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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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Kim YH, Koo H, Kim MS, Jung SD. Fabrication of a photo-crosslinkable fluoropolymer-passivated flexible neural probe and acute recording and stimulation performances in vivo. BIOMATERIALS ADVANCES 2023; 154:213629. [PMID: 37742557 DOI: 10.1016/j.bioadv.2023.213629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 08/25/2023] [Accepted: 09/16/2023] [Indexed: 09/26/2023]
Abstract
Herein, we fabricated fluorine-containing, polymer-based, flexible neural probes with fluorinated ethylene propylene (FEP) films as the substrates and photo-crosslinkable fluoropolymers as the passivation material. For fabrication, metal-free Au layer formation on the FEP film, the simultaneous photo-adhesion and photo-patterning technique, and the pulsed-laser scanning probe shaping technique were combined, followed by Au electrode surface modification. The resultant probes achieved a charge injection limit equal to 5.18 mC cm-2 by implementing iridium oxide-modified nanoporous Au (IrOx/NPG) structures. We performed simultaneous in vivo micro-stimulations of the Schaffer collateral fibres and recorded the evoked field excitatory postsynaptic potentials (fEPSPs) in the stratum radiatum layer of the hippocampal Cornu Ammonis 1 region using a single probe. Inducing the fEPSP at very low charge per pulse settings (3.2-3.6 nC/pulse) indicates the efficient charge injection capability of the IrOx/NPG electrode, thereby enabling safe, prolonged, and thrifty micro-stimulations. Furthermore, the single probe-induced and recorded long-term potentiation persisted for periods longer than 60 min following theta-burst stimulation. The materials used in this study are all biocompatible and chemically robust. The fabricated neural probes can be applied in chronic clinical trials in vivo.
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Affiliation(s)
- Yong Hee Kim
- Cybre Brain Research Section, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 305-700, Republic of Korea
| | - Ho Koo
- Department of Neuroscience, Cell Biology, and Anatomy, University of Texas Medical Branch, Galveston, TX, United States
| | - Min Sun Kim
- Department of Physiology, Wonkwang University School of Medicine, 895 Munwang-ro, Iksan 570-711, Jeollabuk-do, Republic of Korea
| | - Sang-Don Jung
- Cybre Brain Research Section, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 305-700, Republic of Korea.
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3
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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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Klein E, Montes Daza N, Dasgupta I, MacDuffie K, Schönau A, Flynn G, Song D, Goering S. Views of stakeholders at risk for dementia about deep brain stimulation for cognition. Brain Stimul 2023; 16:742-747. [PMID: 37076043 PMCID: PMC10576447 DOI: 10.1016/j.brs.2023.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/23/2023] [Accepted: 04/12/2023] [Indexed: 04/21/2023] Open
Affiliation(s)
- Eran Klein
- Department of Neurology, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, L226, Portland, OR, 97239-3098, United States; Department of Philosophy, University of Washington, Savery Hall, Room 361, Box 353350, Seattle, WA, 98195, United States.
| | - Natalia Montes Daza
- Department of Philosophy, University of Washington, Savery Hall, Room 361, Box 353350, Seattle, WA, 98195, United States
| | - Ishan Dasgupta
- The Dana Foundation, 1270 Avenue of the Americas, 12th Floor, New York, NY, 10020, United States
| | - Kate MacDuffie
- Treuman Katz Center for Pediatric Bioethics, Seattle Children's Research Institute, 1900 Ninth Ave. Seattle, WA, 98101, United States; Department of Pediatrics, Division of Bioethics and Palliative Care, University of Washington School of Medicine, PO Box 5371, Seattle, WA, 98105, United States
| | - Andreas Schönau
- Department of Philosophy, University of Washington, Savery Hall, Room 361, Box 353350, Seattle, WA, 98195, United States
| | - Garrett Flynn
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Denney Research Center (DRB) 140, Los Angeles, CA, 90089-1111, United States
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Denney Research Center (DRB) 140, Los Angeles, CA, 90089-1111, United States
| | - Sara Goering
- Department of Philosophy, University of Washington, Savery Hall, Room 361, Box 353350, Seattle, WA, 98195, United States
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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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Roeder BM, Riley MR, She X, Dakos AS, Robinson BS, Moore BJ, Couture DE, Laxton AW, Popli G, Munger Clary HM, Sam M, Heck C, Nune G, Lee B, Liu C, Shaw S, Gong H, Marmarelis VZ, Berger TW, Deadwyler SA, Song D, Hampson RE. Patterned Hippocampal Stimulation Facilitates Memory in Patients With a History of Head Impact and/or Brain Injury. Front Hum Neurosci 2022; 16:933401. [PMID: 35959242 PMCID: PMC9358788 DOI: 10.3389/fnhum.2022.933401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/13/2022] [Indexed: 11/24/2022] Open
Abstract
Rationale: Deep brain stimulation (DBS) of the hippocampus is proposed for enhancement of memory impaired by injury or disease. Many pre-clinical DBS paradigms can be addressed in epilepsy patients undergoing intracranial monitoring for seizure localization, since they already have electrodes implanted in brain areas of interest. Even though epilepsy is usually not a memory disorder targeted by DBS, the studies can nevertheless model other memory-impacting disorders, such as Traumatic Brain Injury (TBI). Methods: Human patients undergoing Phase II invasive monitoring for intractable epilepsy were implanted with depth electrodes capable of recording neurophysiological signals. Subjects performed a delayed-match-to-sample (DMS) memory task while hippocampal ensembles from CA1 and CA3 cell layers were recorded to estimate a multi-input, multi-output (MIMO) model of CA3-to-CA1 neural encoding and a memory decoding model (MDM) to decode memory information from CA3 and CA1 neuronal signals. After model estimation, subjects again performed the DMS task while either MIMO-based or MDM-based patterned stimulation was delivered to CA1 electrode sites during the encoding phase of the DMS trials. Each subject was sorted (post hoc) by prior experience of repeated and/or mild-to-moderate brain injury (RMBI), TBI, or no history (control) and scored for percentage successful delayed recognition (DR) recall on stimulated vs. non-stimulated DMS trials. The subject’s medical history was unknown to the experimenters until after individual subject memory retention results were scored. Results: When examined compared to control subjects, both TBI and RMBI subjects showed increased memory retention in response to both MIMO and MDM-based hippocampal stimulation. Furthermore, effects of stimulation were also greater in subjects who were evaluated as having pre-existing mild-to-moderate memory impairment. Conclusion: These results show that hippocampal stimulation for memory facilitation was more beneficial for subjects who had previously suffered a brain injury (other than epilepsy), compared to control (epilepsy) subjects who had not suffered a brain injury. This study demonstrates that the epilepsy/intracranial recording model can be extended to test the ability of DBS to restore memory function in subjects who previously suffered a brain injury other than epilepsy, and support further investigation into the beneficial effect of DBS in TBI patients.
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Affiliation(s)
- Brent M. Roeder
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Mitchell R. Riley
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Xiwei She
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Alexander S. Dakos
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Brian S. Robinson
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Bryan J. Moore
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Daniel E. Couture
- Department of Neurosurgery, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Adrian W. Laxton
- Department of Neurosurgery, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Gautam Popli
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Heidi M. Munger Clary
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Maria Sam
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Christi Heck
- Department of Neurology, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - George Nune
- Department of Neurology, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Brian Lee
- Department of Neurosurgery, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Charles Liu
- Department of Neurosurgery, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Susan Shaw
- Department of Neurology, Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Hui Gong
- Department of Neurology, Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Vasilis Z. Marmarelis
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Theodore W. Berger
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Sam A. Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Dong Song
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Robert E. Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
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Isagulyan ED, Mikhailova VA, Aslakhanova KS, Slavin KV. Prospects of neuromodulation for chronic pain. BRAIN DISORDERS 2022. [DOI: 10.1016/j.dscb.2021.100027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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8
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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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Li W, Qian C, Qi Y, Wang Y, Wang Y, Pan G. Efficient Point-Process Modeling of Spiking Neurons for Neuroprosthesis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6198-6202. [PMID: 34892531 DOI: 10.1109/embc46164.2021.9630019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuroprosthesis refers to implantable medical devices which can replace injured biological functions in the brain. One of the core problems in neuroprosthesis study is to construct a neural signal transformation model from one cortical area to another. Since the brain encodes and transmits information in spike trains, spiking neural network (SNN) can be an ideal choice for neuroprosthesis modeling. This paper proposes a spiking neuron point-process model (SNPM), which receives spike times as input, and is capable of modeling nonlinear interactions between cortical areas. The proposed SNPM can be implemented on neuromorphic chips for low-energy computing, thus has potential for clinical applications. Experiments show that SNPM can accurately reconstruct functional relationships from PMd (dorsal premotor cortex) to M1 (primary motor cortex) areas.
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Vakilna YS, Tang WC, Wheeler BC, Brewer GJ. The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing. Front Neural Circuits 2021; 15:660837. [PMID: 34512275 PMCID: PMC8430040 DOI: 10.3389/fncir.2021.660837] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 08/03/2021] [Indexed: 11/21/2022] Open
Abstract
The tri-synaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information inputs largely flow from the entorhinal cortex (EC) to the dentate gyrus (DG), and then are processed further in the CA3 and CA1 before returning to the EC. Here, we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed for monitoring the directionality of individual axons between the subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in microfluidic tunnels. The majority of axons from the EC to the DG operated in the feed-forward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s, indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of inter-burst intervals. This suggested the presence of up-states and down-states in every region, with the least up-states in the DG to CA3 feed-forward axons and the CA3 subregion. The peaks of the log-normal distributions of intra-burst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also log-normally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled the generation of structural connectivity graphs, not possible previously without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational artificial intelligence (AI) networks, neuromorphic hardware, and stimulation and decoding from cognitive implants.
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Affiliation(s)
- Yash S Vakilna
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - William C Tang
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Bruce C Wheeler
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
| | - Gregory J Brewer
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.,Center for Neuroscience of Learning and Memory, Memory Impairments and Neurological Disorders (MIND) Institute, University of California, Irvine, Irvine, CA, United States
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De Ridder D, Maciaczyk J, Vanneste S. The future of neuromodulation: smart neuromodulation. Expert Rev Med Devices 2021; 18:307-317. [PMID: 33764840 DOI: 10.1080/17434440.2021.1909470] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: The International Neuromodulation Society defines neuromodulation as the alteration of nerve activity through targeted delivery of a stimulus, such as electrical stimulation or chemical agents, to specific neurological sites in the body.Areas covered: In the near future (<5 years) increasingly complex implantable neuromodulation systems will enter the market. These devices are capable of closed-loop stimulation and the delivery of novel stimulation designs, pushing the need for upgradability. But what about the near-to-far future, meaning 5-10 years from now?Expert opinion: We propose that neuromodulation in the near to far future (5-10 years) will involve integration of adaptive network neuromodulation with predictive artificial intelligence, automatically adjusted by brain and external sensors, and controlled via cloud-based applications. The components will be introduced in a phased approach, culminating in a fully autonomous brain-stimulator-cloud interface. This may, in the long future (>10 years), lead to the brain of the future, a brain with integrated artificial intelligence.
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Affiliation(s)
- Dirk De Ridder
- Department of Surgical Sciences, Section of Neurosurgery, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Jarek Maciaczyk
- Stereotactic and Functional Neurosurgery, Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Sven Vanneste
- Lab for Clinical & Integrative Neuroscience, Trinity College Institute for Neuroscience, Trinity College Dublin, Dublin, Ireland
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12
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He F, Yang Y. Nonlinear System Identification of Neural Systems from Neurophysiological Signals. Neuroscience 2021; 458:213-228. [PMID: 33309967 PMCID: PMC7925423 DOI: 10.1016/j.neuroscience.2020.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 12/20/2022]
Abstract
The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a certain amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.
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Affiliation(s)
- Fei He
- Centre for Data Science, Coventry University, Coventry CV1 2JH, UK
| | - Yuan Yang
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Tulsa, OK 74135, USA; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Laureate Institute for Brain Research, Tulsa, OK 74136, USA
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13
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Yang Y, Ahmadipour P, Shanechi MM. Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization. J Neural Eng 2021; 18. [PMID: 33254159 DOI: 10.1088/1741-2552/abcefd] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 11/30/2020] [Indexed: 12/29/2022]
Abstract
Objective. Dynamic latent state models are widely used to characterize the dynamics of brain network activity for various neural signal types. To date, dynamic latent state models have largely been developed for stationary brain network dynamics. However, brain network dynamics can be non-stationary for example due to learning, plasticity or recording instability. To enable modeling these non-stationarities, two problems need to be resolved. First, novel methods should be developed that can adaptively update the parameters of latent state models, which is difficult due to the state being latent. Second, new methods are needed to optimize the adaptation learning rate, which specifies how fast new neural observations update the model parameters and can significantly influence adaptation accuracy.Approach. We develop a Rate Optimized-adaptive Linear State-Space Modeling (RO-adaptive LSSM) algorithm that solves these two problems. First, to enable adaptation, we derive a computation- and memory-efficient adaptive LSSM fitting algorithm that updates the LSSM parameters recursively and in real time in the presence of the latent state. Second, we develop a real-time learning rate optimization algorithm. We use comprehensive simulations of a broad range of non-stationary brain network dynamics to validate both algorithms, which together constitute the RO-adaptive LSSM.Main results. We show that the adaptive LSSM fitting algorithm can accurately track the broad simulated non-stationary brain network dynamics. We also find that the learning rate significantly affects the LSSM fitting accuracy. Finally, we show that the real-time learning rate optimization algorithm can run in parallel with the adaptive LSSM fitting algorithm. Doing so, the combined RO-adaptive LSSM algorithm rapidly converges to the optimal learning rate and accurately tracks non-stationarities.Significance. These algorithms can be used to study time-varying neural dynamics underlying various brain functions and enhance future neurotechnologies such as brain-machine interfaces and closed-loop brain stimulation systems.
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Affiliation(s)
- Yuxiao Yang
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.,These authors contributed equally to this work
| | - Parima Ahmadipour
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.,These authors contributed equally to this work
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer 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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14
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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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15
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Moore BJ, Berger T, Song D. Validation of a Convolutional Neural Network Model for Spike Transformation Using a Generalized Linear Model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3236-3239. [PMID: 33018694 DOI: 10.1109/embc44109.2020.9176458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Identification of causal relationships of neural activity is one of the most important problems in neuroscience and neural engineering. We show that a novel deep learning approach using a convolutional neural network to model output neural spike activity from input neural spike activity is able to achieve high correlation between the predicted probability of spiking in the output neuron and the true probability of spiking in the output neuron for data generated with a generalized linear model. The convolutional neural network is also able to recover the true model variables (kernels) used to generate the probability of spiking in the output neuron. Based on the convolutional neural network model's validation via a generalized linear model, future work will include validation with non-linear models that use higher-order kernels.
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16
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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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17
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Tan SZK, Neoh J, Lawrence AJ, Wu EX, Lim LW. Prelimbic Cortical Stimulation Improves Spatial Memory Through Distinct Patterns of Hippocampal Gene Expression in Aged Rats. Neurotherapeutics 2020; 17:2054-2068. [PMID: 32816221 PMCID: PMC7851284 DOI: 10.1007/s13311-020-00913-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Dementia poses major health challenges worldwide, yet current treatments are faced with issues of efficacy and toxicity. Deep brain stimulation (DBS) is a promising non-pharmacological treatment for dementia, but most DBS studies use young healthy animals, which may not be aetiologically relevant. In this study, we used an aged rat model in which cognitive decline occurs through a natural ageing process. We used a Morris water maze (MWM) to determine the effects of prelimbic cortex (PrL) DBS on memory in aged rats. To investigate the underlying mechanisms of the effects of DBS, we carried out microarray, quantitative PCR analysis, and mass spectrometry to detect gene expression and neurotransmitter changes in the hippocampus. We showed PrL DBS improved the performance in MWM, with related distinct patterns of gene expression involving G protein-coupled receptor pathways. We further found neurotransmitter changes in the dorsal hippocampus, which corroborated and extended the microarray findings. Our results suggest that non-neurogenesis pathways play roles in the effects of DBS. Further studies are needed to investigate the effects of DBS on memory beyond neurogenesis and to consider the highlighted pathways suggested by our data.
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Affiliation(s)
- Shawn Zheng Kai Tan
- Neuromodulation Laboratory, School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR China
| | - Joveen Neoh
- Neuromodulation Laboratory, School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR China
| | - Andrew John Lawrence
- Behavioural Neuroscience Division, Florey Institute of Neuroscience and Mental Health, Parkville, VIC Australia
| | - Ed Xuekui Wu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR China
| | - Lee Wei Lim
- Neuromodulation Laboratory, School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR China
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18
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Qian C, Sun X, Wang Y, Zheng X, Wang Y, Pan G. Binless Kernel Machine: Modeling Spike Train Transformation for Cognitive Neural Prostheses. Neural Comput 2020; 32:1863-1900. [PMID: 32795229 DOI: 10.1162/neco_a_01306] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Modeling spike train transformation among brain regions helps in designing a cognitive neural prosthesis that restores lost cognitive functions. Various methods analyze the nonlinear dynamic spike train transformation between two cortical areas with low computational eficiency. The application of a real-time neural prosthesis requires computational eficiency, performance stability, and better interpretation of the neural firing patterns that modulate target spike generation. We propose the binless kernel machine in the point-process framework to describe nonlinear dynamic spike train transformations. Our approach embeds the binless kernel to eficiently capture the feedforward dynamics of spike trains and maps the input spike timings into reproducing kernel Hilbert space (RKHS). An inhomogeneous Bernoulli process is designed to combine with a kernel logistic regression that operates on the binless kernel to generate an output spike train as a point process. Weights of the proposed model are estimated by maximizing the log likelihood of output spike trains in RKHS, which allows a global-optimal solution. To reduce computational complexity, we design a streaming-based clustering algorithm to extract typical and important spike train features. The cluster centers and their weights enable the visualization of the important input spike train patterns that motivate or inhibit output neuron firing. We test the proposed model on both synthetic data and real spike train data recorded from the dorsal premotor cortex and the primary motor cortex of a monkey performing a center-out task. Performances are evaluated by discrete-time rescaling Kolmogorov-Smirnov tests. Our model outperforms the existing methods with higher stability regardless of weight initialization and demonstrates higher eficiency in analyzing neural patterns from spike timing with less historical input (50%). Meanwhile, the typical spike train patterns selected according to weights are validated to encode output spike from the spike train of single-input neuron and the interaction of two input neurons.
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Affiliation(s)
- Cunle Qian
- College of Computer Science, Zhejiang University, Hangzhou 310027, P.R.C., and Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR 99077, P.R.C.
| | - Xuyun Sun
- College of Computer Science, Zhejiang University, Hangzhou 310027, P.R.C.
| | - Yueming Wang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, P.R.C.
| | - Xiaoxiang Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, P.R.C.
| | - Yiwen Wang
- Department of Electronic and Computer Engineering and Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR 99077, P.R.C.
| | - Gang Pan
- College of Computer Science and State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310027, P.R.C.
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19
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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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20
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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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21
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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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22
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Elyahoodayan S, Jiang W, Xu H, Song D. A Multi-Channel Asynchronous Neurostimulator With Artifact Suppression for Neural Code-Based Stimulations. Front Neurosci 2019; 13:1011. [PMID: 31611764 PMCID: PMC6776638 DOI: 10.3389/fnins.2019.01011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 09/05/2019] [Indexed: 11/18/2022] Open
Abstract
A novel neurostimulator for generating neural code-based, precise, asynchronous electrical stimulation pulses is designed, fabricated, and characterized. Through multiplexing, this system can deliver constant current biphasic pulses, with arbitrary temporal patterns, and pulse parameters to 32 electrodes using one pulse generator. The design also features a stimulus artifact suppression (SAS) technique that can be integrated with commercial amplifiers. Using an array of CMOS switches, electrodes are disconnected from recording amplifiers during stimulation, while the input of the recording system is shorted to ground through another CMOS switch to suppress ringing in the recording system. The timing of the switches used to block and suppress the stimulus artifact are crucial and are determined by the electrochemical properties of the electrode. This system allows stimulation and recording from the same electrodes to monitor local field potentials with short latencies from the region of stimulation for achieving feedback control of neural stimulation. In this way, timing between each pulse is controlled by inputs from an external source and stimulus magnitude is controlled by feed-back from neural response from the stimulated tissue. The system was implemented with low-power and compact packaged microchips to constitute an effective, cost-efficient, and miniaturized neurostimulator. The device has been first evaluated in phantom preparations and then tested in hippocampi of behaving rats. Benchtop results demonstrate the capability of the stimulator to generate arbitrary spatio-temporal pattern of stimulation pulses dictated by random number generators (RNGs) to control magnitude and timing between each individual biphasic pulse. In vivo results show that evoked potentials elicited by the neurostimulator can be recorded ∼2 ms after the termination of stimulus pulses from the same electrodes where stimulation pulses are delivered, whereas commercial amplifiers without such an artifact suppression typically result in tens to hundreds of milliseconds recovery period. This neurostimulator design is desirable in a variety of neural interface applications, particularly hippocampal memory prosthesis aiming to restore cognitive functions by reinstating neural code transmissions in the brain.
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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
| | - Huijing Xu
- 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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23
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Yang Y, Sani OG, Chang EF, Shanechi MM. Dynamic network modeling and dimensionality reduction for human ECoG activity. J Neural Eng 2019; 16:056014. [DOI: 10.1088/1741-2552/ab2214] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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24
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Abstract
The human body is a remarkable example of the process of evolution which ultimately created a sentient being with cognitive, motor, and information-processing abilities. The body can also be thought of as an amazing feat of engineering, and specifically as an example of molecular nanotechnology, positioning trillions of cells throughout the body, and creating the billions of unique individuals that have existed since the beginning of humanity. On the other hand, from an engineering perspective, there are numerous limitations associated with the human body and the process of evolution to effect changes in the body is exceedingly slow. For example, our skeletal structure is only so strong, our body is subject to disease, and we are programmed by our DNA to age. Further, it took millions of years for Homo sapiens to evolve and hundreds of thousands of years for hominids to invent the most basic technology. To allow humans to go beyond the capabilities that evolution provided Homo sapiens, current research is leading to technologies that could significantly enhance the cognitive and motor abilities of humans and eventually create the conditions in which humans and technology could merge to form a cybernetic being. Much of this technology is being developed from three fronts: due to medical necessity, an interest within the military to create a cyborg soldier, and the desire among some people to self-enhance their body with technology. This article discusses the processes of biological evolution which led to the current anatomical, physiological, and cognitive capabilities of humans and concludes with a discussion of emerging technologies which are directed primarily at enhancing the cognitive functions performed by the brain. This article also discusses a timeframe in which the body will become increasingly equipped with technology directly controlled by the brain, then as a major paradigm shift in human evolution, humans will merge with the technology itself.
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25
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26
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Hampson RE, Song D, Robinson BS, Fetterhoff D, Dakos AS, Roeder BM, She X, Wicks RT, Witcher MR, Couture DE, Laxton AW, Munger-Clary H, Popli G, Sollman MJ, Whitlow CT, Marmarelis VZ, Berger TW, Deadwyler SA. Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall. J Neural Eng 2018; 15:036014. [PMID: 29589592 DOI: 10.1088/1741-2552/aaaed7] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient's own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval. APPROACH We have constructed a model of processes by which the hippocampus encodes memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of short-term memory. A nonlinear multi-input, multi-output (MIMO) model of hippocampal CA3 and CA1 neural firing is computed that predicts activation patterns of CA1 neurons during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. MAIN RESULTS MIMO model-derived electrical stimulation delivered to the same CA1 locations during the sample phase of DMS trials facilitated short-term/working memory by 37% during the task. Longer term memory retention was also tested in the same human subjects with a delayed recognition (DR) task that utilized images from the DMS task, along with images that were not from the task. Across the subjects, the stimulated trials exhibited significant improvement (35%) in both short-term and long-term retention of visual information. SIGNIFICANCE These results demonstrate the facilitation of memory encoding which is an important feature for the construction of an implantable neural prosthetic to improve human memory.
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Affiliation(s)
- Robert E Hampson
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States of America
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27
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Sandler RA, Geng K, Song D, Hampson RE, Witcher MR, Deadwyler SA, Berger TW, Marmarelis VZ. Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression. Neural Comput 2018; 30:1180-1208. [PMID: 29566356 DOI: 10.1162/neco_a_01075] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study, human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern, which successfully abated 92% of seizures. Finally, in a fully responsive, or closed-loop, neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Kunling Geng
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Mark R Witcher
- Department of Neurosurgery, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
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Xu H, Hirschberg AW, Scholten K, Berger TW, Song D, Meng E. Acute in vivo testing of a conformal polymer microelectrode array for multi-region hippocampal recordings. J Neural Eng 2018; 15:016017. [PMID: 29044049 PMCID: PMC5792195 DOI: 10.1088/1741-2552/aa9451] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The success of a cortical prosthetic device relies upon its ability to attain resolvable spikes from many neurons in particular neural networks over long periods of time. Traditionally, lifetimes of neural recordings are greatly limited by the body's immune response against the foreign implant which causes neuronal death and glial scarring. This immune reaction is posited to be exacerbated by micromotion between the implant, which is often rigid, and the surrounding, soft brain tissue, and attenuates the quality of recordings over time. APPROACH In an attempt to minimize the foreign body response to a penetrating neural array that records from multiple brain regions, Parylene C, a flexible, biocompatible polymer was used as the substrate material for a functional, proof-of-concept neural array with a reduced elastic modulus. This probe array was designed and fabricated to have 64 electrodes positioned to match the anatomy of the rat hippocampus and allow for simultaneous recordings between two cell-body layers of interest. A dissolvable brace was used for deep-brain penetration of the flexible array. MAIN RESULTS Arrays were electrochemically characterized at the benchtop, and a novel insertion technique that restricts acute insertion injury enabled accurate target placement of four, bare, flexible arrays to greater than 4 mm deep into the rat brain. Arrays were tested acutely and in vivo recordings taken intra-operatively reveal spikes in both targeted regions of the hippocampus with spike amplitudes and noise levels similar to those recorded with microwires. Histological staining of a sham array implanted for one month reveals limited astrocytic scarring and neuronal death around the implant. SIGNIFICANCE This work represents one of the first examples of a penetrating polymer probe array that records from individual neurons in structures that lie deep within the brain.
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Affiliation(s)
- Huijing Xu
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089-1111, United States of America
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29
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Ramirez-Zamora A, Giordano JJ, Gunduz A, Brown P, Sanchez JC, Foote KD, Almeida L, Starr PA, Bronte-Stewart HM, Hu W, McIntyre C, Goodman W, Kumsa D, Grill WM, Walker HC, Johnson MD, Vitek JL, Greene D, Rizzuto DS, Song D, Berger TW, Hampson RE, Deadwyler SA, Hochberg LR, Schiff ND, Stypulkowski P, Worrell G, Tiruvadi V, Mayberg HS, Jimenez-Shahed J, Nanda P, Sheth SA, Gross RE, Lempka SF, Li L, Deeb W, Okun MS. Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank. Front Neurosci 2018; 11:734. [PMID: 29416498 PMCID: PMC5787550 DOI: 10.3389/fnins.2017.00734] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/15/2017] [Indexed: 12/21/2022] Open
Abstract
The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.
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Affiliation(s)
- Adolfo Ramirez-Zamora
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States,*Correspondence: Adolfo Ramirez-Zamora
| | - James J. Giordano
- Department of Neurology, Pellegrino Center for Clinical Bioethics, Georgetown University Medical Center, Washington, DC, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Peter Brown
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Justin C. Sanchez
- Biological Technologies Office, Defense Advanced Research Projects Agency, Arlington, VA, United States
| | - Kelly D. Foote
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Leonardo Almeida
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Philip A. Starr
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Helen M. Bronte-Stewart
- Departments of Neurology and Neurological Sciences and Neurosurgery, Stanford University, Stanford, CA, United States
| | - Wei Hu
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Cameron McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Wayne Goodman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Doe Kumsa
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, White Oak Federal Research Center, Silver Spring, MD, United States
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Harrison C. Walker
- Division of Movement Disorders, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States,Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - David Greene
- NeuroPace, Inc., Mountain View, CA, United States
| | - Daniel S. Rizzuto
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
| | - Dong Song
- 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
| | - Robert E. Hampson
- Physiology and Pharmacology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, NC, United States
| | - Sam A. Deadwyler
- Physiology and Pharmacology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, NC, United States
| | - Leigh R. Hochberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States,Center for Neurorestoration and Neurotechnology, Rehabilitation R and D Service, Veterans Affairs Medical Center, Providence, RI, United States,School of Engineering and Brown Institute for Brain Science, Brown University, Providence, RI, United States
| | - Nicholas D. Schiff
- Laboratory of Cognitive Neuromodulation, Feil Family Brain Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Vineet Tiruvadi
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Helen S. Mayberg
- Departments of Psychiatry, Neurology, and Radiology, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Joohi Jimenez-Shahed
- Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Pranav Nanda
- Department of Neurological Surgery, The Neurological Institute, Columbia University Herbert and Florence Irving Medical Center, Colombia University, New York, NY, United States
| | - Sameer A. Sheth
- Department of Neurological Surgery, The Neurological Institute, Columbia University Herbert and Florence Irving Medical Center, Colombia University, New York, NY, United States
| | - Robert E. Gross
- Department of Neurosurgery, Emory University, Atlanta, GA, United States
| | - Scott F. Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China,Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Beijing, China,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Wissam Deeb
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
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Hampson RE, Deadwyler SA, Berger TW. Multi-resolution multi-trial sparse classification model for decoding visual memories from hippocampal spikes in human. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:1046-1049. [PMID: 29060053 DOI: 10.1109/embc.2017.8037006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
To understand how memories are encoded in the hippocampus, we build memory decoding models to classify visual memories based on hippocampal activities in human. Model inputs are spatio-temporal patterns of spikes recorded in the hippocampal CA3 and CA1 regions of epilepsy patients performing a delayed match-to-sample (DMS) task. Model outputs are binary labels indicating categories and features of sample images. To solve the super high-dimensional estimation problem with short data length, we develop a multi-trial, sparse model estimation method utilizing B-spline basis functions with a large range of temporal resolutions and a regularized logistic classifier. Results show that this model can effectively avoid overfitting and provide significant amount of prediction to memory categories and features using very limited number of data points. Stable estimation of sparse classification function matrices for each label can be obtained with this multi-resolution, multi-trial procedure. These classification models can be used not only to predict memory contents, but also to design optimal spatio-temporal patterns for eliciting specific memories in the hippocampus, and thus have important implications to the development of hippocampal memory prostheses.
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31
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Geng K, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Mechanism-Based and Input-Output Modeling of the Key Neuronal Connections and Signal Transformations in the CA3-CA1 Regions of the Hippocampus. Neural Comput 2017; 30:149-183. [PMID: 29064783 DOI: 10.1162/neco_a_01031] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
This letter examines the results of input-output (nonparametric) modeling based on the analysis of data generated by a mechanism-based (parametric) model of CA3-CA1 neuronal connections in the hippocampus. The motivation is to obtain biological insight into the interpretation of such input-output (Volterra-equivalent) models estimated from synthetic data. The insights obtained may be subsequently used to interpretat input-output models extracted from actual experimental data. Specifically, we found that a simplified parametric model may serve as a useful tool to study the signal transformations in the hippocampal CA3-CA1 regions. Input-output modeling of model-based synthetic data show that GABAergic interneurons are responsible for regulating neuronal excitation, controlling the precision of spike timing, and maintaining network oscillations, in a manner consistent with previous studies. The input-output model obtained from real data exhibits intriguing similarities with its synthetic-data counterpart, demonstrating the importance of a dynamic resonance in the system/model response around 2 Hz to 3 Hz. Using the input-output model from real data as a guide, we may be able to amend the parametric model by incorporating more mechanisms in order to yield better-matching input-output model. The approach we present can also be applied to the study of other neural systems and pathways.
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Affiliation(s)
- Kunling Geng
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dae C Shin
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Samuel A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
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