episodes based on an interaction between pre-sleep metamemory and the number of STAMP applications during sleep. Overnight metamemory improvements were mediated by spectral power increases following the offset of STAMPs in the slow-spindle band (8-12 Hz) for left temporal areas in the scalp electroencephalography (EEG) during sleep. These results prescribe an optimal strategy to leverage STAMPs for boosting metamemory and suggest that real-world episodic memories can be modulated in a targeted manner even with coarser, non-invasive spatiotemporal stimulation.
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Affiliation(s)
- Praveen K. Pilly
- Center for Human-Machine Collaboration, Information and Systems Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA, United States
| | - Steven W. Skorheim
- Center for Human-Machine Collaboration, Information and Systems Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA, United States
| | - Ryan J. Hubbard
- Center for Human-Machine Collaboration, Information and Systems Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA, United States
| | - Nicholas A. Ketz
- Center for Human-Machine Collaboration, Information and Systems Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA, United States
| | - Shane M. Roach
- Center for Human-Machine Collaboration, Information and Systems Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA, United States
| | - Itamar Lerner
- Center of Molecular and Behavior Neuroscience, Rutgers University Newark, Newark, NJ, United States
- Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Aaron P. Jones
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Bradley Robert
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Natalie B. Bryant
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Arno Hartholt
- Institute for Creative Technologies, University of Southern California, Los Angeles, CA, United States
| | - Teagan S. Mullins
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Jaehoon Choe
- Center for Human-Machine Collaboration, Information and Systems Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA, United States
| | - Vincent P. Clark
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Michael D. Howard
- Center for Human-Machine Collaboration, Information and Systems Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA, United States
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46
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Machine Learning: From Expert Systems to Deep Learning. Cogn Sci 2019. [DOI: 10.1017/9781108339216.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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47
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The Prehistory of Cognitive Science. Cogn Sci 2019. [DOI: 10.1017/9781108339216.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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48
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Preface. Cogn Sci 2019. [DOI: 10.1017/9781108339216.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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49
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Bibliography. Cogn Sci 2019. [DOI: 10.1017/9781108339216.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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50
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Bayesianism in Cognitive Science. Cogn Sci 2019. [DOI: 10.1017/9781108339216.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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